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Sample records for geostatistics

  1. A geostatistical analysis of geostatistics

    NARCIS (Netherlands)

    Hengl, T.; Minasny, B.; Gould, M.

    2009-01-01

    The bibliometric indices of the scientific field of geostatistics were analyzed using statistical and spatial data analysis. The publications and their citation statistics were obtained from the Web of Science (4000 most relevant), Scopus (2000 most relevant) and Google Scholar (5389). The focus was

  2. Model-based geostatistics

    CERN Document Server

    Diggle, Peter J

    2007-01-01

    Model-based geostatistics refers to the application of general statistical principles of modeling and inference to geostatistical problems. This volume provides a treatment of model-based geostatistics and emphasizes on statistical methods and applications. It also features analyses of datasets from a range of scientific contexts.

  3. Bayesian Geostatistical Design

    DEFF Research Database (Denmark)

    Diggle, Peter; Lophaven, Søren Nymand

    2006-01-01

    This paper describes the use of model-based geostatistics for choosing the set of sampling locations, collectively called the design, to be used in a geostatistical analysis. Two types of design situation are considered. These are retrospective design, which concerns the addition of sampling...

  4. Geostatistical Methods in R

    Directory of Open Access Journals (Sweden)

    Adéla Volfová

    2012-10-01

    Full Text Available Geostatistics is a scientific field which provides methods for processing spatial data.  In our project, geostatistics is used as a tool for describing spatial continuity and making predictions of some natural phenomena. An open source statistical project called R is used for all calculations. Listeners will be provided with a brief introduction to R and its geostatistical packages and basic principles of kriging and cokriging methods. Heavy mathematical background is omitted due to its complexity. In the second part of the presentation, several examples are shown of how to make a prediction in the whole area of interest where observations were made in just a few points. Results of these methods are compared.

  5. Bayesian Geostatistical Design

    DEFF Research Database (Denmark)

    Diggle, Peter; Lophaven, Søren Nymand

    2006-01-01

    This paper describes the use of model-based geostatistics for choosing the set of sampling locations, collectively called the design, to be used in a geostatistical analysis. Two types of design situation are considered. These are retrospective design, which concerns the addition of sampling...... locations to, or deletion of locations from, an existing design, and prospective design, which consists of choosing positions for a new set of sampling locations. We propose a Bayesian design criterion which focuses on the goal of efficient spatial prediction whilst allowing for the fact that model...... parameter values are unknown. The results show that in this situation a wide range of interpoint distances should be included in the design, and the widely used regular design is often not the best choice....

  6. 10th International Geostatistics Congress

    CERN Document Server

    Rodrigo-Ilarri, Javier; Rodrigo-Clavero, María; Cassiraga, Eduardo; Vargas-Guzmán, José

    2017-01-01

    This book contains selected contributions presented at the 10th International Geostatistics Congress held in Valencia from 5 to 9 September, 2016. This is a quadrennial congress that serves as the meeting point for any engineer, professional, practitioner or scientist working in geostatistics. The book contains carefully reviewed papers on geostatistical theory and applications in fields such as mining engineering, petroleum engineering, environmental science, hydrology, ecology, and other fields.

  7. 7th International Geostatistics Congress

    CERN Document Server

    Deutsch, Clayton

    2005-01-01

    The conference proceedings consist of approximately 120 technical papers presented at the Seventh International Geostatistics Congress held in Banff, Alberta, Canada in 2004. All the papers were reviewed by an international panel of leading geostatisticians. The five major sections are: theory, mining, petroleum, environmental and other applications. The first section showcases new and innovative ideas in the theoretical development of geostatistics as a whole; these ideas will have large impact on (1) the directions of future geostatistical research, and (2) the conventional approaches to heterogeneity modelling in a wide range of natural resource industries. The next four sections are focused on applications and innovations relating to the use of geostatistics in specific industries. Historically, mining, petroleum and environmental industries have embraced the use of geostatistics for uncertainty characterization, so these three industries are identified as major application areas. The last section is open...

  8. 4th International Geostatistics Congress

    CERN Document Server

    1993-01-01

    The contributions in this book were presented at the Fourth International Geostatistics Congress held in Tróia, Portugal, in September 1992. They provide a comprehensive account of the current state of the art of geostatistics, including recent theoretical developments and new applications. In particular, readers will find descriptions and applications of the more recent methods of stochastic simulation together with data integration techniques applied to the modelling of hydrocabon reservoirs. In other fields there are stationary and non-stationary geostatistical applications to geology, climatology, pollution control, soil science, hydrology and human sciences. The papers also provide an insight into new trends in geostatistics particularly the increasing interaction with many other scientific disciplines. This book is a significant reference work for practitioners of geostatistics both in academia and industry.

  9. A practical primer on geostatistics

    Science.gov (United States)

    Olea, Ricardo A.

    2009-01-01

    The Challenge—Most geological phenomena are extraordinarily complex in their interrelationships and vast in their geographical extension. Ordinarily, engineers and geoscientists are faced with corporate or scientific requirements to properly prepare geological models with measurements involving a small fraction of the entire area or volume of interest. Exact description of a system such as an oil reservoir is neither feasible nor economically possible. The results are necessarily uncertain. Note that the uncertainty is not an intrinsic property of the systems; it is the result of incomplete knowledge by the observer.The Aim of Geostatistics—The main objective of geostatistics is the characterization of spatial systems that are incompletely known, systems that are common in geology. A key difference from classical statistics is that geostatistics uses the sampling location of every measurement. Unless the measurements show spatial correlation, the application of geostatistics is pointless. Ordinarily the need for additional knowledge goes beyond a few points, which explains the display of results graphically as fishnet plots, block diagrams, and maps.Geostatistical Methods—Geostatistics is a collection of numerical techniques for the characterization of spatial attributes using primarily two tools: probabilistic models, which are used for spatial data in a manner similar to the way in which time-series analysis characterizes temporal data, or pattern recognition techniques. The probabilistic models are used as a way to handle uncertainty in results away from sampling locations, making a radical departure from alternative approaches like inverse distance estimation methods.Differences with Time Series—On dealing with time-series analysis, users frequently concentrate their attention on extrapolations for making forecasts. Although users of geostatistics may be interested in extrapolation, the methods work at their best interpolating. This simple difference

  10. A Practical Primer on Geostatistics

    Science.gov (United States)

    Olea, Ricardo A.

    2009-01-01

    THE CHALLENGE Most geological phenomena are extraordinarily complex in their interrelationships and vast in their geographical extension. Ordinarily, engineers and geoscientists are faced with corporate or scientific requirements to properly prepare geological models with measurements involving a small fraction of the entire area or volume of interest. Exact description of a system such as an oil reservoir is neither feasible nor economically possible. The results are necessarily uncertain. Note that the uncertainty is not an intrinsic property of the systems; it is the result of incomplete knowledge by the observer. THE AIM OF GEOSTATISTICS The main objective of geostatistics is the characterization of spatial systems that are incompletely known, systems that are common in geology. A key difference from classical statistics is that geostatistics uses the sampling location of every measurement. Unless the measurements show spatial correlation, the application of geostatistics is pointless. Ordinarily the need for additional knowledge goes beyond a few points, which explains the display of results graphically as fishnet plots, block diagrams, and maps. GEOSTATISTICAL METHODS Geostatistics is a collection of numerical techniques for the characterization of spatial attributes using primarily two tools: probabilistic models, which are used for spatial data in a manner similar to the way in which time-series analysis characterizes temporal data, or pattern recognition techniques. The probabilistic models are used as a way to handle uncertainty in results away from sampling locations, making a radical departure from alternative approaches like inverse distance estimation methods. DIFFERENCES WITH TIME SERIES On dealing with time-series analysis, users frequently concentrate their attention on extrapolations for making forecasts. Although users of geostatistics may be interested in extrapolation, the methods work at their best interpolating. This simple difference has

  11. Model Selection for Geostatistical Models

    Energy Technology Data Exchange (ETDEWEB)

    Hoeting, Jennifer A.; Davis, Richard A.; Merton, Andrew A.; Thompson, Sandra E.

    2006-02-01

    We consider the problem of model selection for geospatial data. Spatial correlation is typically ignored in the selection of explanatory variables and this can influence model selection results. For example, the inclusion or exclusion of particular explanatory variables may not be apparent when spatial correlation is ignored. To address this problem, we consider the Akaike Information Criterion (AIC) as applied to a geostatistical model. We offer a heuristic derivation of the AIC in this context and provide simulation results that show that using AIC for a geostatistical model is superior to the often used approach of ignoring spatial correlation in the selection of explanatory variables. These ideas are further demonstrated via a model for lizard abundance. We also employ the principle of minimum description length (MDL) to variable selection for the geostatistical model. The effect of sampling design on the selection of explanatory covariates is also explored.

  12. Application of geostatistics to risk assessment.

    Science.gov (United States)

    Thayer, William C; Griffith, Daniel A; Goodrum, Philip E; Diamond, Gary L; Hassett, James M

    2003-10-01

    Geostatistics offers two fundamental contributions to environmental contaminant exposure assessment: (1) a group of methods to quantitatively describe the spatial distribution of a pollutant and (2) the ability to improve estimates of the exposure point concentration by exploiting the geospatial information present in the data. The second contribution is particularly valuable when exposure estimates must be derived from small data sets, which is often the case in environmental risk assessment. This article addresses two topics related to the use of geostatistics in human and ecological risk assessments performed at hazardous waste sites: (1) the importance of assessing model assumptions when using geostatistics and (2) the use of geostatistics to improve estimates of the exposure point concentration (EPC) in the limited data scenario. The latter topic is approached here by comparing design-based estimators that are familiar to environmental risk assessors (e.g., Land's method) with geostatistics, a model-based estimator. In this report, we summarize the basics of spatial weighting of sample data, kriging, and geostatistical simulation. We then explore the two topics identified above in a case study, using soil lead concentration data from a Superfund site (a skeet and trap range). We also describe several areas where research is needed to advance the use of geostatistics in environmental risk assessment.

  13. Geostatistics and spatial analysis in biological anthropology.

    Science.gov (United States)

    Relethford, John H

    2008-05-01

    A variety of methods have been used to make evolutionary inferences based on the spatial distribution of biological data, including reconstructing population history and detection of the geographic pattern of natural selection. This article provides an examination of geostatistical analysis, a method used widely in geology but which has not often been applied in biological anthropology. Geostatistical analysis begins with the examination of a variogram, a plot showing the relationship between a biological distance measure and the geographic distance between data points and which provides information on the extent and pattern of spatial correlation. The results of variogram analysis are used for interpolating values of unknown data points in order to construct a contour map, a process known as kriging. The methods of geostatistical analysis and discussion of potential problems are applied to a large data set of anthropometric measures for 197 populations in Ireland. The geostatistical analysis reveals two major sources of spatial variation. One pattern, seen for overall body and craniofacial size, shows an east-west cline most likely reflecting the combined effects of past population dispersal and settlement. The second pattern is seen for craniofacial height and shows an isolation by distance pattern reflecting rapid spatial changes in the midlands region of Ireland, perhaps attributable to the genetic impact of the Vikings. The correspondence of these results with other analyses of these data and the additional insights generated from variogram analysis and kriging illustrate the potential utility of geostatistical analysis in biological anthropology.

  14. GEOSTATISTICS FOR WASTE MANAGEMENT: A USER'S MANUAL FOR THE GEOPACK (VERSION 1.0) GEOSTATISTICAL SOFTWARE SYSTEM

    Science.gov (United States)

    GEOPACK, a comprehensive user-friendly geostatistical software system, was developed to help in the analysis of spatially correlated data. The software system was developed to be used by scientists, engineers, regulators, etc., with little experience in geostatistical techniques...

  15. Geostatistical methods applied to field model residuals

    DEFF Research Database (Denmark)

    Maule, Fox; Mosegaard, K.; Olsen, Nils

    consists of measurement errors and unmodelled signal), and is typically assumed to be uncorrelated and Gaussian distributed. We have applied geostatistical methods to analyse the residuals of the Oersted(09d/04) field model [http://www.dsri.dk/Oersted/Field_models/IGRF_2005_candidates/], which is based...

  16. Satellite Magnetic Residuals Investigated With Geostatistical Methods

    DEFF Research Database (Denmark)

    Fox Maule, Chaterine; Mosegaard, Klaus; Olsen, Nils

    2005-01-01

    (which consists of measurement errors and unmodeled signal), and is typically assumed to be uncorrelated and Gaussian distributed. We have applied geostatistical methods to analyze the residuals of the Oersted (09d/04) field model (www.dsri.dk/Oersted/Field models/IGRF 2005 candidates/), which is based...

  17. Geostatistical modeling of topography using auxiliary maps

    NARCIS (Netherlands)

    Hengl, T.; Bajat, B.; Blagojević, D.; Reuter, H.I.

    2008-01-01

    This paper recommends computational procedures for employing auxiliary maps, such as maps of drainage patterns, land cover and remote-sensing-based indices, directly in the geostatistical modeling of topography. The methodology is based on the regression-kriging technique, as implemented in the R pa

  18. Mixed-point geostatistical simulation: A combination of two- and multiple-point geostatistics

    Science.gov (United States)

    Cordua, Knud Skou; Hansen, Thomas Mejer; Gulbrandsen, Mats Lundh; Barnes, Christophe; Mosegaard, Klaus

    2016-09-01

    Multiple-point-based geostatistical methods are used to model complex geological structures. However, a training image containing the characteristic patterns of the Earth model has to be provided. If no training image is available, two-point (i.e., covariance-based) geostatistical methods are typically applied instead because these methods provide fewer constraints on the Earth model. This study is motivated by the case where 1-D vertical training images are available through borehole logs, whereas little or no information about horizontal dependencies exists. This problem is solved by developing theory that makes it possible to combine information from multiple- and two-point geostatistics for different directions, leading to a mixed-point geostatistical model. An example of combining information from the multiple-point-based single normal equation simulation algorithm and two-point-based sequential indicator simulation algorithm is provided. The mixed-point geostatistical model is used for conditional sequential simulation based on vertical training images from five borehole logs and a range parameter describing the horizontal dependencies.

  19. Applications of geostatistics in plant nematology.

    Science.gov (United States)

    Wallace, M K; Hawkins, D M

    1994-12-01

    The application of geostatistics to plant nematology was made by evaluating soil and nematode data acquired from 200 soil samples collected from the A(p) horizon of a reed canary-grass field in northern Minnesota. Geostatistical concepts relevant to nematology include semi-variogram modelling, kriging, and change of support calculations. Soil and nematode data generally followed a spherical semi-variogram model, with little random variability associated with soil data and large inherent variability for nematode data. Block kriging of soil and nematode data provided useful contour maps of the data. Change of snpport calculations indicated that most of the random variation in nematode data was due to short-range spatial variability in the nematode population densities.

  20. Geostatistics and Analysis of Spatial Data

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    2007-01-01

    This note deals with geostatistical measures for spatial correlation, namely the auto-covariance function and the semi-variogram, as well as deterministic and geostatistical methods for spatial interpolation, namely inverse distance weighting and kriging. Some semi-variogram models are mentioned......, specifically the spherical, the exponential and the Gaussian models. Equations to carry out simple og ordinary kriging are deduced. Other types of kriging are mentioned, and references to international literature, Internet addresses and state-of-the-art software in the field are given. A very simple example...... to illustrate the computations and a more realistic example with height data from an area near Slagelse, Denmark, are given. Finally, a series of attractive characteristics of kriging are mentioned, and a simple sampling strategic consideration is given based on the dependence of the kriging variance...

  1. The application of geostatistics in erosion hazard mapping

    NARCIS (Netherlands)

    Beurden, S.A.H.A. van; Riezebos, H.Th.

    1988-01-01

    Geostatistical interpolation or kriging of soil and vegetation variables has become an important alternative to other mapping techniques. Although a reconnaissance sampling is necessary and basic requirements of geostatistics have to be met, kriging has the advantage of giving estimates with a minim

  2. Reducing complexity of inverse problems using geostatistical priors

    DEFF Research Database (Denmark)

    Hansen, Thomas Mejer; Mosegaard, Klaus; Cordua, Knud Skou

    a posterior sample, can be reduced significantly using informed priors based on geostatistical models. We discuss two approaches to include such geostatistically based prior information. One is based on a parametric description of the prior likelihood that applies to 2-point based statistical models...

  3. GEOSTATISTICS FOR WASTE MANAGEMENT: A USER'S MANUEL FOR THE GEOPACK (VERSION 1.0) GEOSTATISTICAL SOFTWARE SYSTEM

    Science.gov (United States)

    A comprehensive, user-friendly geostatistical software system called GEOPACk has been developed. The purpose of this software is to make available the programs necessary to undertake a geostatistical analysis of spatially correlated data. The programs were written so that they ...

  4. Geostatistical enhancement of european hydrological predictions

    Science.gov (United States)

    Pugliese, Alessio; Castellarin, Attilio; Parajka, Juraj; Arheimer, Berit; Bagli, Stefano; Mazzoli, Paolo; Montanari, Alberto; Blöschl, Günter

    2016-04-01

    Geostatistical Enhancement of European Hydrological Prediction (GEEHP) is a research experiment developed within the EU funded SWITCH-ON project, which proposes to conduct comparative experiments in a virtual laboratory in order to share water-related information and tackle changes in the hydrosphere for operational needs (http://www.water-switch-on.eu). The main objective of GEEHP deals with the prediction of streamflow indices and signatures in ungauged basins at different spatial scales. In particular, among several possible hydrological signatures we focus in our experiment on the prediction of flow-duration curves (FDCs) along the stream-network, which has attracted an increasing scientific attention in the last decades due to the large number of practical and technical applications of the curves (e.g. hydropower potential estimation, riverine habitat suitability and ecological assessments, etc.). We apply a geostatistical procedure based on Top-kriging, which has been recently shown to be particularly reliable and easy-to-use regionalization approach, employing two different type of streamflow data: pan-European E-HYPE simulations (http://hypeweb.smhi.se/europehype) and observed daily streamflow series collected in two pilot study regions, i.e. Tyrol (merging data from Austrian and Italian stream gauging networks) and Sweden. The merger of the two study regions results in a rather large area (~450000 km2) and might be considered as a proxy for a pan-European application of the approach. In a first phase, we implement a bidirectional validation, i.e. E-HYPE catchments are set as training sites to predict FDCs at the same sites where observed data are available, and vice-versa. Such a validation procedure reveals (1) the usability of the proposed approach for predicting the FDCs over the entire river network of interest using alternatively observed data and E-HYPE simulations and (2) the accuracy of E-HYPE-based predictions of FDCs in ungauged sites. In a

  5. Geostatistics, remote sensing and precision farming.

    Science.gov (United States)

    Mulla, D J

    1997-01-01

    Precision farming is possible today because of advances in farming technology, procedures for mapping and interpolating spatial patterns, and geographic information systems for overlaying and interpreting several soil, landscape and crop attributes. The key component of precision farming is the map showing spatial patterns in field characteristics. Obtaining information for this map is often achieved by soil sampling. This approach, however, can be cost-prohibitive for grain crops. Soil sampling strategies can be simplified by use of auxiliary data provided by satellite or aerial photo imagery. This paper describes geostatistical methods for estimating spatial patterns in soil organic matter, soil test phosphorus and wheat grain yield from a combination of Thematic Mapper imaging and soil sampling.

  6. Bayesian modelling of geostatistical malaria risk data

    Directory of Open Access Journals (Sweden)

    L. Gosoniu

    2006-11-01

    Full Text Available Bayesian geostatistical models applied to malaria risk data quantify the environment-disease relations, identify significant environmental predictors of malaria transmission and provide model-based predictions of malaria risk together with their precision. These models are often based on the stationarity assumption which implies that spatial correlation is a function of distance between locations and independent of location. We relax this assumption and analyse malaria survey data in Mali using a Bayesian non-stationary model. Model fit and predictions are based on Markov chain Monte Carlo simulation methods. Model validation compares the predictive ability of the non-stationary model with the stationary analogue. Results indicate that the stationarity assumption is important because it influences the significance of environmental factors and the corresponding malaria risk maps.

  7. Bayesian modelling of geostatistical malaria risk data.

    Science.gov (United States)

    Gosoniu, L; Vounatsou, P; Sogoba, N; Smith, T

    2006-11-01

    Bayesian geostatistical models applied to malaria risk data quantify the environment-disease relations, identify significant environmental predictors of malaria transmission and provide model-based predictions of malaria risk together with their precision. These models are often based on the stationarity assumption which implies that spatial correlation is a function of distance between locations and independent of location. We relax this assumption and analyse malaria survey data in Mali using a Bayesian non-stationary model. Model fit and predictions are based on Markov chain Monte Carlo simulation methods. Model validation compares the predictive ability of the non-stationary model with the stationary analogue. Results indicate that the stationarity assumption is important because it influences the significance of environmental factors and the corresponding malaria risk maps.

  8. Geostatistical Estimations of Regional Hydraulic Conductivity Fields

    Science.gov (United States)

    Patriarche, D.; Castro, M. C.; Goovaerts, P.

    2004-12-01

    Direct and indirect measurements of hydraulic conductivity (K) are commonly performed, providing information on the magnitude of this parameter at the local scale (tens of centimeters to hundreds of meters) and at shallow depths. By contrast, field information on hydraulic conductivities at regional scales of tens to hundreds of kilometers and at greater depths is relatively scarce. Geostatistical methods allow for sparsely sampled observations of a variable (primary information) to be complemented by a more densely sampled secondary attribute. Geostatistical estimations of the hydraulic conductivity field in the Carrizo aquifer, a major groundwater flow system extending along Texas, are performed using available primary (e.g., transmissivity, hydraulic conductivity) and secondary (specific capacity) information, for depths up to 2.2 km, and over three regional domains of increasing extent: 1) the domain corresponding to a three-dimensional groundwater flow model previously built (model domain); 2) the area corresponding to the ten counties encompassing the model domain (County domain), and; 3) the full extension of the Carrizo aquifer within Texas (Texas domain). Two different approaches are used: 1) an indirect approach are transmissivity (T) is estimated first and (K) is retrieved through division of the T estimate by the screening length of the wells, and; 2) a direct approach where K data are kriged directly. Prediction performances of the tested geostatistical procedures (kriging combined with linear regression, kriging with known local means, kriging of residuals, and cokriging) are evaluated through cross validation for both log-transformed variables and back-transformed ones. For the indirect approach, kriging of log T residuals yields the best estimates for both log-transformed and back-transformed variables in the model domain. For larger regional scales (County and Texas domains), cokriging performs generally better than univariate kriging procedures

  9. Optimizing Groundwater Monitoring Networks Using Integrated Statistical and Geostatistical Approaches

    Directory of Open Access Journals (Sweden)

    Jay Krishna Thakur

    2015-08-01

    Full Text Available The aim of this work is to investigate new approaches using methods based on statistics and geo-statistics for spatio-temporal optimization of groundwater monitoring networks. The formulated and integrated methods were tested with the groundwater quality data set of Bitterfeld/Wolfen, Germany. Spatially, the monitoring network was optimized using geo-statistical methods. Temporal optimization of the monitoring network was carried out using Sen’s method (1968. For geostatistical network optimization, a geostatistical spatio-temporal algorithm was used to identify redundant wells in 2- and 2.5-D Quaternary and Tertiary aquifers. Influences of interpolation block width, dimension, contaminant association, groundwater flow direction and aquifer homogeneity on statistical and geostatistical methods for monitoring network optimization were analysed. The integrated approach shows 37% and 28% redundancies in the monitoring network in Quaternary aquifer and Tertiary aquifer respectively. The geostatistical method also recommends 41 and 22 new monitoring wells in the Quaternary and Tertiary aquifers respectively. In temporal optimization, an overall optimized sampling interval was recommended in terms of lower quartile (238 days, median quartile (317 days and upper quartile (401 days in the research area of Bitterfeld/Wolfen. Demonstrated methods for improving groundwater monitoring network can be used in real monitoring network optimization with due consideration given to influencing factors.

  10. The role of geostatistics in medical geology

    Science.gov (United States)

    Goovaerts, Pierre

    2014-05-01

    Since its development in the mining industry, geostatistics has emerged as the primary tool for spatial data analysis in various fields, ranging from earth and atmospheric sciences, to agriculture, soil science, remote sensing, and more recently environmental exposure assessment. In the last few years, these tools have been tailored to the field of medical geography or spatial epidemiology, which is concerned with the study of spatial patterns of disease incidence and mortality and the identification of potential 'causes' of disease, such as environmental exposure, diet and unhealthy behaviors, economic or socio-demographic factors. On the other hand, medical geology is an emerging interdisciplinary scientific field studying the relationship between natural geological factors and their effects on human and animal health. This paper provides an introduction to the field of medical geology with an overview of geostatistical methods available for the analysis of geological and health data. Key concepts are illustrated using the mapping of groundwater arsenic concentrations across eleven Michigan counties and the exploration of its relationship to the incidence of prostate cancer at the township level. Arsenic in drinking-water is a major problem and has received much attention because of the large human population exposed and the extremely high concentrations (e.g. 600 to 700 μg/L) recorded in many instances. Few studies have however assessed the risks associated with exposure to low levels of arsenic (say < 50 μg/L) most commonly found in drinking water in the United States. In the Michigan thumb region, arsenopyrite (up to 7% As by weight) has been identified in the bedrock of the Marshall Sandstone aquifer, one of the region's most productive aquifers. Epidemiologic studies have suggested a possible associationbetween exposure to inorganic arsenic and prostate cancer mortality, including a study of populations residing in Utah. The information available for the

  11. On the geostatistical characterization of hierarchical media

    Science.gov (United States)

    Neuman, Shlomo P.; Riva, Monica; Guadagnini, Alberto

    2008-02-01

    The subsurface consists of porous and fractured materials exhibiting a hierarchical geologic structure, which gives rise to systematic and random spatial and directional variations in hydraulic and transport properties on a multiplicity of scales. Traditional geostatistical moment analysis allows one to infer the spatial covariance structure of such hierarchical, multiscale geologic materials on the basis of numerous measurements on a given support scale across a domain or "window" of a given length scale. The resultant sample variogram often appears to fit a stationary variogram model with constant variance (sill) and integral (spatial correlation) scale. In fact, some authors, who recognize that hierarchical sedimentary architecture and associated log hydraulic conductivity fields tend to be nonstationary, nevertheless associate them with stationary "exponential-like" transition probabilities and variograms, respectively, the latter being a consequence of the former. We propose that (1) the apparent ability of stationary spatial statistics to characterize the covariance structure of nonstationary hierarchical media is an artifact stemming from the finite size of the windows within which geologic and hydrologic variables are ubiquitously sampled, and (2) the artifact is eliminated upon characterizing the covariance structure of such media with the aid of truncated power variograms, which represent stationary random fields obtained upon sampling a nonstationary fractal over finite windows. To support our opinion, we note that truncated power variograms arise formally when a hierarchical medium is sampled jointly across all geologic categories and scales within a window; cite direct evidence that geostatistical parameters (variance and integral scale) inferred on the basis of traditional variograms vary systematically with support and window scales; demonstrate the ability of truncated power models to capture these variations in terms of a few scaling parameters

  12. Robust geostatistical analysis of spatial data

    Science.gov (United States)

    Papritz, Andreas; Künsch, Hans Rudolf; Schwierz, Cornelia; Stahel, Werner A.

    2013-04-01

    Most of the geostatistical software tools rely on non-robust algorithms. This is unfortunate, because outlying observations are rather the rule than the exception, in particular in environmental data sets. Outliers affect the modelling of the large-scale spatial trend, the estimation of the spatial dependence of the residual variation and the predictions by kriging. Identifying outliers manually is cumbersome and requires expertise because one needs parameter estimates to decide which observation is a potential outlier. Moreover, inference after the rejection of some observations is problematic. A better approach is to use robust algorithms that prevent automatically that outlying observations have undue influence. Former studies on robust geostatistics focused on robust estimation of the sample variogram and ordinary kriging without external drift. Furthermore, Richardson and Welsh (1995) proposed a robustified version of (restricted) maximum likelihood ([RE]ML) estimation for the variance components of a linear mixed model, which was later used by Marchant and Lark (2007) for robust REML estimation of the variogram. We propose here a novel method for robust REML estimation of the variogram of a Gaussian random field that is possibly contaminated by independent errors from a long-tailed distribution. It is based on robustification of estimating equations for the Gaussian REML estimation (Welsh and Richardson, 1997). Besides robust estimates of the parameters of the external drift and of the variogram, the method also provides standard errors for the estimated parameters, robustified kriging predictions at both sampled and non-sampled locations and kriging variances. Apart from presenting our modelling framework, we shall present selected simulation results by which we explored the properties of the new method. This will be complemented by an analysis a data set on heavy metal contamination of the soil in the vicinity of a metal smelter. Marchant, B.P. and Lark, R

  13. High Performance Geostatistical Modeling of Biospheric Resources

    Science.gov (United States)

    Pedelty, J. A.; Morisette, J. T.; Smith, J. A.; Schnase, J. L.; Crosier, C. S.; Stohlgren, T. J.

    2004-12-01

    We are using parallel geostatistical codes to study spatial relationships among biospheric resources in several study areas. For example, spatial statistical models based on large- and small-scale variability have been used to predict species richness of both native and exotic plants (hot spots of diversity) and patterns of exotic plant invasion. However, broader use of geostastics in natural resource modeling, especially at regional and national scales, has been limited due to the large computing requirements of these applications. To address this problem, we implemented parallel versions of the kriging spatial interpolation algorithm. The first uses the Message Passing Interface (MPI) in a master/slave paradigm on an open source Linux Beowulf cluster, while the second is implemented with the new proprietary Xgrid distributed processing system on an Xserve G5 cluster from Apple Computer, Inc. These techniques are proving effective and provide the basis for a national decision support capability for invasive species management that is being jointly developed by NASA and the US Geological Survey.

  14. Preferential sampling and Bayesian geostatistics: Statistical modeling and examples.

    Science.gov (United States)

    Cecconi, Lorenzo; Grisotto, Laura; Catelan, Dolores; Lagazio, Corrado; Berrocal, Veronica; Biggeri, Annibale

    2016-08-01

    Preferential sampling refers to any situation in which the spatial process and the sampling locations are not stochastically independent. In this paper, we present two examples of geostatistical analysis in which the usual assumption of stochastic independence between the point process and the measurement process is violated. To account for preferential sampling, we specify a flexible and general Bayesian geostatistical model that includes a shared spatial random component. We apply the proposed model to two different case studies that allow us to highlight three different modeling and inferential aspects of geostatistical modeling under preferential sampling: (1) continuous or finite spatial sampling frame; (2) underlying causal model and relevant covariates; and (3) inferential goals related to mean prediction surface or prediction uncertainty.

  15. Geostatistics and GIS: tools for characterizing environmental contamination.

    Science.gov (United States)

    Henshaw, Shannon L; Curriero, Frank C; Shields, Timothy M; Glass, Gregory E; Strickland, Paul T; Breysse, Patrick N

    2004-08-01

    Geostatistics is a set of statistical techniques used in the analysis of georeferenced data that can be applied to environmental contamination and remediation studies. In this study, the 1,1-dichloro-2,2-bis(p-chlorophenyl)ethylene (DDE) contamination at a Superfund site in western Maryland is evaluated. Concern about the site and its future clean up has triggered interest within the community because residential development surrounds the area. Spatial statistical methods, of which geostatistics is a subset, are becoming increasingly popular, in part due to the availability of geographic information system (GIS) software in a variety of application packages. In this article, the joint use of ArcGIS software and the R statistical computing environment are demonstrated as an approach for comprehensive geostatistical analyses. The spatial regression method, kriging, is used to provide predictions of DDE levels at unsampled locations both within the site and the surrounding areas where residential development is ongoing.

  16. Geostatistical inference using crosshole ground-penetrating radar

    DEFF Research Database (Denmark)

    Looms, Majken C; Hansen, Thomas Mejer; Cordua, Knud Skou

    2010-01-01

    , the moisture content will reflect the variation of the physical properties of the subsurface, which determine the flow patterns in the unsaturated zone. Deterministic least-squares inversion of crosshole groundpenetrating-radar GPR traveltimes result in smooth, minimumvariance estimates of the subsurface radar...... wave velocity structure, which may diminish the utility of these images for geostatistical inference. We have used a linearized stochastic inversion technique to infer the geostatistical properties of the subsurface radar wave velocity distribution using crosshole GPR traveltimes directly. Expanding...... of the subsurface are used to evaluate the uncertainty of the inversion estimate. We have explored the full potential of the geostatistical inference method using several synthetic models of varying correlation structures and have tested the influence of different assumptions concerning the choice of covariance...

  17. Geostatistical inference using crosshole ground-penetrating radar

    DEFF Research Database (Denmark)

    Looms, Majken C; Hansen, Thomas Mejer; Cordua, Knud Skou

    2010-01-01

    , the moisture content will reflect the variation of the physical properties of the subsurface, which determine the flow patterns in the unsaturated zone. Deterministic least-squares inversion of crosshole groundpenetrating-radar GPR traveltimes result in smooth, minimumvariance estimates of the subsurface radar...... wave velocity structure, which may diminish the utility of these images for geostatistical inference. We have used a linearized stochastic inversion technique to infer the geostatistical properties of the subsurface radar wave velocity distribution using crosshole GPR traveltimes directly. Expanding...

  18. Hydrogeologic unit flow characterization using transition probability geostatistics.

    Science.gov (United States)

    Jones, Norman L; Walker, Justin R; Carle, Steven F

    2005-01-01

    This paper describes a technique for applying the transition probability geostatistics method for stochastic simulation to a MODFLOW model. Transition probability geostatistics has some advantages over traditional indicator kriging methods including a simpler and more intuitive framework for interpreting geologic relationships and the ability to simulate juxtapositional tendencies such as fining upward sequences. The indicator arrays generated by the transition probability simulation are converted to layer elevation and thickness arrays for use with the new Hydrogeologic Unit Flow package in MODFLOW 2000. This makes it possible to preserve complex heterogeneity while using reasonably sized grids and/or grids with nonuniform cell thicknesses.

  19. Random spatial processes and geostatistical models for soil variables

    Science.gov (United States)

    Lark, R. M.

    2009-04-01

    Geostatistical models of soil variation have been used to considerable effect to facilitate efficient and powerful prediction of soil properties at unsampled sites or over partially sampled regions. Geostatistical models can also be used to investigate the scaling behaviour of soil process models, to design sampling strategies and to account for spatial dependence in the random effects of linear mixed models for spatial variables. However, most geostatistical models (variograms) are selected for reasons of mathematical convenience (in particular, to ensure positive definiteness of the corresponding variables). They assume some underlying spatial mathematical operator which may give a good description of observed variation of the soil, but which may not relate in any clear way to the processes that we know give rise to that observed variation in the real world. In this paper I shall argue that soil scientists should pay closer attention to the underlying operators in geostatistical models, with a view to identifying, where ever possible, operators that reflect our knowledge of processes in the soil. I shall illustrate how this can be done in the case of two problems. The first exemplar problem is the definition of operators to represent statistically processes in which the soil landscape is divided into discrete domains. This may occur at disparate scales from the landscape (outcrops, catchments, fields with different landuse) to the soil core (aggregates, rhizospheres). The operators that underly standard geostatistical models of soil variation typically describe continuous variation, and so do not offer any way to incorporate information on processes which occur in discrete domains. I shall present the Poisson Voronoi Tessellation as an alternative spatial operator, examine its corresponding variogram, and apply these to some real data. The second exemplar problem arises from different operators that are equifinal with respect to the variograms of the

  20. Geostatistical Solutions for Downscaling Remotely Sensed Land Surface Temperature

    Science.gov (United States)

    Wang, Q.; Rodriguez-Galiano, V.; Atkinson, P. M.

    2017-09-01

    Remotely sensed land surface temperature (LST) downscaling is an important issue in remote sensing. Geostatistical methods have shown their applicability in downscaling multi/hyperspectral images. In this paper, four geostatistical solutions, including regression kriging (RK), downscaling cokriging (DSCK), kriging with external drift (KED) and area-to-point regression kriging (ATPRK), are applied for downscaling remotely sensed LST. Their differences are analyzed theoretically and the performances are compared experimentally using a Landsat 7 ETM+ dataset. They are also compared to the classical TsHARP method.

  1. Gstat: a program for geostatistical modelling, prediction and simulation

    Science.gov (United States)

    Pebesma, Edzer J.; Wesseling, Cees G.

    1998-01-01

    Gstat is a computer program for variogram modelling, and geostatistical prediction and simulation. It provides a generic implementation of the multivariable linear model with trends modelled as a linear function of coordinate polynomials or of user-defined base functions, and independent or dependent, geostatistically modelled, residuals. Simulation in gstat comprises conditional or unconditional (multi-) Gaussian sequential simulation of point values or block averages, or (multi-) indicator sequential simulation. Besides many of the popular options found in other geostatistical software packages, gstat offers the unique combination of (i) an interactive user interface for modelling variograms and generalized covariances (residual variograms), that uses the device-independent plotting program gnuplot for graphical display, (ii) support for several ascii and binary data and map file formats for input and output, (iii) a concise, intuitive and flexible command language, (iv) user customization of program defaults, (v) no built-in limits, and (vi) free, portable ANSI-C source code. This paper describes the class of problems gstat can solve, and addresses aspects of efficiency and implementation, managing geostatistical projects, and relevant technical details.

  2. Reducing uncertainty in geostatistical description with well testing pressure data

    Energy Technology Data Exchange (ETDEWEB)

    Reynolds, A.C.; He, Nanqun [Univ. of Tulsa, OK (United States); Oliver, D.S. [Chevron Petroleum Technology Company, La Habra, CA (United States)

    1997-08-01

    Geostatistics has proven to be an effective tool for generating realizations of reservoir properties conditioned to static data, e.g., core and log data and geologic knowledge. Due to the lack of closely spaced data in the lateral directions, there will be significant variability in reservoir descriptions generated by geostatistical simulation, i.e., significant uncertainty in the reservoir descriptions. In past work, we have presented procedures based on inverse problem theory for generating reservoir descriptions (rock property fields) conditioned to pressure data and geostatistical information represented as prior means for log-permeability and porosity and variograms. Although we have shown that the incorporation of pressure data reduces the uncertainty below the level contained in the geostatistical model based only on static information (the prior model), our previous results assumed did not explicitly account for uncertainties in the prior means and the parameters defining the variogram model. In this work, we investigate how pressure data can help detect errors in the prior means. If errors in the prior means are large and are not taken into account, realizations conditioned to pressure data represent incorrect samples of the a posteriori probability density function for the rock property fields, whereas, if the uncertainty in the prior mean is incorporated properly into the model, one obtains realistic realizations of the rock property fields.

  3. Geostatistical Modeling of Evolving Landscapes by Means of Image Quilting

    Science.gov (United States)

    Mendes, J. H.; Caers, J.; Scheidt, C.

    2015-12-01

    Realistic geological representation of subsurface heterogeneity remains an important outstanding challenge. While many geostatistical methods exist for representing sedimentary systems, such as multiple-point geostatistics, rule-based methods or Boolean methods, the question of what the prior uncertainty on parameters (or training images) of such algorithms are, remains outstanding. In this initial work, we investigate the use of flume experiments to constrain better such prior uncertainty and to start understanding what information should be provided to geostatistical algorithms. In particular, we study the use of image quilting as a novel multiple-point method for generating fast geostatistical realizations once a training image is provided. Image quilting is a method emanating from computer graphics where patterns are extracted from training images and then stochastically quilted along a raster path to create stochastic variation of the stated training image. In this initial study, we use a flume experiment and extract 10 training images as representative for the variability of the evolving landscape over a period of 136 minutes. The training images consists of wet/dry regions obtained from overhead shots taken over the flume experiment. To investigate whether such image quilting reproduces the same variability of the evolving landscape in terms of wet/dry regions, we generate multiple realizations with all 10 training images and compare that variability with the variability seen in the entire flume experiment. By proper tuning of the quilting parameters we find generally reasonable agreement with the flume experiment.

  4. Multiple Point Geostatistics for automated landform mapping

    Science.gov (United States)

    Karssenberg, D.; Vannametee, E.; Babel, L.; Schuur, J.; Hendriks, M.; Bierkens, M. F.

    2011-12-01

    Land-surface processes are often studied at the level of elementary landform units, e.g. geomorphological units. To avoid expensive and difficult field surveys and to ensure a consistent mapping scheme, automated derivation of these units is desirable. However, automated classification based on two-point statistics of topographical attributes (e.g. semivarigram) is inadequate in reproducing complex, curvilinear landform patterns. Therefore, the spatial structure and configuration of terrain characteristics suitable for landform classification should be based on statistics from multiple points. In this study, a generic automated landform classification routine is developed which is based on Multiple Point Geostatistics (MPG) using information from a field map of geomorphology in a training area and a gridded Digital Elevation Model (DEM). Focus is on classification of geomorphologic units; e.g. alluvial fan, river terrace. The approach is evaluated using data from the French Alps. In the first procedural step, spatial statistics of the geomorphologic units are retrieved from a training data set, consisting of a digital elevation model and a geomorphologic map, created using field observations and 37.5 x 37.5 m2 cells. For each grid cell in the training data set, the geomorphological unit of the grid cell and a set of topographical attributes (i.e. a pattern) of the grid cell is stored in a frequency database. The set of topographical attributes stored is chosen such that it represents criteria used in field mapping. These are, for instance, topographical slope gradient, upstream area, or geomorphological units mapped in the neighborhood of the cell. Continuous information (e.g. slope) is converted to categorical data (slope class), which is required in the MPG approach. The second step is to use the knowledge stored in the frequency database for mapping. The algorithm reads a set of attribute classes from a classification target cell and its surrounding cells taking

  5. Stochastic Local Interaction (SLI) model: Bridging machine learning and geostatistics

    Science.gov (United States)

    Hristopulos, Dionissios T.

    2015-12-01

    Machine learning and geostatistics are powerful mathematical frameworks for modeling spatial data. Both approaches, however, suffer from poor scaling of the required computational resources for large data applications. We present the Stochastic Local Interaction (SLI) model, which employs a local representation to improve computational efficiency. SLI combines geostatistics and machine learning with ideas from statistical physics and computational geometry. It is based on a joint probability density function defined by an energy functional which involves local interactions implemented by means of kernel functions with adaptive local kernel bandwidths. SLI is expressed in terms of an explicit, typically sparse, precision (inverse covariance) matrix. This representation leads to a semi-analytical expression for interpolation (prediction), which is valid in any number of dimensions and avoids the computationally costly covariance matrix inversion.

  6. Assessing the resolution-dependent utility of tomograms for geostatistics

    Science.gov (United States)

    Day-Lewis, F. D.; Lane, J.W.

    2004-01-01

    Geophysical tomograms are used increasingly as auxiliary data for geostatistical modeling of aquifer and reservoir properties. The correlation between tomographic estimates and hydrogeologic properties is commonly based on laboratory measurements, co-located measurements at boreholes, or petrophysical models. The inferred correlation is assumed uniform throughout the interwell region; however, tomographic resolution varies spatially due to acquisition geometry, regularization, data error, and the physics underlying the geophysical measurements. Blurring and inversion artifacts are expected in regions traversed by few or only low-angle raypaths. In the context of radar traveltime tomography, we derive analytical models for (1) the variance of tomographic estimates, (2) the spatially variable correlation with a hydrologic parameter of interest, and (3) the spatial covariance of tomographic estimates. Synthetic examples demonstrate that tomograms of qualitative value may have limited utility for geostatistics; moreover, the imprint of regularization may preclude inference of meaningful spatial statistics from tomograms.

  7. 4th European Conference on Geostatistics for Environmental Applications

    CERN Document Server

    Carrera, Jesus; Gómez-Hernández, José

    2004-01-01

    The fourth edition of the European Conference on Geostatistics for Environmental Applications (geoENV IV) took place in Barcelona, November 27-29, 2002. As a proof that there is an increasing interest in environmental issues in the geostatistical community, the conference attracted over 100 participants, mostly Europeans (up to 10 European countries were represented), but also from other countries in the world. Only 46 contributions, selected out of around 100 submitted papers, were invited to be presented orally during the conference. Additionally 30 authors were invited to present their work in poster format during a special session. All oral and poster contributors were invited to submit their work to be considered for publication in this Kluwer series. All papers underwent a reviewing process, which consisted on two reviewers for oral presentations and one reviewer for posters. The book opens with one keynote paper by Philippe Naveau. It is followed by 40 papers that correspond to those presented orally d...

  8. Mapping malaria risk in Bangladesh using Bayesian geostatistical models.

    Science.gov (United States)

    Reid, Heidi; Haque, Ubydul; Clements, Archie C A; Tatem, Andrew J; Vallely, Andrew; Ahmed, Syed Masud; Islam, Akramul; Haque, Rashidul

    2010-10-01

    Background malaria-control programs are increasingly dependent on accurate risk maps to effectively guide the allocation of interventions and resources. Advances in model-based geostatistics and geographical information systems (GIS) have enabled researchers to better understand factors affecting malaria transmission and thus, more accurately determine the limits of malaria transmission globally and nationally. Here, we construct Plasmodium falciparum risk maps for Bangladesh for 2007 at a scale enabling the malaria-control bodies to more accurately define the needs of the program. A comprehensive malaria-prevalence survey (N = 9,750 individuals; N = 354 communities) was carried out in 2007 across the regions of Bangladesh known to be endemic for malaria. Data were corrected to a standard age range of 2 to less than 10 years. Bayesian geostatistical logistic regression models with environmental covariates were used to predict P. falciparum prevalence for 2- to 10-year-old children (PfPR(2-10)) across the endemic areas of Bangladesh. The predictions were combined with gridded population data to estimate the number of individuals living in different endemicity classes. Across the endemic areas, the average PfPR(2-10) was 3.8%. Environmental variables selected for prediction were vegetation cover, minimum temperature, and elevation. Model validation statistics revealed that the final Bayesian geostatistical model had good predictive ability. Risk maps generated from the model showed a heterogeneous distribution of PfPR(2-10) ranging from 0.5% to 50%; 3.1 million people were estimated to be living in areas with a PfPR(2-10) greater than 1%. Contemporary GIS and model-based geostatistics can be used to interpolate malaria risk in Bangladesh. Importantly, malaria risk was found to be highly varied across the endemic regions, necessitating the targeting of resources to reduce the burden in these areas.

  9. A reservoir skeleton-based multiple point geostatistics method

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    Traditional stochastic reservoir modeling,including object-based and pixel-based methods,cannot solve the problem of reproducing continuous and curvilinear reservoir objects. The paper first dives into the various stochastic modeling methods and extracts their merits,then proposes the skeleton-based multiple point geostatistics(SMPS) for the fluvial reservoir. The core idea is using the skeletons of reservoir objects to restrict the selection of data patterns. The skeleton-based multiple point geostatistics consists of two steps. First,predicting the channel skeleton(namely,channel centerline) by using the method in object-based modeling. The paper proposes a new method of search window to predict the skeleton. Then forecasting the distributions of reservoir objects using multiple point geostatistics with the restriction of channel skeleton. By the restriction of channel centerline,the selection of data events will be more reasonable and the realization will be achieved more really. The checks by the conceptual model and the real reservoir show that SMPS is much better than Sisim(sequential indicator simulation) ,Snesim(Single Normal Equation Simulation) and Simpat(simulation with patterns) in building the fluvial reservoir model. This new method will contribute to both the theoretical research of stochastic modeling and the oilfield developments of constructing highly precise reservoir geological models.

  10. Breast carcinoma, intratumour heterogeneity and histological grading, using geostatistics.

    Science.gov (United States)

    Sharifi-Salamatian, V; de Roquancourt, A; Rigaut, J P

    2000-01-01

    Tumour progression is currently believed to result from genetic instability. Chromosomal patterns specific of a type of cancer are frequent even though phenotypic spatial heterogeneity is omnipresent. The latter is the usual cause of histological grading imprecision, a well documented problem, without any fully satisfactory solution up to now. The present article addresses this problem in breast carcinoma. The assessment of a genetic marker for human tumours requires quantifiable measures of intratumoral heterogeneity. If any invariance paradigm representing a stochastic or geostatistic function could be discovered, this might help in solving the grading problem. A novel methodological approach using geostatistics to measure heterogeneity is used. Twenty tumours from the three usual (Scarff-Bloom and Richardson) grades were obtained and paraffin sections stained by MIB-1 (Ki-67) and peroxidase staining. Whole two-dimensional sections were sampled. Morphometric grids of variable sizes allowed a simple and fast recording of positions of epithelial nuclei, marked or not by MIB-1. The geostatistical method is based here upon the asymptotic behaviour of dispersion variance. Measure of asymptotic exponent of dispersion variance shows an increase from grade 1 to grade 3. Preliminary results are encouraging: grades 1 and 3 on one hand and 2 and 3 on the other hand are totally separated. The final proof of an improved grading using this measure will of course require a confrontation with the results of survival studies.

  11. 2nd European Conference on Geostatistics for Environmental Applications

    CERN Document Server

    Soares, Amílcar; Froidevaux, Roland

    1999-01-01

    The Second European Conference on Geostatistics for Environmental Ap­ plications took place in Valencia, November 18-20, 1998. Two years have past from the first meeting in Lisbon and the geostatistical community has kept active in the environmental field. In these days of congress inflation, we feel that continuity can only be achieved by ensuring quality in the papers. For this reason, all papers in the book have been reviewed by, at least, two referees, and care has been taken to ensure that the reviewer comments have been incorporated in the final version of the manuscript. We are thankful to the members of the scientific committee for their timely review of the scripts. All in all, there are three keynote papers from experts in soil science, climatology and ecology and 43 contributed papers providing a good indication of the status of geostatistics as applied in the environ­ mental field all over the world. We feel now confident that the geoENV conference series, seeded around a coffee table almost six...

  12. Spatial distribution of decapod crustaceans in the Galician continental shelf (NW Spain) using geostatistical analysis

    National Research Council Canada - National Science Library

    Freire, J; Fernandez, L; Gonzalez-Gurriaran, E

    1991-01-01

    Geostatistical methodology was applied to analyze spatial structure and distribution of the epibenthic crustaceans Liocarcinus depurator, Macropipus tuberculatus, Polybius henslowii, Munida intermedia...

  13. Geospatial Interpolation and Mapping of Tropospheric Ozone Pollution Using Geostatistics

    Directory of Open Access Journals (Sweden)

    Swatantra R. Kethireddy

    2014-01-01

    Full Text Available Tropospheric ozone (O3 pollution is a major problem worldwide, including in the United States of America (USA, particularly during the summer months. Ozone oxidative capacity and its impact on human health have attracted the attention of the scientific community. In the USA, sparse spatial observations for O3 may not provide a reliable source of data over a geo-environmental region. Geostatistical Analyst in ArcGIS has the capability to interpolate values in unmonitored geo-spaces of interest. In this study of eastern Texas O3 pollution, hourly episodes for spring and summer 2012 were selectively identified. To visualize the O3 distribution, geostatistical techniques were employed in ArcMap. Using ordinary Kriging, geostatistical layers of O3 for all the studied hours were predicted and mapped at a spatial resolution of 1 kilometer. A decent level of prediction accuracy was achieved and was confirmed from cross-validation results. The mean prediction error was close to 0, the root mean-standardized-prediction error was close to 1, and the root mean square and average standard errors were small. O3 pollution map data can be further used in analysis and modeling studies. Kriging results and O3 decadal trends indicate that the populace in Houston-Sugar Land-Baytown, Dallas-Fort Worth-Arlington, Beaumont-Port Arthur, San Antonio, and Longview are repeatedly exposed to high levels of O3-related pollution, and are prone to the corresponding respiratory and cardiovascular health effects. Optimization of the monitoring network proves to be an added advantage for the accurate prediction of exposure levels.

  14. Geospatial interpolation and mapping of tropospheric ozone pollution using geostatistics.

    Science.gov (United States)

    Kethireddy, Swatantra R; Tchounwou, Paul B; Ahmad, Hafiz A; Yerramilli, Anjaneyulu; Young, John H

    2014-01-10

    Tropospheric ozone (O3) pollution is a major problem worldwide, including in the United States of America (USA), particularly during the summer months. Ozone oxidative capacity and its impact on human health have attracted the attention of the scientific community. In the USA, sparse spatial observations for O3 may not provide a reliable source of data over a geo-environmental region. Geostatistical Analyst in ArcGIS has the capability to interpolate values in unmonitored geo-spaces of interest. In this study of eastern Texas O3 pollution, hourly episodes for spring and summer 2012 were selectively identified. To visualize the O3 distribution, geostatistical techniques were employed in ArcMap. Using ordinary Kriging, geostatistical layers of O3 for all the studied hours were predicted and mapped at a spatial resolution of 1 kilometer. A decent level of prediction accuracy was achieved and was confirmed from cross-validation results. The mean prediction error was close to 0, the root mean-standardized-prediction error was close to 1, and the root mean square and average standard errors were small. O3 pollution map data can be further used in analysis and modeling studies. Kriging results and O3 decadal trends indicate that the populace in Houston-Sugar Land-Baytown, Dallas-Fort Worth-Arlington, Beaumont-Port Arthur, San Antonio, and Longview are repeatedly exposed to high levels of O3-related pollution, and are prone to the corresponding respiratory and cardiovascular health effects. Optimization of the monitoring network proves to be an added advantage for the accurate prediction of exposure levels.

  15. Assessment of spatial distribution of fallout radionuclides through geostatistics concept.

    Science.gov (United States)

    Mabit, L; Bernard, C

    2007-01-01

    After introducing geostatistics concept and its utility in environmental science and especially in Fallout Radionuclide (FRN) spatialisation, a case study for cesium-137 ((137)Cs) redistribution at the field scale using geostatistics is presented. On a Canadian agricultural field, geostatistics coupled with a Geographic Information System (GIS) was used to test three different techniques of interpolation [Ordinary Kriging (OK), Inverse Distance Weighting power one (IDW1) and two (IDW2)] to create a (137)Cs map and to establish a radioisotope budget. Following the optimization of variographic parameters, an experimental semivariogram was developed to determine the spatial dependence of (137)Cs. It was adjusted to a spherical isotropic model with a range of 30 m and a very small nugget effect. This (137)Cs semivariogram showed a good autocorrelation (R(2)=0.91) and was well structured ('nugget-to-sill' ratio of 4%). It also revealed that the sampling strategy was adequate to reveal the spatial correlation of (137)Cs. The spatial redistribution of (137)Cs was estimated by Ordinary Kriging and IDW to produce contour maps. A radioisotope budget was established for the 2.16 ha agricultural field under investigation. It was estimated that around 2 x 10(7)Bq of (137)Cs were missing (around 30% of the total initial fallout) and were exported by physical processes (runoff and erosion processes) from the area under investigation. The cross-validation analysis showed that in the case of spatially structured data, OK is a better interpolation method than IDW1 or IDW2 for the assessment of potential radioactive contamination and/or pollution.

  16. Mercury emissions from coal combustion in Silesia, analysis using geostatistics

    Science.gov (United States)

    Zasina, Damian; Zawadzki, Jaroslaw

    2015-04-01

    Data provided by the UNEP's report on mercury [1] shows that solid fuel combustion in significant source of mercury emission to air. Silesia, located in southwestern Poland, is notably affected by mercury emission due to being one of the most industrialized Polish regions: the place of coal mining, production of metals, stone mining, mineral quarrying and chemical industry. Moreover, Silesia is the region with high population density. People are exposed to severe risk of mercury emitted from both: industrial and domestic sources (i.e. small household furnaces). Small sources have significant contribution to total emission of mercury. Official and statistical analysis, including prepared for international purposes [2] did not provide data about spatial distribution of the mercury emitted to air, however number of analysis on Polish public power and energy sector had been prepared so far [3; 4]. The distribution of locations exposed for mercury emission from small domestic sources is interesting matter merging information from various sources: statistical, economical and environmental. This paper presents geostatistical approach to distibution of mercury emission from coal combustion. Analysed data organized in 2 independent levels: individual, bottom-up approach derived from national emission reporting system [5; 6] and top down - regional data calculated basing on official statistics [7]. Analysis, that will be presented, will include comparison of spatial distributions of mercury emission using data derived from sources mentioned above. Investigation will include three voivodeships of Poland: Lower Silesian, Opole (voivodeship) and Silesian using selected geostatistical methodologies including ordinary kriging [8]. References [1] UNEP. Global Mercury Assessment 2013: Sources, Emissions, Releases and Environmental Transport. UNEP Chemicals Branch, Geneva, Switzerland, 2013. [2] NCEM. Poland's Informative Inventory Report 2014. NCEM at the IEP-NRI, 2014. http

  17. Geostatistical inference using crosshole ground-penetrating radar

    DEFF Research Database (Denmark)

    Looms, Majken C; Hansen, Thomas Mejer; Cordua, Knud Skou;

    2010-01-01

    of the subsurface are used to evaluate the uncertainty of the inversion estimate. We have explored the full potential of the geostatistical inference method using several synthetic models of varying correlation structures and have tested the influence of different assumptions concerning the choice of covariance...... function and data noise level. In addition, we have tested the methodology on traveltime data collected at a field site in Denmark. There, inferred correlation structures indicate that structural differences exist between two areas located approximately 10 m apart, an observation confirmed by a GPR...

  18. Regional flow duration curves: Geostatistical techniques versus multivariate regression

    Science.gov (United States)

    Pugliese, Alessio; Farmer, William H.; Castellarin, Attilio; Archfield, Stacey A.; Vogel, Richard M.

    2016-10-01

    A period-of-record flow duration curve (FDC) represents the relationship between the magnitude and frequency of daily streamflows. Prediction of FDCs is of great importance for locations characterized by sparse or missing streamflow observations. We present a detailed comparison of two methods which are capable of predicting an FDC at ungauged basins: (1) an adaptation of the geostatistical method, Top-kriging, employing a linear weighted average of dimensionless empirical FDCs, standardised with a reference streamflow value; and (2) regional multiple linear regression of streamflow quantiles, perhaps the most common method for the prediction of FDCs at ungauged sites. In particular, Top-kriging relies on a metric for expressing the similarity between catchments computed as the negative deviation of the FDC from a reference streamflow value, which we termed total negative deviation (TND). Comparisons of these two methods are made in 182 largely unregulated river catchments in the southeastern U.S. using a three-fold cross-validation algorithm. Our results reveal that the two methods perform similarly throughout flow-regimes, with average Nash-Sutcliffe Efficiencies 0.566 and 0.662, (0.883 and 0.829 on log-transformed quantiles) for the geostatistical and the linear regression models, respectively. The differences between the reproduction of FDC's occurred mostly for low flows with exceedance probability (i.e. duration) above 0.98.

  19. Validating spatial structure in canopy water content using geostatistics

    Science.gov (United States)

    Sanderson, E. W.; Zhang, M. H.; Ustin, S. L.; Rejmankova, E.; Haxo, R. S.

    1995-01-01

    Heterogeneity in ecological phenomena are scale dependent and affect the hierarchical structure of image data. AVIRIS pixels average reflectance produced by complex absorption and scattering interactions between biogeochemical composition, canopy architecture, view and illumination angles, species distributions, and plant cover as well as other factors. These scales affect validation of pixel reflectance, typically performed by relating pixel spectra to ground measurements acquired at scales of 1m(exp 2) or less (e.g., field spectra, foilage and soil samples, etc.). As image analysis becomes more sophisticated, such as those for detection of canopy chemistry, better validation becomes a critical problem. This paper presents a methodology for bridging between point measurements and pixels using geostatistics. Geostatistics have been extensively used in geological or hydrogeolocial studies but have received little application in ecological studies. The key criteria for kriging estimation is that the phenomena varies in space and that an underlying controlling process produces spatial correlation between the measured data points. Ecological variation meets this requirement because communities vary along environmental gradients like soil moisture, nutrient availability, or topography.

  20. Spatial prediction of soil penetration resistance using functional geostatistics

    Directory of Open Access Journals (Sweden)

    Diego Leonardo Cortés-D

    Full Text Available ABSTRACT Knowledge of agricultural soils is a relevant factor for the sustainable development of farming activities. Studies on agricultural soils usually begin with the analysis of data obtained from sampling a finite number of sites in a particular region of interest. The variables measured at each site can be scalar (chemical properties or functional (infiltration water or penetration resistance. The use of functional geostatistics (FG allows to perform spatial curve interpolation to generate prediction curves (instead of single variables at sites that lack information. This study analyzed soil penetration resistance (PR data measured between 0 and 35 cm depth at 75 sites within a 37 ha plot dedicated to livestock. The data from each site were converted to curves using non-parametric smoothing techniques. In this study, a B-splines basis of 18 functions was used to estimate PR curves for each of the 75 sites. The applicability of FG as a spatial prediction tool for PR curves was then evaluated using cross-validation, and the results were compared with classical spatial prediction methods (univariate geostatistics that are generally used for studying this type of information. We concluded that FG is a reliable tool for analyzing PR because a high correlation was obtained between the observed and predicted curves (R2 = 94 %. In addition, the results from descriptive analyses calculated from field data and FG models were similar for the observed and predicted values.

  1. Regional flow duration curves: Geostatistical techniques versus multivariate regression

    Science.gov (United States)

    Pugliese, Alessio; Farmer, William H.; Castellarin, Attilio; Archfield, Stacey A.; Vogel, Richard M.

    2016-01-01

    A period-of-record flow duration curve (FDC) represents the relationship between the magnitude and frequency of daily streamflows. Prediction of FDCs is of great importance for locations characterized by sparse or missing streamflow observations. We present a detailed comparison of two methods which are capable of predicting an FDC at ungauged basins: (1) an adaptation of the geostatistical method, Top-kriging, employing a linear weighted average of dimensionless empirical FDCs, standardised with a reference streamflow value; and (2) regional multiple linear regression of streamflow quantiles, perhaps the most common method for the prediction of FDCs at ungauged sites. In particular, Top-kriging relies on a metric for expressing the similarity between catchments computed as the negative deviation of the FDC from a reference streamflow value, which we termed total negative deviation (TND). Comparisons of these two methods are made in 182 largely unregulated river catchments in the southeastern U.S. using a three-fold cross-validation algorithm. Our results reveal that the two methods perform similarly throughout flow-regimes, with average Nash-Sutcliffe Efficiencies 0.566 and 0.662, (0.883 and 0.829 on log-transformed quantiles) for the geostatistical and the linear regression models, respectively. The differences between the reproduction of FDC's occurred mostly for low flows with exceedance probability (i.e. duration) above 0.98.

  2. A Geostatistical Approach to Indoor Surface Sampling Strategies

    DEFF Research Database (Denmark)

    Schneider, Thomas; Petersen, Ole Holm; Nielsen, Allan Aasbjerg

    1990-01-01

    framework for designing sampling strategies is thus developed. The distribution and spatial correlation of surface contamination can be characterized using concepts from geostatistical science, where spatial applications of statistics is most developed. The theory is summarized and particulate surface......Particulate surface contamination is of concern in production industries such as food processing, aerospace, electronics and semiconductor manufacturing. There is also an increased awareness that surface contamination should be monitored in industrial hygiene surveys. A conceptual and theoretical...... contamination, sampled from small areas on a table, have been used to illustrate the method. First, the spatial correlation is modelled and the parameters estimated from the data. Next, it is shown how the contamination at positions not measured can be estimated with kriging, a minimum mean square error method...

  3. Geostatistical sampling optimization and waste characterization of contaminated premises

    Energy Technology Data Exchange (ETDEWEB)

    Desnoyers, Y.; Jeannee, N. [GEOVARIANCES, 49bis avenue Franklin Roosevelt, BP91, Avon, 77212 (France); Chiles, J.P. [Centre de geostatistique, Ecole des Mines de Paris (France); Dubot, D. [CEA DSV/FAR/USLT/SPRE/SAS (France); Lamadie, F. [CEA DEN/VRH/DTEC/SDTC/LTM (France)

    2009-06-15

    At the end of process equipment dismantling, the complete decontamination of nuclear facilities requires a radiological assessment of the building structure residual activity. From this point of view, the set up of an appropriate evaluation methodology is of crucial importance. The radiological characterization of contaminated premises can be divided into three steps. First, the most exhaustive facility analysis provides historical and qualitative information. Then, a systematic (exhaustive) control of the emergent signal is commonly performed using in situ measurement methods such as surface controls combined with in situ gamma spectrometry. Finally, in order to assess the contamination depth, samples are collected at several locations within the premises and analyzed. Combined with historical information and emergent signal maps, such data allow the definition of a preliminary waste zoning. The exhaustive control of the emergent signal with surface measurements usually leads to inaccurate estimates, because of several factors: varying position of the measuring device, subtraction of an estimate of the background signal, etc. In order to provide reliable estimates while avoiding supplementary investigation costs, there is therefore a crucial need for sampling optimization methods together with appropriate data processing techniques. The initial activity usually presents a spatial continuity within the premises, with preferential contamination of specific areas or existence of activity gradients. Taking into account this spatial continuity is essential to avoid bias while setting up the sampling plan. In such a case, Geostatistics provides methods that integrate the contamination spatial structure. After the characterization of this spatial structure, most probable estimates of the surface activity at un-sampled locations can be derived using kriging techniques. Variants of these techniques also give access to estimates of the uncertainty associated to the spatial

  4. A Geostatistical Approach to Indoor Surface Sampling Strategies

    DEFF Research Database (Denmark)

    Schneider, Thomas; Petersen, Ole Holm; Nielsen, Allan Aasbjerg

    1990-01-01

    contamination, sampled from small areas on a table, have been used to illustrate the method. First, the spatial correlation is modelled and the parameters estimated from the data. Next, it is shown how the contamination at positions not measured can be estimated with kriging, a minimum mean square error method...... using the global information. Then methods for choosing a proper sampling area for a single sample of dust on a table are given. The global contamination of an object is determined by a maximum likelihood estimator. Finally, it is shown how specified experimental goals can be included to determine...... framework for designing sampling strategies is thus developed. The distribution and spatial correlation of surface contamination can be characterized using concepts from geostatistical science, where spatial applications of statistics is most developed. The theory is summarized and particulate surface...

  5. Bayesian geostatistics in health cartography: the perspective of malaria.

    Science.gov (United States)

    Patil, Anand P; Gething, Peter W; Piel, Frédéric B; Hay, Simon I

    2011-06-01

    Maps of parasite prevalences and other aspects of infectious diseases that vary in space are widely used in parasitology. However, spatial parasitological datasets rarely, if ever, have sufficient coverage to allow exact determination of such maps. Bayesian geostatistics (BG) is a method for finding a large sample of maps that can explain a dataset, in which maps that do a better job of explaining the data are more likely to be represented. This sample represents the knowledge that the analyst has gained from the data about the unknown true map. BG provides a conceptually simple way to convert these samples to predictions of features of the unknown map, for example regional averages. These predictions account for each map in the sample, yielding an appropriate level of predictive precision.

  6. Medical Geography: a Promising Field of Application for Geostatistics.

    Science.gov (United States)

    Goovaerts, P

    2009-01-01

    The analysis of health data and putative covariates, such as environmental, socio-economic, behavioral or demographic factors, is a promising application for geostatistics. It presents, however, several methodological challenges that arise from the fact that data are typically aggregated over irregular spatial supports and consist of a numerator and a denominator (i.e. population size). This paper presents an overview of recent developments in the field of health geostatistics, with an emphasis on three main steps in the analysis of areal health data: estimation of the underlying disease risk, detection of areas with significantly higher risk, and analysis of relationships with putative risk factors. The analysis is illustrated using age-adjusted cervix cancer mortality rates recorded over the 1970-1994 period for 118 counties of four states in the Western USA. Poisson kriging allows the filtering of noisy mortality rates computed from small population sizes, enhancing the correlation with two putative explanatory variables: percentage of habitants living below the federally defined poverty line, and percentage of Hispanic females. Area-to-point kriging formulation creates continuous maps of mortality risk, reducing the visual bias associated with the interpretation of choropleth maps. Stochastic simulation is used to generate realizations of cancer mortality maps, which allows one to quantify numerically how the uncertainty about the spatial distribution of health outcomes translates into uncertainty about the location of clusters of high values or the correlation with covariates. Last, geographically-weighted regression highlights the non-stationarity in the explanatory power of covariates: the higher mortality values along the coast are better explained by the two covariates than the lower risk recorded in Utah.

  7. Combining geostatistics with Moran's I analysis for mapping soil heavy metals in Beijing, China.

    Science.gov (United States)

    Huo, Xiao-Ni; Li, Hong; Sun, Dan-Feng; Zhou, Lian-Di; Li, Bao-Guo

    2012-03-01

    Production of high quality interpolation maps of heavy metals is important for risk assessment of environmental pollution. In this paper, the spatial correlation characteristics information obtained from Moran's I analysis was used to supplement the traditional geostatistics. According to Moran's I analysis, four characteristics distances were obtained and used as the active lag distance to calculate the semivariance. Validation of the optimality of semivariance demonstrated that using the two distances where the Moran's I and the standardized Moran's I, Z(I) reached a maximum as the active lag distance can improve the fitting accuracy of semivariance. Then, spatial interpolation was produced based on the two distances and their nested model. The comparative analysis of estimation accuracy and the measured and predicted pollution status showed that the method combining geostatistics with Moran's I analysis was better than traditional geostatistics. Thus, Moran's I analysis is a useful complement for geostatistics to improve the spatial interpolation accuracy of heavy metals.

  8. Geostatistical methods for the integrated information; Metodos geoestadisticos para la integracion de informacion

    Energy Technology Data Exchange (ETDEWEB)

    Cassiraga, E.F.; Gomez-Hernandez, J.J. [Departamento de Ingenieria Hidraulica y Medio Ambiente, Universidad Politecnica de Valencia, Valencia (Spain)

    1996-10-01

    The main objective of this report is to describe the different geostatistical techniques to use the geophysical and hydrological parameters. We analyze the characteristics of estimation methods used in others studies.

  9. A geostatistics-informed hierarchical sensitivity analysis method for complex groundwater flow and transport modeling: GEOSTATISTICAL SENSITIVITY ANALYSIS

    Energy Technology Data Exchange (ETDEWEB)

    Dai, Heng [Pacific Northwest National Laboratory, Richland Washington USA; Chen, Xingyuan [Pacific Northwest National Laboratory, Richland Washington USA; Ye, Ming [Department of Scientific Computing, Florida State University, Tallahassee Florida USA; Song, Xuehang [Pacific Northwest National Laboratory, Richland Washington USA; Zachara, John M. [Pacific Northwest National Laboratory, Richland Washington USA

    2017-05-01

    Sensitivity analysis is an important tool for quantifying uncertainty in the outputs of mathematical models, especially for complex systems with a high dimension of spatially correlated parameters. Variance-based global sensitivity analysis has gained popularity because it can quantify the relative contribution of uncertainty from different sources. However, its computational cost increases dramatically with the complexity of the considered model and the dimension of model parameters. In this study we developed a hierarchical sensitivity analysis method that (1) constructs an uncertainty hierarchy by analyzing the input uncertainty sources, and (2) accounts for the spatial correlation among parameters at each level of the hierarchy using geostatistical tools. The contribution of uncertainty source at each hierarchy level is measured by sensitivity indices calculated using the variance decomposition method. Using this methodology, we identified the most important uncertainty source for a dynamic groundwater flow and solute transport in model at the Department of Energy (DOE) Hanford site. The results indicate that boundary conditions and permeability field contribute the most uncertainty to the simulated head field and tracer plume, respectively. The relative contribution from each source varied spatially and temporally as driven by the dynamic interaction between groundwater and river water at the site. By using a geostatistical approach to reduce the number of realizations needed for the sensitivity analysis, the computational cost of implementing the developed method was reduced to a practically manageable level. The developed sensitivity analysis method is generally applicable to a wide range of hydrologic and environmental problems that deal with high-dimensional spatially-distributed parameters.

  10. Analysis of dengue fever risk using geostatistics model in bone regency

    Science.gov (United States)

    Amran, Stang, Mallongi, Anwar

    2017-03-01

    This research aim is to analysis of dengue fever risk based on Geostatistics model in Bone Regency. Risk levels of dengue fever are denoted by parameter of Binomial distribution. Effect of temperature, rainfalls, elevation, and larvae abundance are investigated through Geostatistics model. Bayesian hierarchical method is used in estimation process. Using dengue fever data in eleven locations this research shows that temperature and rainfall have significant effect of dengue fever risk in Bone regency.

  11. Should hydraulic tomography data be interpreted using geostatistical inverse modeling? A laboratory sandbox investigation

    Science.gov (United States)

    Illman, Walter A.; Berg, Steven J.; Zhao, Zhanfeng

    2015-05-01

    The robust performance of hydraulic tomography (HT) based on geostatistics has been demonstrated through numerous synthetic, laboratory, and field studies. While geostatistical inverse methods offer many advantages, one key disadvantage is its highly parameterized nature, which renders it computationally intensive for large-scale problems. Another issue is that geostatistics-based HT may produce overly smooth images of subsurface heterogeneity when there are few monitoring interval data. Therefore, some may question the utility of the geostatistical inversion approach in certain situations and seek alternative approaches. To investigate these issues, we simultaneously calibrated different groundwater models with varying subsurface conceptualizations and parameter resolutions using a laboratory sandbox aquifer. The compared models included: (1) isotropic and anisotropic effective parameter models; (2) a heterogeneous model that faithfully represents the geological features; and (3) a heterogeneous model based on geostatistical inverse modeling. The performance of these models was assessed by quantitatively examining the results from model calibration and validation. Calibration data consisted of steady state drawdown data from eight pumping tests and validation data consisted of data from 16 separate pumping tests not used in the calibration effort. Results revealed that the geostatistical inversion approach performed the best among the approaches compared, although the geological model that faithfully represented stratigraphy came a close second. In addition, when the number of pumping tests available for inverse modeling was small, the geological modeling approach yielded more robust validation results. This suggests that better knowledge of stratigraphy obtained via geophysics or other means may contribute to improved results for HT.

  12. The Geostatistical Framework for Spatial Prediction%空间预测的地统计学框架

    Institute of Scientific and Technical Information of China (English)

    张景雄; 姚娜

    2008-01-01

    Geostatistics provides a coherent framework for spatial prediction and uncertainty assessment, whereby spatial dependence, as quantified by variograms, is utilized for best linear unbiased estimation of a regionalized variable at unsampied locations. Geostatistics for prediction of continuous regionalized variables is reviewed, with key methods underlying the derivation of major variants of uni-variate Kriging described in an easy-to-follow manner. This paper will contribute to demystification and, hence, popularization of geostatistics in geoinformatics communities.

  13. Bayesian Analysis of Geostatistical Models With an Auxiliary Lattice

    KAUST Repository

    Park, Jincheol

    2012-04-01

    The Gaussian geostatistical model has been widely used for modeling spatial data. However, this model suffers from a severe difficulty in computation: it requires users to invert a large covariance matrix. This is infeasible when the number of observations is large. In this article, we propose an auxiliary lattice-based approach for tackling this difficulty. By introducing an auxiliary lattice to the space of observations and defining a Gaussian Markov random field on the auxiliary lattice, our model completely avoids the requirement of matrix inversion. It is remarkable that the computational complexity of our method is only O(n), where n is the number of observations. Hence, our method can be applied to very large datasets with reasonable computational (CPU) times. The numerical results indicate that our model can approximate Gaussian random fields very well in terms of predictions, even for those with long correlation lengths. For real data examples, our model can generally outperform conventional Gaussian random field models in both prediction errors and CPU times. Supplemental materials for the article are available online. © 2012 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.

  14. Geostatistical interpolation for modelling SPT data in northern Izmir

    Indian Academy of Sciences (India)

    Selim Altun; A Burak Göktepe; Alper Sezer

    2013-12-01

    In this study, it was aimed to map the corrected Standard Penetration Test(SPT) values in Karşıyaka city center by kriging approach. Six maps were prepared by this geostatistical approach at depths of 3, 6, 9, 13.5, 18 and 25.5m. Borehole test results obtained from 388 boreholes in central Karşıyaka were used to model the spatial variation of $(\\text{N}_1)_{\\text{60cs}}$ values in an area of 5.5 km2. Corrections were made for depth, hammer energy, rod length, sampler, borehole diameter and fines content, to the data in hand. At various depths, prepared variograms and the kriging method were used together to model the variation of corrected SPT data in the region, which enabled the estimation of missing data in the region. The results revealed that the estimation ability of the models were acceptable, which were validated by a number of parameters as well as the comparisons of the actual and estimated data. Outcomes of this study can be used in microzonation studies, site response analyses, calculation of bearing capacity of subsoils in the region and producing a number of parameters which are empirically related to corrected SPT number as well.

  15. Bayesian geostatistical modeling of leishmaniasis incidence in Brazil.

    Directory of Open Access Journals (Sweden)

    Dimitrios-Alexios Karagiannis-Voules

    Full Text Available BACKGROUND: Leishmaniasis is endemic in 98 countries with an estimated 350 million people at risk and approximately 2 million cases annually. Brazil is one of the most severely affected countries. METHODOLOGY: We applied Bayesian geostatistical negative binomial models to analyze reported incidence data of cutaneous and visceral leishmaniasis in Brazil covering a 10-year period (2001-2010. Particular emphasis was placed on spatial and temporal patterns. The models were fitted using integrated nested Laplace approximations to perform fast approximate Bayesian inference. Bayesian variable selection was employed to determine the most important climatic, environmental, and socioeconomic predictors of cutaneous and visceral leishmaniasis. PRINCIPAL FINDINGS: For both types of leishmaniasis, precipitation and socioeconomic proxies were identified as important risk factors. The predicted number of cases in 2010 were 30,189 (standard deviation [SD]: 7,676 for cutaneous leishmaniasis and 4,889 (SD: 288 for visceral leishmaniasis. Our risk maps predicted the highest numbers of infected people in the states of Minas Gerais and Pará for visceral and cutaneous leishmaniasis, respectively. CONCLUSIONS/SIGNIFICANCE: Our spatially explicit, high-resolution incidence maps identified priority areas where leishmaniasis control efforts should be targeted with the ultimate goal to reduce disease incidence.

  16. A Classification for a Geostatistical Index of Spatial Dependence

    Directory of Open Access Journals (Sweden)

    Enio Júnior Seidel

    Full Text Available ABSTRACT: In geostatistical studies, spatial dependence can generally be described by means of the semivariogram or, in complementary form, with a single index followed by its categorization to classify the degree of such dependence. The objective of this study was to construct a categorization for the spatial dependence index (SDI proposed by Seidel and Oliveira (2014 in order to classify spatial variability in terms of weak, moderate, and strong dependence. Theoretical values were constructed from different degrees of spatial dependence, which served as a basis for calculation of the SDI. In view of the form of distribution and SDI descriptive measures, we developed a categorization for posterior classification of spatial dependence, specific to each semivariogram model. The SDI categorization was based on its median and 3rd quartile, allowing us to classify spatial dependence as weak, moderate, or strong. We established that for the spherical semivariogram: SDISpherical (% ≤ 7 % (weak spatial dependence, 7 % 15 % (strong spatial dependence; for the exponential semivariogram: SDIExponential (% ≤ 6 % (weak spatial dependence, 6 % 13 % (strong spatial dependence; and for the Gaussian semivariogram: SDIGaussian (% ≤ 9 % (weak spatial dependence, 9 % 20 % (strong spatial dependence. The proposed categorization allows the user to transform the numerical values calculated for SDI into categories of variability of spatial dependence, with adequate power for explanation and comparison.

  17. Geostatistical Study of Precipitation on the Island of Crete

    Science.gov (United States)

    Agou, Vasiliki D.; Varouchakis, Emmanouil A.; Hristopulos, Dionissios T.

    2015-04-01

    precipitation which are fitted locally to a three-parameter probability distribution, based on which a normalized index is derived. We use the Spartan variogram function to model space-time correlations, because it is more flexible than classical models [3]. The performance of the variogram model is tested by means of leave-one-out cross validation. The variogram model is then used in connection with ordinary kriging to generate precipitation maps for the entire island. In the future, we will explore the joint spatiotemporal evolution of precipitation patterns on Crete. References [1] P. Goovaerts. Geostatistical approaches for incorporating elevation into the spatial interpolation of precipitation. Journal of Hydrology, 228(1):113-129, 2000. [2] N. B. Guttman. Accepting the standardized precipitation index: a calculation algorithm. American Water Resource Association, 35(2):311-322, 1999. [3] D. T Hristopulos. Spartan Gibbs random field models for geostatistical applications. SIAM Journal on Scientific Computing, 24(6):2125-2162, 2003. [4] A.G. Koutroulis, A.-E.K. Vrohidou, and I.K. Tsanis. Spatiotemporal characteristics of meteorological drought for the island of Crete. Journal of Hydrometeorology, 12(2):206-226, 2011. [5] T. B. McKee, N. J. Doesken, and J. Kleist. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, page 179-184, Anaheim, California, 1993.

  18. Unsupervised classification of multivariate geostatistical data: Two algorithms

    Science.gov (United States)

    Romary, Thomas; Ors, Fabien; Rivoirard, Jacques; Deraisme, Jacques

    2015-12-01

    With the increasing development of remote sensing platforms and the evolution of sampling facilities in mining and oil industry, spatial datasets are becoming increasingly large, inform a growing number of variables and cover wider and wider areas. Therefore, it is often necessary to split the domain of study to account for radically different behaviors of the natural phenomenon over the domain and to simplify the subsequent modeling step. The definition of these areas can be seen as a problem of unsupervised classification, or clustering, where we try to divide the domain into homogeneous domains with respect to the values taken by the variables in hand. The application of classical clustering methods, designed for independent observations, does not ensure the spatial coherence of the resulting classes. Image segmentation methods, based on e.g. Markov random fields, are not adapted to irregularly sampled data. Other existing approaches, based on mixtures of Gaussian random functions estimated via the expectation-maximization algorithm, are limited to reasonable sample sizes and a small number of variables. In this work, we propose two algorithms based on adaptations of classical algorithms to multivariate geostatistical data. Both algorithms are model free and can handle large volumes of multivariate, irregularly spaced data. The first one proceeds by agglomerative hierarchical clustering. The spatial coherence is ensured by a proximity condition imposed for two clusters to merge. This proximity condition relies on a graph organizing the data in the coordinates space. The hierarchical algorithm can then be seen as a graph-partitioning algorithm. Following this interpretation, a spatial version of the spectral clustering algorithm is also proposed. The performances of both algorithms are assessed on toy examples and a mining dataset.

  19. Bayesian geostatistical modeling of Malaria Indicator Survey data in Angola.

    Directory of Open Access Journals (Sweden)

    Laura Gosoniu

    Full Text Available The 2006-2007 Angola Malaria Indicator Survey (AMIS is the first nationally representative household survey in the country assessing coverage of the key malaria control interventions and measuring malaria-related burden among children under 5 years of age. In this paper, the Angolan MIS data were analyzed to produce the first smooth map of parasitaemia prevalence based on contemporary nationwide empirical data in the country. Bayesian geostatistical models were fitted to assess the effect of interventions after adjusting for environmental, climatic and socio-economic factors. Non-linear relationships between parasitaemia risk and environmental predictors were modeled by categorizing the covariates and by employing two non-parametric approaches, the B-splines and the P-splines. The results of the model validation showed that the categorical model was able to better capture the relationship between parasitaemia prevalence and the environmental factors. Model fit and prediction were handled within a Bayesian framework using Markov chain Monte Carlo (MCMC simulations. Combining estimates of parasitaemia prevalence with the number of children under we obtained estimates of the number of infected children in the country. The population-adjusted prevalence ranges from in Namibe province to in Malanje province. The odds of parasitaemia in children living in a household with at least ITNs per person was by 41% lower (CI: 14%, 60% than in those with fewer ITNs. The estimates of the number of parasitaemic children produced in this paper are important for planning and implementing malaria control interventions and for monitoring the impact of prevention and control activities.

  20. Patch-based iterative conditional geostatistical simulation using graph cuts

    Science.gov (United States)

    Li, Xue; Mariethoz, Gregoire; Lu, DeTang; Linde, Niklas

    2016-08-01

    Training image-based geostatistical methods are increasingly popular in groundwater hydrology even if existing algorithms present limitations that often make real-world applications difficult. These limitations include a computational cost that can be prohibitive for high-resolution 3-D applications, the presence of visual artifacts in the model realizations, and a low variability between model realizations due to the limited pool of patterns available in a finite-size training image. In this paper, we address these issues by proposing an iterative patch-based algorithm which adapts a graph cuts methodology that is widely used in computer graphics. Our adapted graph cuts method optimally cuts patches of pixel values borrowed from the training image and assembles them successively, each time accounting for the information of previously stitched patches. The initial simulation result might display artifacts, which are identified as regions of high cost. These artifacts are reduced by iteratively placing new patches in high-cost regions. In contrast to most patch-based algorithms, the proposed scheme can also efficiently address point conditioning. An advantage of the method is that the cut process results in the creation of new patterns that are not present in the training image, thereby increasing pattern variability. To quantify this effect, a new measure of variability is developed, the merging index, quantifies the pattern variability in the realizations with respect to the training image. A series of sensitivity analyses demonstrates the stability of the proposed graph cuts approach, which produces satisfying simulations for a wide range of parameters values. Applications to 2-D and 3-D cases are compared to state-of-the-art multiple-point methods. The results show that the proposed approach obtains significant speedups and increases variability between realizations. Connectivity functions applied to 2-D models transport simulations in 3-D models are used to

  1. A Bayesian geostatistical approach for evaluating the uncertainty of contaminant mass discharges from point sources

    DEFF Research Database (Denmark)

    Troldborg, Mads; Nowak, Wolfgang; Binning, Philip John

    compared to existing methods that are either too simple or computationally demanding. The method is based on conditional geostatistical simulation and accounts for i) heterogeneity of both the flow field and the concentration distribution through Bayesian geostatistics, ii) measurement uncertainty, and iii...... a multilevel control plane. The method decouples the flow and transport simulation and has the advantage of avoiding the heavy computational burden of three-dimensional numerical flow and transport simulation coupled with geostatistical inversion. It may therefore be of practical relevance to practitioners......) uncertain source zone and transport parameters. The method generates multiple equally likely realisations of the spatial flow and concentration distribution, which all honour the measured data at the control plane. The flow realisations are generated by co-simulating the hydraulic conductivity...

  2. [Spatial distribution pattern of Chilo suppressalis analyzed by classical method and geostatistics].

    Science.gov (United States)

    Yuan, Zheming; Fu, Wei; Li, Fangyi

    2004-04-01

    Two original samples of Chilo suppressalis and their grid, random and sequence samples were analyzed by classical method and geostatistics to characterize the spatial distribution pattern of C. suppressalis. The limitations of spatial distribution analysis with classical method, especially influenced by the original position of grid, were summarized rather completely. On the contrary, geostatistics characterized well the spatial distribution pattern, congregation intensity and spatial heterogeneity of C. suppressalis. According to geostatistics, the population was up to Poisson distribution in low density. As for higher density population, its distribution was up to aggregative, and the aggregation intensity and dependence range were 0.1056 and 193 cm, respectively. Spatial heterogeneity was also found in the higher density population. Its spatial correlativity in line direction was more closely than that in row direction, and the dependence ranges in line and row direction were 115 and 264 cm, respectively.

  3. Use of geostatistics for remediation planning to transcend urban political boundaries.

    Science.gov (United States)

    Milillo, Tammy M; Sinha, Gaurav; Gardella, Joseph A

    2012-11-01

    Soil remediation plans are often dictated by areas of jurisdiction or property lines instead of scientific information. This study exemplifies how geostatistically interpolated surfaces can substantially improve remediation planning. Ordinary kriging, ordinary co-kriging, and inverse distance weighting spatial interpolation methods were compared for analyzing surface and sub-surface soil sample data originally collected by the US EPA and researchers at the University at Buffalo in Hickory Woods, an industrial-residential neighborhood in Buffalo, NY, where both lead and arsenic contamination is present. Past clean-up efforts estimated contamination levels from point samples, but parcel and agency jurisdiction boundaries were used to define remediation sites, rather than geostatistical models estimating the spatial behavior of the contaminants in the soil. Residents were understandably dissatisfied with the arbitrariness of the remediation plan. In this study we show how geostatistical mapping and participatory assessment can make soil remediation scientifically defensible, socially acceptable, and economically feasible.

  4. Evaluation of spatial variability of metal bioavailability in soils using geostatistics

    DEFF Research Database (Denmark)

    Owsianiak, Mikolaj; Hauschild, Michael Zwicky; Rosenbaum, Ralph K.

    2012-01-01

    for different soils. Here, variography is employed to analyse spatial variability of bioavailability factors (BFs) of metals at the global scale. First, published empirical regressions are employed to calculate BFs of metals for 7180 topsoil profiles. Next, geostatistical interpretation of calculated BFs...... is performed using ArcGIS Geostatistical Analyst. Results show that BFs of copper span a range of 6 orders of magnitude, and have signifficant spatial variability at local and continental scales. The model nugget variance is signifficantly higher than zero, suggesting the presence of spatial variability...... at lags smaller than those in the data set. Geostatistical analyses indicate however, that BFs exhibit no signifficant spatial correlation at a range beyond 3200 km. Because BF is spatially correlated, its values at unsampled locations can be predicted, as demonstrated using ordinary kriggin method...

  5. TiConverter: A training image converting tool for multiple-point geostatistics

    Science.gov (United States)

    Fadlelmula F., Mohamed M.; Killough, John; Fraim, Michael

    2016-11-01

    TiConverter is a tool developed to ease the application of multiple-point geostatistics whether by the open source Stanford Geostatistical Modeling Software (SGeMS) or other available commercial software. TiConverter has a user-friendly interface and it allows the conversion of 2D training images into numerical representations in four different file formats without the need for additional code writing. These are the ASCII (.txt), the geostatistical software library (GSLIB) (.txt), the Isatis (.dat), and the VTK formats. It performs the conversion based on the RGB color system. In addition, TiConverter offers several useful tools including image resizing, smoothing, and segmenting tools. The purpose of this study is to introduce the TiConverter, and to demonstrate its application and advantages with several examples from the literature.

  6. Geostatistical Prediction of Ocean Outfall Plume Characteristics Based on an Autonomous Underwater Vehicle

    Directory of Open Access Journals (Sweden)

    Patrícia Alexandra Gregório Ramos

    2013-07-01

    Full Text Available Geostatistics has been successfully used to analyse and characterize the spatial variability of environmental properties. Besides providing estimated values at unsampled locations, geostatistics measures the accuracy of the estimate, which is a significant advantage over traditional methods used to assess pollution. This work uses universal block kriging to model and map the spatial distribution of salinity measurements gathered by an Autonomous Underwater Vehicle in a sea outfall monitoring campaign. The aim is to distinguish the effluent plume from the receiving waters, characterizing its spatial variability in the vicinity of the discharge and estimating dilution. The results demonstrate that geostatistical methodology can provide good estimates of the dispersion of effluents, which are valuable in assessing the environmental impact and managing sea outfalls. Moreover, since accurate measurements of the plume’s dilution are rare, these studies may be very helpful in the future to validate dispersion models.

  7. Application of Bayesian geostatistics for evaluation of mass discharge uncertainty at contaminated sites

    DEFF Research Database (Denmark)

    Troldborg, Mads; Nowak, Wolfgang; Lange, Ida Vedel

    2012-01-01

    . Here a geostatistical simulation method for quantifying the uncertainty of the mass discharge across a multilevel control plane is presented. The method accounts for (1) heterogeneity of both the flow field and the concentration distribution through Bayesian geostatistics, (2) measurement uncertainty......-Cox transformed concentration data is used to simulate observed deviations from this mean solution. By combining the flow and concentration realizations, a mass discharge probability distribution is obtained. The method has the advantage of avoiding the heavy computational burden of three-dimensional numerical...

  8. Methodology and applications in non-linear model-based geostatistics

    DEFF Research Database (Denmark)

    Christensen, Ole Fredslund

    Today geostatistics is used in a number of research areas, among others agricultural and environmental sciences.This thesis concerns data and applications where the classical Gaussian spatial model is not appropriate. A transformation could be used in an attempt to obtain data that are approximat......Today geostatistics is used in a number of research areas, among others agricultural and environmental sciences.This thesis concerns data and applications where the classical Gaussian spatial model is not appropriate. A transformation could be used in an attempt to obtain data...

  9. Geostatistical simulation of geological architecture and uncertainty propagation in groundwater modeling

    DEFF Research Database (Denmark)

    He, Xiulan

    parameters and model structures, which are the primary focuses of this PhD research. Parameter uncertainty was analyzed using an optimization tool (PEST: Parameter ESTimation) in combination with a random sampling method (LHS: Latin Hypercube Sampling). Model structure, namely geological architecture...... was analyzed using both a traditional two-point based geostatistical approach and multiple-point geostatistics (MPS). Our results documented that model structure is as important as model parameter regarding groundwater modeling uncertainty. Under certain circumstances the inaccuracy on model structure can...

  10. INTRODUCTION OF SPATIAL INTERPOLATION METHODS IN GEOSTATISTICS ANALYST%Geostatistics Analyst中空间内插方法的介绍

    Institute of Scientific and Technical Information of China (English)

    秦涛

    2005-01-01

    主流GIS软件ArcGIS 9的Geostatistics Analyst模块中所涉及的两大类空间内插方法:确定性内插方法和地统计内插方法.结合该软件对各种插值方法的应用和处理进行了介绍,应用示例比较各种内插方法的适用范围.

  11. Characterizing subsurface textural properties using electromagnetic induction mapping and geostatistics

    Science.gov (United States)

    Abdu, Hiruy

    Knowledge of the spatial distribution of soil textural properties at the watershed scale is important for understanding spatial patterns of water movement, and in determining soil moisture storage and soil hydraulic transport properties. Capturing the heterogeneous nature of the subsurface without exhaustive and costly sampling presents a significant challenge. Soil scientists and geologists have adapted geophysical methods that measure a surrogate property related to the vital underlying process. Apparent electrical conductivity (ECa) is such a proxy, providing a measure of charge mobility due to application of an electric field, and is highly correlated to the electrical conductivity of the soil solution, clay percentage, and water content. Electromagnetic induction (EMI) provides the possibility of obtaining high resolution images of ECa across a landscape to identify subtle changes in subsurface properties. The aim of this study was to better characterize subsurface textural properties using EMI mapping and geostatistical analysis techniques. The effect of variable temperature environments on EMI instrumental response, and EC a -- depth relationship were first determined. Then a procedure of repeated EMI mapping at varying soil water content was developed and integrated with temporal stability analysis to capture the time invariant properties of spatial soil texture on an agricultural field. In addition, an EMI imaging approach of densely sampling the subsurface of the Reynolds Mountain East watershed was presented using kriging to interpolate, and Sequential Gaussian Simulation to estimate the uncertainty in the maps. Due to the relative time-invariant characteristics of textural properties, it was possible to correlate clay samples collected over three seasons to ECa data of one mapping event. Kriging methods [ordinary kriging (OK), cokriging (CK), and regression kriging (RK)] were then used to integrate various levels of information (clay percentage, ECa

  12. Confronting uncertainty in model-based geostatistics using Markov Chain Monte Carlo simulation

    NARCIS (Netherlands)

    Minasny, B.; Vrugt, J.A.; McBratney, A.B.

    2011-01-01

    This paper demonstrates for the first time the use of Markov Chain Monte Carlo (MCMC) simulation for parameter inference in model-based soil geostatistics. We implemented the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm to jointly summarize the posterior distributi

  13. The use of geostatistics in the study of floral phenology of Vulpia geniculata (L.) link.

    Science.gov (United States)

    León Ruiz, Eduardo J; García Mozo, Herminia; Domínguez Vilches, Eugenio; Galán, Carmen

    2012-01-01

    Traditionally phenology studies have been focused on changes through time, but there exist many instances in ecological research where it is necessary to interpolate among spatially stratified samples. The combined use of Geographical Information Systems (GIS) and Geostatistics can be an essential tool for spatial analysis in phenological studies. Geostatistics are a family of statistics that describe correlations through space/time and they can be used for both quantifying spatial correlation and interpolating unsampled points. In the present work, estimations based upon Geostatistics and GIS mapping have enabled the construction of spatial models that reflect phenological evolution of Vulpia geniculata (L.) Link throughout the study area during sampling season. Ten sampling points, scattered throughout the city and low mountains in the "Sierra de Córdoba" were chosen to carry out the weekly phenological monitoring during flowering season. The phenological data were interpolated by applying the traditional geostatitical method of Kriging, which was used to elaborate weekly estimations of V. geniculata phenology in unsampled areas. Finally, the application of Geostatistics and GIS to create phenological maps could be an essential complement in pollen aerobiological studies, given the increased interest in obtaining automatic aerobiological forecasting maps.

  14. Adapting geostatistics to analyze spatial and temporal trends in weed populations

    Science.gov (United States)

    Geostatistics were originally developed in mining to estimate the location, abundance and quality of ore over large areas from soil samples to optimize future mining efforts. Here, some of these methods were adapted to weeds to account for a limited distribution area (i.e., inside a field), variatio...

  15. Combining Geostatistics with Moran’s I Analysis for Mapping Soil Heavy Metals in Beijing, China

    Directory of Open Access Journals (Sweden)

    Bao-Guo Li

    2012-03-01

    Full Text Available Production of high quality interpolation maps of heavy metals is important for risk assessment of environmental pollution. In this paper, the spatial correlation characteristics information obtained from Moran’s I analysis was used to supplement the traditional geostatistics. According to Moran’s I analysis, four characteristics distances were obtained and used as the active lag distance to calculate the semivariance. Validation of the optimality of semivariance demonstrated that using the two distances where the Moran’s I and the standardized Moran’s I, Z(I reached a maximum as the active lag distance can improve the fitting accuracy of semivariance. Then, spatial interpolation was produced based on the two distances and their nested model. The comparative analysis of estimation accuracy and the measured and predicted pollution status showed that the method combining geostatistics with Moran’s I analysis was better than traditional geostatistics. Thus, Moran’s I analysis is a useful complement for geostatistics to improve the spatial interpolation accuracy of heavy metals.

  16. Incorporating temporal variability to improve geostatistical analysis of satellite-observed CO2 in China

    Institute of Scientific and Technical Information of China (English)

    ZENG ZhaoCheng; LEI LiPing; GUO LiJie; ZHANG Li; ZHANG Bing

    2013-01-01

    Observations of atmospheric carbon dioxide (CO2) from satellites offer new data sources to understand global carbon cycling.The correlation structure of satellite-observed CO2 can be analyzed and modeled by geostatistical methods,and CO2 values at unsampled locations can be predicted with a correlation model.Conventional geostatistical analysis only investigates the spatial correlation of CO2,and does not consider temporal variation in the satellite-observed CO2 data.In this paper,a spatiotemporal geostatistical method that incorporates temporal variability is implemented and assessed for analyzing the spatiotemporal correlation structure and prediction of monthly CO2 in China.The spatiotemporal correlation is estimated and modeled by a product-sum variogram model with a global nugget component.The variogram result indicates a significant degree of temporal correlation within satellite-observed CO2 data sets in China.Prediction of monthly CO2 using the spatiotemporal variogram model and spacetime kriging procedure is implemented.The prediction is compared with a spatial-only geostatistical prediction approach using a cross-validation technique.The spatiotemporal approach gives better results,with higher correlation coefficient (r2),and less mean absolute prediction error and root mean square error.Moreover,the monthly mapping result generated from the spatiotemporal approach has less prediction uncertainty and more detailed spatial variation of CO2 than those from the spatial-only approach.

  17. Geostatistical description of geological heterogeneity in clayey till as input for improved characterization of contaminated sites

    DEFF Research Database (Denmark)

    Kessler, Timo Christian; Klint, K.E.S.; Renard, P.;

    2010-01-01

    at a clay till outcrop in Denmark to characterise the shapes and the spatial variability. Further, geostatistics were applied to simulate the distribution and to develop a heterogeneity model that can be incorporated into an existing geological model of, for example, a contaminated site....

  18. The Use of Geostatistics in the Study of Floral Phenology of Vulpia geniculata (L. Link

    Directory of Open Access Journals (Sweden)

    Eduardo J. León Ruiz

    2012-01-01

    Full Text Available Traditionally phenology studies have been focused on changes through time, but there exist many instances in ecological research where it is necessary to interpolate among spatially stratified samples. The combined use of Geographical Information Systems (GIS and Geostatistics can be an essential tool for spatial analysis in phenological studies. Geostatistics are a family of statistics that describe correlations through space/time and they can be used for both quantifying spatial correlation and interpolating unsampled points. In the present work, estimations based upon Geostatistics and GIS mapping have enabled the construction of spatial models that reflect phenological evolution of Vulpia geniculata (L. Link throughout the study area during sampling season. Ten sampling points, scattered troughout the city and low mountains in the “Sierra de Córdoba” were chosen to carry out the weekly phenological monitoring during flowering season. The phenological data were interpolated by applying the traditional geostatitical method of Kriging, which was used to ellaborate weekly estimations of V. geniculata phenology in unsampled areas. Finally, the application of Geostatistics and GIS to create phenological maps could be an essential complement in pollen aerobiological studies, given the increased interest in obtaining automatic aerobiological forecasting maps.

  19. Quantifying natural delta variability using a multiple-point geostatistics prior uncertainty model

    Science.gov (United States)

    Scheidt, Céline; Fernandes, Anjali M.; Paola, Chris; Caers, Jef

    2016-10-01

    We address the question of quantifying uncertainty associated with autogenic pattern variability in a channelized transport system by means of a modern geostatistical method. This question has considerable relevance for practical subsurface applications as well, particularly those related to uncertainty quantification relying on Bayesian approaches. Specifically, we show how the autogenic variability in a laboratory experiment can be represented and reproduced by a multiple-point geostatistical prior uncertainty model. The latter geostatistical method requires selection of a limited set of training images from which a possibly infinite set of geostatistical model realizations, mimicking the training image patterns, can be generated. To that end, we investigate two methods to determine how many training images and what training images should be provided to reproduce natural autogenic variability. The first method relies on distance-based clustering of overhead snapshots of the experiment; the second method relies on a rate of change quantification by means of a computer vision algorithm termed the demon algorithm. We show quantitatively that with either training image selection method, we can statistically reproduce the natural variability of the delta formed in the experiment. In addition, we study the nature of the patterns represented in the set of training images as a representation of the "eigenpatterns" of the natural system. The eigenpattern in the training image sets display patterns consistent with previous physical interpretations of the fundamental modes of this type of delta system: a highly channelized, incisional mode; a poorly channelized, depositional mode; and an intermediate mode between the two.

  20. Use of geostatistics for detailed mine planning. Geoestadistica aplicada a la planificacion minera de detalle

    Energy Technology Data Exchange (ETDEWEB)

    Fuente Martin, P.; Gonzalez Marroquin, V.M.; Fernandez de Castro Fernandez Sahw, F.; Saez Garcia, E. (HUNOSA, Oviedo (Spain))

    1989-06-01

    The aim of this project, which has been financed by Ocicarbon, is to develop both in theory and in practice, the use of geostatistics to predict the geological behaviour of coal seams, in virgin panels, using data from panels already worked. Examples of seams selected for full mechanisation are given. 3 figs., 3 tabs.

  1. Introduction to This Special Issue on Geostatistics and Geospatial Techniques in Remote Sensing

    Science.gov (United States)

    Atkinson, Peter; Quattrochi, Dale A.; Goodman, H. Michael (Technical Monitor)

    2000-01-01

    The germination of this special Computers & Geosciences (C&G) issue began at the Royal Geographical Society (with the Institute of British Geographers) (RGS-IBG) annual meeting in January 1997 held at the University of Exeter, UK. The snow and cold of the English winter were tempered greatly by warm and cordial discussion of how to stimulate and enhance cooperation on geostatistical and geospatial research in remote sensing 'across the big pond' between UK and US researchers. It was decided that one way forward would be to hold parallel sessions in 1998 on geostatistical and geospatial research in remote sensing at appropriate venues in both the UK and the US. Selected papers given at these sessions would be published as special issues of C&G on the UK side and Photogrammetric Engineering and Remote Sensing (PE&RS) on the US side. These issues would highlight the commonality in research on geostatistical and geospatial research in remote sensing on both sides of the Atlantic Ocean. As a consequence, a session on "Geostatistics and Geospatial Techniques for Remote Sensing of Land Surface Processes" was held at the RGS-IBG annual meeting in Guildford, Surrey, UK in January 1998, organized by the Modeling and Advanced Techniques Special Interest Group (MAT SIG) of the Remote Sensing Society (RSS). A similar session was held at the Association of American Geographers (AAG) annual meeting in Boston, Massachusetts in March 1998, sponsored by the AAG's Remote Sensing Specialty Group (RSSG). The 10 papers that make up this issue of C&G, comprise 7 papers from the UK and 3 papers from the LIS. We are both co-editors of each of the journal special issues, with the lead editor of each journal issue being from their respective side of the Atlantic. The special issue of PE&RS (vol. 65) that constitutes the other half of this co-edited journal series was published in early 1999, comprising 6 papers by US authors. We are indebted to the International Association for Mathematical

  2. Reducing spatial uncertainty in climatic maps through geostatistical analysis

    Science.gov (United States)

    Pesquer, Lluís; Ninyerola, Miquel; Pons, Xavier

    2014-05-01

    ), applying different interpolation methods/parameters are shown: RMS (mm) error values obtained from the independent test set (20 % of the samples) follow, according to this order: IDW (exponent=1.5, 2, 2.5, 3) / SPT (tension=100, 125, 150, 175, 200) / OK. LOOCV: 92.5; 80.2; 74.2; 72.3 / 181.6; 90.6; 75.7; 71.1; 69.4; 68.8 RS: 101.2; 89.6; 83.9; 81.9 / 115.1; 92.4; 84.0; 81.4; 80.9; 81.1 / 81.1 EU: 57.4; 51.3; 53.1; 55.5 / 59.1; 57.1; 55.9; 55.0; 54.3 / 51.8 A3D: 48.3; 49.8; 52.5; 62.2 / 57.1; 54.4; 52.5; 51.2; 50.2 / 49.7 To study these results, a geostatistical analysis of uncertainty has been done. Main results: variogram analysis of the error (using the test set) shows that the total sill is reduced (50% EU, 60% A3D) when using the two new approaches, while the spatialized standard deviation model calculated from the OK shows significantly lower values when compared to the RS. In conclusion, A3D and EU highly improve LOOCV and RS, whereas A3D slightly improves EU. Also, LOOCV only shows slightly better results than RS, suggesting that non-random-split increases the power of both fitting-test steps. * Ninyerola, Pons, Roure. A methodological approach of climatological modelling of air temperature and precipitation through GIS techniques. IJC, 2000; 20:1823-1841.

  3. Monte Carlo full-waveform inversion of crosshole GPR data using multiple-point geostatistical a priori information

    DEFF Research Database (Denmark)

    Cordua, Knud Skou; Hansen, Thomas Mejer; Mosegaard, Klaus

    2012-01-01

    We present a general Monte Carlo full-waveform inversion strategy that integrates a priori information described by geostatistical algorithms with Bayesian inverse problem theory. The extended Metropolis algorithm can be used to sample the a posteriori probability density of highly nonlinear......) Based on a posteriori realizations, complicated statistical questions can be answered, such as the probability of connectivity across a layer. (3) Complex a priori information can be included through geostatistical algorithms. These benefits, however, require more computing resources than traditional...

  4. SRS 2010 Vegetation Inventory GeoStatistical Mapping Results for Custom Reaction Intensity and Total Dead Fuels.

    Energy Technology Data Exchange (ETDEWEB)

    Edwards, Lloyd A. [Leading Solutions, LLC.; Paresol, Bernard [U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland, OR.

    2014-09-01

    This report of the geostatistical analysis results of the fire fuels response variables, custom reaction intensity and total dead fuels is but a part of an SRS 2010 vegetation inventory project. For detailed description of project, theory and background including sample design, methods, and results please refer to USDA Forest Service Savannah River Site internal report “SRS 2010 Vegetation Inventory GeoStatistical Mapping Report”, (Edwards & Parresol 2013).

  5. Geochemical and geostatistical evaluation, Arkansas Canyon Planning Unit, Fremont and Custer Counties, Colorado

    Science.gov (United States)

    Weiland, E.F.; Connors, R.A.; Robinson, M.L.; Lindemann, J.W.; Meyer, W.T.

    1982-01-01

    A mineral assessment of the Arkansas Canyon Planning Unit was undertaken by Barringer Resources Inc., under the terms of contract YA-553-CTO-100 with the Bureau of Land Management, Colorado State Office. The study was based on a geochemical-geostatistical survey in which 700 stream sediment samples were collected and analyzed for 25 elements. Geochemical results were interpreted by statistical processing which included factor, discriminant, multiple regression and characteristic analysis. The major deposit types evaluated were massive sulfide-base metal, sedimentary and magmatic uranium, thorium vein, magmatic segregation, and carbonatite related deposits. Results of the single element data and multivariate geostatistical analysis indicate that limited potential exists for base metal mineralization near the Horseshoe, El Plomo, and Green Mountain Mines. Thirty areas are considered to be anomalous with regard to one or more of the geochemical parameters evaluated during this study. The evaluation of carbonatite related mineralization was restricted due to the lack of geochemical data specific to this environment.

  6. Geostatistical Spatio-Time model of crime in el Salvador: Structural and Predictive Analysis

    Directory of Open Access Journals (Sweden)

    Welman Rosa Alvarado

    2011-07-01

    Full Text Available Today, to study a geospatial and spatio-temporal phenomena requires searching statistical tools that enable the analysis of the dependency of space, time and interactions. The science that studies this kind of subjects is the Geoestatics which the goal is to predict spatial phenomenon. This science is considered the base for modeling phenomena that involves interactions between space and time. In the past 10 years, the Geostatistic had seen a great development in areas like the geology, soils, remote sensing, epidemiology, agriculture, ecology, economy, etc. In this research, the geostatistic had been apply to build a predictive map about crime in El Salvador; for that the variability of space and time together is studied to generate crime scenarios: crime hot spots are determined, crime vulnerable groups are identified, to improve political decisions and facilitate to decision makers about the insecurity in the country.

  7. Geostatistical simulations for radon indoor with a nested model including the housing factor.

    Science.gov (United States)

    Cafaro, C; Giovani, C; Garavaglia, M

    2016-01-01

    The radon prone areas definition is matter of many researches in radioecology, since radon is considered a leading cause of lung tumours, therefore the authorities ask for support to develop an appropriate sanitary prevention strategy. In this paper, we use geostatistical tools to elaborate a definition accounting for some of the available information about the dwellings. Co-kriging is the proper interpolator used in geostatistics to refine the predictions by using external covariates. In advance, co-kriging is not guaranteed to improve significantly the results obtained by applying the common lognormal kriging. Here, instead, such multivariate approach leads to reduce the cross-validation residual variance to an extent which is deemed as satisfying. Furthermore, with the application of Monte Carlo simulations, the paradigm provides a more conservative radon prone areas definition than the one previously made by lognormal kriging.

  8. Simultaneous inversion of petrophysical parameters based on geostatistical a priori information

    Institute of Scientific and Technical Information of China (English)

    Yin Xing-Yao; Sun Rui-Ying; Wang Bao-Li; Zhang Guang-Zhi

    2014-01-01

    The high-resolution nonlinear simultaneous inversion of petrophysical parameters is based on Bayesian statistics and combines petrophysics with geostatistical a priori information. We used the fast Fourier transform-moving average (FFT-MA) and gradual deformation method (GDM) to obtain a reasonable variogram by using structural analysis and geostatistical a priori information of petrophysical parameters. Subsequently, we constructed the likelihood function according to the statistical petrophysical model. Finally, we used the Metropolis algorithm to sample the posteriori probability density and complete the inversion of the petrophysical parameters. We used the proposed method to process data from an oil fi eld in China and found good match between inversion and real data with high-resolution. In addition, the direct inversion of petrophysical parameters avoids the error accumulation and decreases the uncertainty, and increases the computational effi ciency.

  9. Geostatistics for spatial genetic structures: study of wild populations of perennial ryegrass.

    Science.gov (United States)

    Monestiez, P; Goulard, M; Charmet, G

    1994-04-01

    Methods based on geostatistics were applied to quantitative traits of agricultural interest measured on a collection of 547 wild populations of perennial ryegrass in France. The mathematical background of these methods, which resembles spatial autocorrelation analysis, is briefly described. When a single variable is studied, the spatial structure analysis is similar to spatial autocorrelation analysis, and a spatial prediction method, called "kriging", gives a filtered map of the spatial pattern over all the sampled area. When complex interactions of agronomic traits with different evaluation sites define a multivariate structure for the spatial analysis, geostatistical methods allow the spatial variations to be broken down into two main spatial structures with ranges of 120 km and 300 km, respectively. The predicted maps that corresponded to each range were interpreted as a result of the isolation-by-distance model and as a consequence of selection by environmental factors. Practical collecting methodology for breeders may be derived from such spatial structures.

  10. Geostatistical analysis and kriging of Hexachlorocyclohexane residues in topsoil from Tianjin, China

    Energy Technology Data Exchange (ETDEWEB)

    Li, B.G. [College of Environmental Sciences, MOE Laboratory for Earth Surface Processes, Peking University, Beijing 100871 (China); Cao, J. [College of Environmental Sciences, MOE Laboratory for Earth Surface Processes, Peking University, Beijing 100871 (China); Liu, W.X. [College of Environmental Sciences, MOE Laboratory for Earth Surface Processes, Peking University, Beijing 100871 (China); Shen, W.R. [Environmental Protection Bureau, Tianjin 300191 (China); Wang, X.J. [College of Environmental Sciences, MOE Laboratory for Earth Surface Processes, Peking University, Beijing 100871 (China); Tao, S. [College of Environmental Sciences, MOE Laboratory for Earth Surface Processes, Peking University, Beijing 100871 (China)]. E-mail: taos@urban.pku.edu.cn

    2006-08-15

    A previously published data set of HCH isomer concentrations in topsoil samples from Tianjin, China, was subjected to geospatial analysis. Semivariograms were calculated and modeled using geostatistical techniques. Parameters of semivariogram models were analyzed and compared for four HCH isomers. Two-dimensional ordinary block kriging was applied to HCH isomers data set for mapping purposes. Dot maps and gray-scaled raster maps of HCH concentrations were presented based on kriging results. The appropriateness of the kriging procedure for mapping purposes was evaluated based on the kriging errors and kriging variances. It was found that ordinary block kriging can be applied to interpolate HCH concentrations in Tianjin topsoil with acceptable accuracy for mapping purposes. - Geostatistical analysis and kriging were applied to HCH concentrations in topsoil of Tianjin, China for mapping purposes.

  11. Integrating Address Geocoding, Land Use Regression, and Spatiotemporal Geostatistical Estimation for Groundwater Tetrachloroethylene

    OpenAIRE

    Messier, Kyle P.; Akita, Yasuyuki; Serre, Marc L.

    2012-01-01

    Geographic Information Systems (GIS) based techniques are cost-effective and efficient methods used by state agencies and epidemiology researchers for estimating concentration and exposure. However, budget limitations have made statewide assessments of contamination difficult, especially in groundwater media. Many studies have implemented address geocoding, land use regression, and geostatistics independently, but this is the first to examine the benefits of integrating these GIS techniques t...

  12. Multivariate analysis and geostatistics of the fertility of a humic rhodic hapludox under coffee cultivation

    Directory of Open Access Journals (Sweden)

    Samuel de Assis Silva

    2012-04-01

    Full Text Available The spatial variability of soil and plant properties exerts great influence on the yeld of agricultural crops. This study analyzed the spatial variability of the fertility of a Humic Rhodic Hapludox with Arabic coffee, using principal component analysis, cluster analysis and geostatistics in combination. The experiment was carried out in an area under Coffea arabica L., variety Catucai 20/15 - 479. The soil was sampled at a depth 0.20 m, at 50 points of a sampling grid. The following chemical properties were determined: P, K+, Ca2+, Mg2+, Na+, S, Al3+, pH, H + Al, SB, t, T, V, m, OM, Na saturation index (SSI, remaining phosphorus (P-rem, and micronutrients (Zn, Fe, Mn, Cu and B. The data were analyzed with descriptive statistics, followed by principal component and cluster analyses. Geostatistics were used to check and quantify the degree of spatial dependence of properties, represented by principal components. The principal component analysis allowed a dimensional reduction of the problem, providing interpretable components, with little information loss. Despite the characteristic information loss of principal component analysis, the combination of this technique with geostatistical analysis was efficient for the quantification and determination of the structure of spatial dependence of soil fertility. In general, the availability of soil mineral nutrients was low and the levels of acidity and exchangeable Al were high.

  13. A conceptual sedimentological-geostatistical model of aquifer heterogeneity based on outcrop studies

    Energy Technology Data Exchange (ETDEWEB)

    Davis, J.M.

    1994-01-01

    Three outcrop studies were conducted in deposits of different depositional environments. At each site, permeability measurements were obtained with an air-minipermeameter developed as part of this study. In addition, the geological units were mapped with either surveying, photographs, or both. Geostatistical analysis of the permeability data was performed to estimate the characteristics of the probability distribution function and the spatial correlation structure. The information obtained from the geological mapping was then compared with the results of the geostatistical analysis for any relationships that may exist. The main field site was located in the Albuquerque Basin of central New Mexico at an outcrop of the Pliocene-Pleistocene Sierra Ladrones Formation. The second study was conducted on the walls of waste pits in alluvial fan deposits at the Nevada Test Site. The third study was conducted on an outcrop of an eolian deposit (miocene) south of Socorro, New Mexico. The results of the three studies were then used to construct a conceptual model relating depositional environment to geostatistical models of heterogeneity. The model presented is largely qualitative but provides a basis for further hypothesis formulation and testing.

  14. A geostatistical methodology to assess the accuracy of unsaturated flow models

    Energy Technology Data Exchange (ETDEWEB)

    Smoot, J.L.; Williams, R.E.

    1996-04-01

    The Pacific Northwest National Laboratory spatiotemporal movement of water injected into (PNNL) has developed a Hydrologic unsaturated sediments at the Hanford Site in Evaluation Methodology (HEM) to assist the Washington State was used to develop a new U.S. Nuclear Regulatory Commission in method for evaluating mathematical model evaluating the potential that infiltrating meteoric predictions. Measured water content data were water will produce leachate at commercial low- interpolated geostatistically to a 16 x 16 x 36 level radioactive waste disposal sites. Two key grid at several time intervals. Then a issues are raised in the HEM: (1) evaluation of mathematical model was used to predict water mathematical models that predict facility content at the same grid locations at the selected performance, and (2) estimation of the times. Node-by-node comparison of the uncertainty associated with these mathematical mathematical model predictions with the model predictions. The technical objective of geostatistically interpolated values was this research is to adapt geostatistical tools conducted. The method facilitates a complete commonly used for model parameter estimation accounting and categorization of model error at to the problem of estimating the spatial every node. The comparison suggests that distribution of the dependent variable to be model results generally are within measurement calculated by the model. To fulfill this error. The worst model error occurs in silt objective, a database describing the lenses and is in excess of measurement error.

  15. Geostatistical integration and uncertainty in pollutant concentration surface under preferential sampling

    Directory of Open Access Journals (Sweden)

    Laura Grisotto

    2016-04-01

    Full Text Available In this paper the focus is on environmental statistics, with the aim of estimating the concentration surface and related uncertainty of an air pollutant. We used air quality data recorded by a network of monitoring stations within a Bayesian framework to overcome difficulties in accounting for prediction uncertainty and to integrate information provided by deterministic models based on emissions meteorology and chemico-physical characteristics of the atmosphere. Several authors have proposed such integration, but all the proposed approaches rely on representativeness and completeness of existing air pollution monitoring networks. We considered the situation in which the spatial process of interest and the sampling locations are not independent. This is known in the literature as the preferential sampling problem, which if ignored in the analysis, can bias geostatistical inferences. We developed a Bayesian geostatistical model to account for preferential sampling with the main interest in statistical integration and uncertainty. We used PM10 data arising from the air quality network of the Environmental Protection Agency of Lombardy Region (Italy and numerical outputs from the deterministic model. We specified an inhomogeneous Poisson process for the sampling locations intensities and a shared spatial random component model for the dependence between the spatial location of monitors and the pollution surface. We found greater predicted standard deviation differences in areas not properly covered by the air quality network. In conclusion, in this context inferences on prediction uncertainty may be misleading when geostatistical modelling does not take into account preferential sampling.

  16. Geostatistical integration and uncertainty in pollutant concentration surface under preferential sampling.

    Science.gov (United States)

    Grisotto, Laura; Consonni, Dario; Cecconi, Lorenzo; Catelan, Dolores; Lagazio, Corrado; Bertazzi, Pier Alberto; Baccini, Michela; Biggeri, Annibale

    2016-04-18

    In this paper the focus is on environmental statistics, with the aim of estimating the concentration surface and related uncertainty of an air pollutant. We used air quality data recorded by a network of monitoring stations within a Bayesian framework to overcome difficulties in accounting for prediction uncertainty and to integrate information provided by deterministic models based on emissions meteorology and chemico-physical characteristics of the atmosphere. Several authors have proposed such integration, but all the proposed approaches rely on representativeness and completeness of existing air pollution monitoring networks. We considered the situation in which the spatial process of interest and the sampling locations are not independent. This is known in the literature as the preferential sampling problem, which if ignored in the analysis, can bias geostatistical inferences. We developed a Bayesian geostatistical model to account for preferential sampling with the main interest in statistical integration and uncertainty. We used PM10 data arising from the air quality network of the Environmental Protection Agency of Lombardy Region (Italy) and numerical outputs from the deterministic model. We specified an inhomogeneous Poisson process for the sampling locations intensities and a shared spatial random component model for the dependence between the spatial location of monitors and the pollution surface. We found greater predicted standard deviation differences in areas not properly covered by the air quality network. In conclusion, in this context inferences on prediction uncertainty may be misleading when geostatistical modelling does not take into account preferential sampling.

  17. Integration of GIS, Geostatistics, and 3-D Technology to Assess the Spatial Distribution of Soil Moisture

    Science.gov (United States)

    Betts, M.; Tsegaye, T.; Tadesse, W.; Coleman, T. L.; Fahsi, A.

    1998-01-01

    The spatial and temporal distribution of near surface soil moisture is of fundamental importance to many physical, biological, biogeochemical, and hydrological processes. However, knowledge of these space-time dynamics and the processes which control them remains unclear. The integration of geographic information systems (GIS) and geostatistics together promise a simple mechanism to evaluate and display the spatial and temporal distribution of this vital hydrologic and physical variable. Therefore, this research demonstrates the use of geostatistics and GIS to predict and display soil moisture distribution under vegetated and non-vegetated plots. The research was conducted at the Winfred Thomas Agricultural Experiment Station (WTAES), Hazel Green, Alabama. Soil moisture measurement were done on a 10 by 10 m grid from tall fescue grass (GR), alfalfa (AA), bare rough (BR), and bare smooth (BS) plots. Results indicated that variance associated with soil moisture was higher for vegetated plots than non-vegetated plots. The presence of vegetation in general contributed to the spatial variability of soil moisture. Integration of geostatistics and GIS can improve the productivity of farm lands and the precision of farming.

  18. Spatial Downscaling of TRMM Precipitation Using Geostatistics and Fine Scale Environmental Variables

    Directory of Open Access Journals (Sweden)

    No-Wook Park

    2013-01-01

    Full Text Available A geostatistical downscaling scheme is presented and can generate fine scale precipitation information from coarse scale Tropical Rainfall Measuring Mission (TRMM data by incorporating auxiliary fine scale environmental variables. Within the geostatistical framework, the TRMM precipitation data are first decomposed into trend and residual components. Quantitative relationships between coarse scale TRMM data and environmental variables are then estimated via regression analysis and used to derive trend components at a fine scale. Next, the residual components, which are the differences between the trend components and the original TRMM data, are then downscaled at a target fine scale via area-to-point kriging. The trend and residual components are finally added to generate fine scale precipitation estimates. Stochastic simulation is also applied to the residual components in order to generate multiple alternative realizations and to compute uncertainty measures. From an experiment using a digital elevation model (DEM and normalized difference vegetation index (NDVI, the geostatistical downscaling scheme generated the downscaling results that reflected detailed characteristics with better predictive performance, when compared with downscaling without the environmental variables. Multiple realizations and uncertainty measures from simulation also provided useful information for interpretations and further environmental modeling.

  19. Technology demonstration: geostatistical and hydrologic analysis of salt areas. Assessment of effectiveness of geologic isolation systems

    Energy Technology Data Exchange (ETDEWEB)

    Doctor, P.G.; Oberlander, P.L.; Rice, W.A.; Devary, J.L.; Nelson, R.W.; Tucker, P.E.

    1982-09-01

    The Office of Nuclear Waste Isolation (ONWI) requested Pacific Northwest Laboratory (PNL) to: (1) use geostatistical analyses to evaluate the adequacy of hydrologic data from three salt regions, each of which contains a potential nuclear waste repository site; and (2) demonstrate a methodology that allows quantification of the value of additional data collection. The three regions examined are the Paradox Basin in Utah, the Permian Basin in Texas, and the Mississippi Study Area. Additional and new data became available to ONWI during and following these analyses; therefore, this report must be considered a methodology demonstration here would apply as illustrated had the complete data sets been available. A combination of geostatistical and hydrologic analyses was used for this demonstration. Geostatistical analyses provided an optimal estimate of the potentiometric surface from the available data, a measure of the uncertainty of that estimate, and a means for selecting and evaluating the location of future data. The hydrologic analyses included the calculation of transmissivities, flow paths, travel times, and ground-water flow rates from hypothetical repository sites. Simulation techniques were used to evaluate the effect of optimally located future data on the potentiometric surface, flow lines, travel times, and flow rates. Data availability, quality, quantity, and conformance with model assumptions differed in each of the salt areas. Report highlights for the three locations are given.

  20. A space and time scale-dependent nonlinear geostatistical approach for downscaling daily precipitation and temperature

    KAUST Repository

    Jha, Sanjeev Kumar

    2015-07-21

    A geostatistical framework is proposed to downscale daily precipitation and temperature. The methodology is based on multiple-point geostatistics (MPS), where a multivariate training image is used to represent the spatial relationship between daily precipitation and daily temperature over several years. Here, the training image consists of daily rainfall and temperature outputs from the Weather Research and Forecasting (WRF) model at 50 km and 10 km resolution for a twenty year period ranging from 1985 to 2004. The data are used to predict downscaled climate variables for the year 2005. The result, for each downscaled pixel, is daily time series of precipitation and temperature that are spatially dependent. Comparison of predicted precipitation and temperature against a reference dataset indicates that both the seasonal average climate response together with the temporal variability are well reproduced. The explicit inclusion of time dependence is explored by considering the climate properties of the previous day as an additional variable. Comparison of simulations with and without inclusion of time dependence shows that the temporal dependence only slightly improves the daily prediction because the temporal variability is already well represented in the conditioning data. Overall, the study shows that the multiple-point geostatistics approach is an efficient tool to be used for statistical downscaling to obtain local scale estimates of precipitation and temperature from General Circulation Models. This article is protected by copyright. All rights reserved.

  1. Demonstration and Validation of the Geostatistical Temporal-Spatial Algorithm (GTS) for Optimization of Long-Term Monitoring (LTM) of Groundwater at Military and Government Sites

    Science.gov (United States)

    2010-08-01

    Validation of the Geostatistical Temporal-Spatial Algorithm (GTS) for Optimization of Long-Term Monitoring (LTM) of Groundwater at Military and... Geostatistical Temporal-Spatial Algorithm (GTS) for Optimization of Long-Term Monitoring (LTM) of Groundwater at Military and Government Sites 5a. CONTRACT NUMBER...ABSTRACT The primary objective of this ESTCP project was to demonstrate and validate use of the Geostatistical Temporal-Spatial (GTS) groundwater

  2. Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

    Directory of Open Access Journals (Sweden)

    Ute C. Herzfeld

    2016-07-01

    Full Text Available Recent drastic reductions in the Arctic sea-ice cover have raised an interest in understanding the role of sea ice in the global system as well as pointed out a need to understand the physical processes that lead to such changes. Satellite remote-sensing data provide important information about remote ice areas, and Synthetic Aperture Radar (SAR data have the advantages of penetration of the omnipresent cloud cover and of high spatial resolution. A challenge addressed in this paper is how to extract information on sea-ice types and sea-ice processes from SAR data. We introduce, validate and apply geostatistical and statistical approaches to automated classification of sea ice from SAR data, to be used as individual tools for mapping sea-ice properties and provinces or in combination. A key concept of the geostatistical classification method is the analysis of spatial surface structures and their anisotropies, more generally, of spatial surface roughness, at variable, intermediate-sized scales. The geostatistical approach utilizes vario parameters extracted from directional vario functions, the parameters can be mapped or combined into feature vectors for classification. The method is flexible with respect to window sizes and parameter types and detects anisotropies. In two applications to RADARSAT and ERS-2 SAR data from the area near Point Barrow, Alaska, it is demonstrated that vario-parameter maps may be utilized to distinguish regions of different sea-ice characteristics in the Beaufort Sea, the Chukchi Sea and in Elson Lagoon. In a third and a fourth case study the analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification. Field measurements and high-resolution aerial observations serve as basis for validation of the geostatistical-statistical classification methods. A combination of supervised classification and vario-parameter mapping yields best results

  3. Estimation of extreme daily precipitation: comparison between regional and geostatistical approaches.

    Science.gov (United States)

    Hellies, Matteo; Deidda, Roberto; Langousis, Andreas

    2016-04-01

    We study the extreme rainfall regime of the Island of Sardinia in Italy, based on annual maxima of daily precipitation. The statistical analysis is conducted using 229 daily rainfall records with at least 50 complete years of observations, collected at different sites by the Hydrological Survey of the Sardinia Region. Preliminary analysis, and the L-skewness and L-kurtosis diagrams, show that the Generalized Extreme Value (GEV) distribution model performs best in describing daily rainfall extremes. The GEV distribution parameters are estimated using the method of Probability Weighted Moments (PWM). To obtain extreme rainfall estimates at ungauged sites, while minimizing uncertainties due to sampling variability, a regional and a geostatistical approach are compared. The regional approach merges information from different gauged sites, within homogeneous regions, to obtain GEV parameter estimates at ungauged locations. The geostatistical approach infers the parameters of the GEV distribution model at locations where measurements are available, and then spatially interpolates them over the study region. In both approaches we use local rainfall means as index-rainfall. In the regional approach we define homogeneous regions by applying a hierarchical cluster analysis based on Ward's method, with L-moment ratios (i.e. L-CV and L-Skewness) as metrics. The analysis results in four contiguous regions, which satisfy the Hosking and Wallis (1997) homogeneity tests. The latter have been conducted using a Monte-Carlo approach based on a 4-parameter Kappa distribution model, fitted to each station cluster. Note that the 4-parameter Kappa model includes the GEV distribution as a sub-case, when the fourth parameter h is set to 0. In the geostatistical approach we apply kriging for uncertain data (KUD), which accounts for the error variance in local parameter estimation and, therefore, may serve as a useful tool for spatial interpolation of metrics affected by high uncertainty. In

  4. Conditioning geostatistical simulations of a bedrock fluvial aquifer using single well pumping tests

    Science.gov (United States)

    Niazi, A.; Bentley, L. R.; Hayashi, M.

    2015-12-01

    Geostatistical simulation is a powerful tool to explore the uncertainty associated with heterogeneity in groundwater and reservoir studies. Nonetheless, conditioning simulations merely with lithological information does not utilize all of the available information and so some workers additionally condition simulations with flow data. In this study, we introduce an approach to condition geostatistical simulations of the Paskapoo Formation, which is a paleo-fluvial system consisting of sandstone channels embedded in mudstone. The conditioning data consist of two-hour single well pumping tests extracted from the public water well database in Alberta, Canada. In this approach, lithologic models of an entire watershed are simulated and conditioned with hard lithological data using transition probability geostatistics (TPROGS). Then, a segment of the simulation around a pumping well was used to populate a flow model (FEFLOW) with either sand or mudstone. The values of the hydraulic conductivity and specific storage of sand and mudstone were then adjusted to minimize the difference between simulated and actual pumping test data using the parameter estimation program PEST. If the simulated data do not adequately match the measured data, the lithologic model is updated by locally deforming the lithology distribution using the probability perturbation method (PPM) and the model parameters are again updated with PEST. This procedure is repeated until the simulated and measured data agree within a pre-determined tolerance. The procedure is repeated for each pumping well that has pumping test data. The method constrains the lithological simulations and provides estimates of hydraulic conductivity and specific storage that are consistent with the pumping test data. Eventually, the simulations will be combined in watershed scale groundwater models.

  5. Characterizing Geothermal Surface Manifestation Based on Multivariate Geostatistics of Ground Measurements Data

    Science.gov (United States)

    Ishaq; Nur Heriawan, Mohamad; Saepuloh, Asep

    2016-09-01

    Mt. Wayang Windu is one of geothermal field located in West Java, Indonesia. The characterization of steam spots at surface manifestation zones based on the soil physical measurements of the area is presented in this study. The multivariate geostatistical methods incorporating the soil physical parameter data were used to characterize the zonation of geothermal surface manifestations. The purpose of this study is to evaluate the performance of spatial estimation method of multivariate geostatistics using Ordinary Cokriging (COK) to characterize the physical properties of geothermal surface manifestations at Mt. Wayang Windu. The COK method was selected because this method is favorable when the secondary variables has more number than the primary variables. There are four soil physical parameters used as the basis of COK method, i.e. Electrical Conductivity, Susceptibility, pH, and Temperature. The parameters were measured directly at and around geothermal surface manifestations including hot springs, fumaroles, and craters. Each location of surface manifestations was measured about 30 points with 30 x 30 m grids. The measurement results were analyzed by descriptive statistics to identify at the nature of data. The correlation among variables was analyzed using linear regression. When the correlation coefficient among variables is higher, the estimation results is expected to have better Linear Coregionalization Model (LCM). LCM was used to analyze the spatial correlation of each variable based on their variogram and cross-variogram model. In oder to evaluate the performance of multivariate geostatistical using COK method, a Root Mean Square Error (RMSE) was performed. Estimation result using COK method is well applicable for characterizing the surface physics parameters of radar images data.

  6. Can Geostatistical Models Represent Nature's Variability? An Analysis Using Flume Experiments

    Science.gov (United States)

    Scheidt, C.; Fernandes, A. M.; Paola, C.; Caers, J.

    2015-12-01

    The lack of understanding in the Earth's geological and physical processes governing sediment deposition render subsurface modeling subject to large uncertainty. Geostatistics is often used to model uncertainty because of its capability to stochastically generate spatially varying realizations of the subsurface. These methods can generate a range of realizations of a given pattern - but how representative are these of the full natural variability? And how can we identify the minimum set of images that represent this natural variability? Here we use this minimum set to define the geostatistical prior model: a set of training images that represent the range of patterns generated by autogenic variability in the sedimentary environment under study. The proper definition of the prior model is essential in capturing the variability of the depositional patterns. This work starts with a set of overhead images from an experimental basin that showed ongoing autogenic variability. We use the images to analyze the essential characteristics of this suite of patterns. In particular, our goal is to define a prior model (a minimal set of selected training images) such that geostatistical algorithms, when applied to this set, can reproduce the full measured variability. A necessary prerequisite is to define a measure of variability. In this study, we measure variability using a dissimilarity distance between the images. The distance indicates whether two snapshots contain similar depositional patterns. To reproduce the variability in the images, we apply an MPS algorithm to the set of selected snapshots of the sedimentary basin that serve as training images. The training images are chosen from among the initial set by using the distance measure to ensure that only dissimilar images are chosen. Preliminary investigations show that MPS can reproduce fairly accurately the natural variability of the experimental depositional system. Furthermore, the selected training images provide

  7. Geostatistical techniques applied to mapping limnological variables and quantify the uncertainty associated with estimates

    Directory of Open Access Journals (Sweden)

    Cristiano Cigagna

    2015-12-01

    Full Text Available Abstract Aim: This study aimed to map the concentrations of limnological variables in a reservoir employing semivariogram geostatistical techniques and Kriging estimates for unsampled locations, as well as the uncertainty calculation associated with the estimates. Methods: We established twenty-seven points distributed in a regular mesh for sampling. Then it was determined the concentrations of chlorophyll-a, total nitrogen and total phosphorus. Subsequently, a spatial variability analysis was performed and the semivariogram function was modeled for all variables and the variographic mathematical models were established. The main geostatistical estimation technique was the ordinary Kriging. The work was developed with the estimate of a heavy grid points for each variables that formed the basis of the interpolated maps. Results: Through the semivariogram analysis was possible to identify the random component as not significant for the estimation process of chlorophyll-a, and as significant for total nitrogen and total phosphorus. Geostatistical maps were produced from the Kriging for each variable and the respective standard deviations of the estimates calculated. These measurements allowed us to map the concentrations of limnological variables throughout the reservoir. The calculation of standard deviations provided the quality of the estimates and, consequently, the reliability of the final product. Conclusions: The use of the Kriging statistical technique to estimate heavy mesh points associated with the error dispersion (standard deviation of the estimate, made it possible to make quality and reliable maps of the estimated variables. Concentrations of limnological variables in general were higher in the lacustrine zone and decreased towards the riverine zone. The chlorophyll-a and total nitrogen correlated comparing the grid generated by Kriging. Although the use of Kriging is more laborious compared to other interpolation methods, this

  8. A geostatistical approach to estimate mining efficiency indicators with flexible meshes

    Science.gov (United States)

    Freixas, Genis; Garriga, David; Fernàndez-Garcia, Daniel; Sanchez-Vila, Xavier

    2014-05-01

    Geostatistics is a branch of statistics developed originally to predict probability distributions of ore grades for mining operations by considering the attributes of a geological formation at unknown locations as a set of correlated random variables. Mining exploitations typically aim to maintain acceptable mineral laws to produce commercial products based upon demand. In this context, we present a new geostatistical methodology to estimate strategic efficiency maps that incorporate hydraulic test data, the evolution of concentrations with time obtained from chemical analysis (packer tests and production wells) as well as hydraulic head variations. The methodology is applied to a salt basin in South America. The exploitation is based on the extraction of brines through vertical and horizontal wells. Thereafter, brines are precipitated in evaporation ponds to obtain target potassium and magnesium salts of economic interest. Lithium carbonate is obtained as a byproduct of the production of potassium chloride. Aside from providing an assemble of traditional geostatistical methods, the strength of this study falls with the new methodology developed, which focus on finding the best sites to exploit the brines while maintaining efficiency criteria. Thus, some strategic indicator efficiency maps have been developed under the specific criteria imposed by exploitation standards to incorporate new extraction wells in new areas that would allow maintain or improve production. Results show that the uncertainty quantification of the efficiency plays a dominant role and that the use flexible meshes, which properly describe the curvilinear features associated with vertical stratification, provides a more consistent estimation of the geological processes. Moreover, we demonstrate that the vertical correlation structure at the given salt basin is essentially linked to variations in the formation thickness, which calls for flexible meshes and non-stationarity stochastic processes.

  9. Modeling spatial variability of sand-lenses in clay till settings using transition probability and multiple-point geostatistics

    DEFF Research Database (Denmark)

    Kessler, Timo Christian; Nilsson, Bertel; Klint, Knud Erik;

    2010-01-01

    the geology of e.g. a contaminated site, it is not always possible to gather enough information to build a representative geological model. Mapping in analogue geological settings and applying geostatistical tools to simulate spatial variability of heterogeneities can improve ordinary geological models...... that are predicated only on vertical borehole information. This study documents methods to map geological heterogeneity in clay till and ways to calibrate geostatistical models with field observations. A well-exposed cross-section in an excavation pit was used to measure and illustrate the occurrence and distribution...... of sand-lenses in clay till. Sand-lenses mainly account for horizontal transport and are prioritised in this study. Based on field observations, the distribution has been modeled using two different geostatistical approaches. One method uses a Markov chain model calculating the transition probabilities...

  10. Evaluation of geostatistical parameters based on well tests; Estimation de parametres geostatistiques a partir de tests de puits

    Energy Technology Data Exchange (ETDEWEB)

    Gauthier, Y.

    1997-10-20

    Geostatistical tools are increasingly used to model permeability fields in subsurface reservoirs, which are considered as a particular random variable development depending of several geostatistical parameters such as variance and correlation length. The first part of the thesis is devoted to the study of relations existing between the transient well pressure (the well test) and the stochastic permeability field, using the apparent permeability concept.The well test performs a moving permeability average over larger and larger volume with increasing time. In the second part, the geostatistical parameters are evaluated using well test data; a Bayesian framework is used and parameters are estimated using the maximum likelihood principle by maximizing the well test data probability density function with respect to these parameters. This method, involving a well test fast evaluation, provides an estimation of the correlation length and the variance over different realizations of a two-dimensional permeability field

  11. Geostatistical analysis of the flood risk perception queries in the village of Navaluenga (Central Spain)

    Science.gov (United States)

    Guardiola-Albert, Carolina; Díez-Herrero, Andrés; Amérigo, María; García, Juan Antonio; María Bodoque, José; Fernández-Naranjo, Nuria

    2017-04-01

    Flash floods provoke a high average mortality as they are usually unexpected events which evolve rapidly and affect relatively small areas. The short time available for minimizing risks requires preparedness and response actions to be put into practice. Therefore, it is necessary the development of emergency response plans to evacuate and rescue people in the context of a flash-flood hazard. In this framework, risk management has to integrate the social dimension of flash-flooding and its spatial distribution by understanding the characteristics of local communities in order to enhance community resilience during a flash-flood. In this regard, the flash-flood social risk perception of the village of Navaluenga (Central Spain) has been recently assessed, as well as the level of awareness of civil protection and emergency management strategies (Bodoque et al., 2016). This has been done interviewing 254 adults, representing roughly 12% of the population census. The present study wants to go further in the analysis of the resulting questionnaires, incorporating in the analysis the location of home spatial coordinates in order to characterize the spatial distribution and possible geographical interpretation of flood risk perception. We apply geostatistical methods to analyze spatial relations of social risk perception and level of awareness with distance to the rivers (Alberche and Chorrerón) or to the flood-prone areas (50-year, 100-year and 500-year flood plains). We want to discover spatial patterns, if any, using correlation functions (variograms). Geostatistical analyses results can help to either confirm the logical pattern (i.e., less awareness further to the rivers or high return period of flooding) or reveal departures from expected. It can also be possible to identify hot spots, cold spots, and spatial outliers. The interpretation of these spatial patterns can give valuable information to define strategies to improve the awareness regarding preparedness and

  12. Image smoothing of multispectral imagery based on the HNN and geo-statistics

    Institute of Scientific and Technical Information of China (English)

    Nguyen Quang Minh

    2011-01-01

    A new method for image down-scaling using geostatistical interpolation or smoothing based on the Hopfield Neural Network (HNN) and zero semivariance value is introduced.The method utilises the smoothing effect of the semivariogram matching process to produce the smoothened sub-pixel multispectral (MS) image with smaller RMSEs in comparison with the bilinear interpolation.In fact,the zero semivariograms increase the spatial correlation between the adjacent sub-pixels of the superresolution image.Containing higher spatial correlation,the resulting super-resolution MS image has smaller RMSEs compared with the original coarse image.

  13. Geostatistical analysis of soil properties at field scale using standardized data

    Science.gov (United States)

    Millan, H.; Tarquis, A. M.; Pérez, L. D.; Matos, J.; González-Posada, M.

    2012-04-01

    Indentifying areas with physical degradation is a crucial step to ameliorate the effects in soil erosion. The quantification and interpretation of spatial variability is a key issue for site-specific soil management. Geostatistics has been the main methodological tool for implementing precision agriculture using field data collected at different spatial resolutions. Even though many works have made significant contributions to the body of knowledge on spatial statistics and its applications, some other key points need to be addressed for conducting precise comparisons between soil properties using geostatistical parameters. The objectives of the present work were (i) to quantify the spatial structure of different physical properties collected from a Vertisol, (ii) to search for potential correlations between different spatial patterns and (iii) to identify relevant components through multivariate spatial analysis. The study was conducted on a Vertisol (Typic Hapludert) dedicated to sugarcane (Saccharum officinarum L.) production during the last sixty years. We used six soil properties collected from a squared grid (225 points) (penetrometer resistance (PR), total porosity, fragmentation dimension (Df), vertical electrical conductivity (ECv), horizontal electrical conductivity (ECh) and soil water content (WC)). All the original data sets were z-transformed before geostatistical analysis. Three different types of semivariogram models were necessary for fitting individual experimental semivariograms. This suggests the different natures of spatial variability patterns. Soil water content rendered the largest nugget effect (C0 = 0.933) while soil total porosity showed the largest range of spatial correlation (A = 43.92 m). The bivariate geostatistical analysis also rendered significant cross-semivariance between different paired soil properties. However, four different semivariogram models were required in that case. This indicates an underlying co

  14. Geostatistical approaches to interpolation and classification of remote-sensing data from ice surfaces

    Science.gov (United States)

    Herzfeld, Ute Christina; Mayer, Helmut; Higginson, Chris A.; Matassa, Michael

    1996-01-01

    Geostatistical methods for interpolation and extrapolation techniques are used in glaciological data analysis. The results of a program involving the mapping of the Antarctica from satellite radar altimeter data are discussed. A combination of high and low resolution techniques was applied in the analysis of the Bering Glacier (Alaska). The global positioning system (GPS) located video data collected from small aircraft and the ERS-1 synthetic aperture radar (SAR) images were used. From the perspective of SAR data analysis, the Bering Glacier surge was the opportunity to characterize the surface of fast flowing ice and the rapid changes in the surface roughness.

  15. 3D Geostatistical Modeling and Uncertainty Analysis in a Carbonate Reservoir, SW Iran

    Directory of Open Access Journals (Sweden)

    Mohammad Reza Kamali

    2013-01-01

    Full Text Available The aim of geostatistical reservoir characterization is to utilize wide variety of data, in different scales and accuracies, to construct reservoir models which are able to represent geological heterogeneities and also quantifying uncertainties by producing numbers of equiprobable models. Since all geostatistical methods used in estimation of reservoir parameters are inaccurate, modeling of “estimation error” in form of uncertainty analysis is very important. In this paper, the definition of Sequential Gaussian Simulation has been reviewed and construction of stochastic models based on it has been discussed. Subsequently ranking and uncertainty quantification of those stochastically populated equiprobable models and sensitivity study of modeled properties have been presented. Consequently, the application of sensitivity analysis on stochastic models of reservoir horizons, petrophysical properties, and stochastic oil-water contacts, also their effect on reserve, clearly shows any alteration in the reservoir geometry has significant effect on the oil in place. The studied reservoir is located at carbonate sequences of Sarvak Formation, Zagros, Iran; it comprises three layers. The first one which is located beneath the cap rock contains the largest portion of the reserve and other layers just hold little oil. Simulations show that average porosity and water saturation of the reservoir is about 20% and 52%, respectively.

  16. Sensitivity analysis of geostatistical approach to recover pollution source release history in groundwater

    Science.gov (United States)

    Long, Y. Q.; Cui, T. T.; Li, W.; Yang, Z. P.; Gai, Y. W.

    2017-08-01

    The geostatistical approach has been studied for many year to identify the pollution source re-lease history in groundwater. We focus on the influence of observation error and hydraulic parameters on the groundwater pollution identification (PSI) result in the paper. Numerical experiment and sensitivity analysis are carried out to find the influence of observation point configuration, error and hydraulic parameters on the PSI result in a 1D homogeneous aquifer. It has been found out that if concentration observation data could accurately describe the characteristics of the real concentration plume at the observed time point, a nice identification of the pollution release process could be obtained. If the calculated pollution discharge process has good similarity with the real discharge process, the order of the observation error fell within 10-6 and 10-3.5, the dispersion coefficient varies fells within -10% and 5%, and the actual mean velocity fell within ±2%. The actual mean velocity is the most sensitive parameter of the geostatistical approach in this case.

  17. Geostatistical improvements of evapotranspiration spatial information using satellite land surface and weather stations data

    Science.gov (United States)

    de Carvalho Alves, Marcelo; de Carvalho, Luiz Gonsaga; Vianello, Rubens Leite; Sediyama, Gilberto C.; de Oliveira, Marcelo Silva; de Sá Junior, Arionaldo

    2013-07-01

    The objective of the present study was to use the simple cokriging methodology to characterize the spatial variability of Penman-Monteith reference evapotranspiration and Thornthwaite potential evapotranspiration methods based on Moderate Resolution Imaging Spetroradiometer (MODIS) global evapotranspiration products and high-resolution surfaces of WordClim temperature and precipitation data. The climatic element data referred to 39 National Institute of Meteorology climatic stations located in Minas Gerais state, Brazil and surrounding states. The use of geostatistics and simple cokriging technique enabled the characterization of the spatial variability of the evapotranspiration providing uncertainty information on the spatial prediction pattern. Evapotranspiration and precipitation surfaces were implemented for the climatic classification in Minas Gerais. Multivariate geostatistical determined improvements of evapotranspiration spatial information. The regions in the south of Minas Gerais derived from the moisture index estimated with the MODIS evapotranspiration (2000-2010), presented divergence of humid conditions when compared to the moisture index derived from the simple kriged and cokriged evapotranspiration (1961-1990), indicating climate change in this region. There was stronger pattern of crossed covariance between evapotranspiration and precipitation rather than temperature, indicating that trends in precipitation could be one of the main external drivers of the evapotranspiration in Minas Gerais state, Brazil.

  18. Characterization of groundwater quality using water evaluation indices, multivariate statistics and geostatistics in central Bangladesh

    Directory of Open Access Journals (Sweden)

    Md. Bodrud-Doza

    2016-04-01

    Full Text Available This study investigates the groundwater quality in the Faridpur district of central Bangladesh based on preselected 60 sample points. Water evaluation indices and a number of statistical approaches such as multivariate statistics and geostatistics are applied to characterize water quality, which is a major factor for controlling the groundwater quality in term of drinking purposes. The study reveal that EC, TDS, Ca2+, total As and Fe values of groundwater samples exceeded Bangladesh and international standards. Ground water quality index (GWQI exhibited that about 47% of the samples were belonging to good quality water for drinking purposes. The heavy metal pollution index (HPI, degree of contamination (Cd, heavy metal evaluation index (HEI reveal that most of the samples belong to low level of pollution. However, Cd provide better alternative than other indices. Principle component analysis (PCA suggests that groundwater quality is mainly related to geogenic (rock–water interaction and anthropogenic source (agrogenic and domestic sewage in the study area. Subsequently, the findings of cluster analysis (CA and correlation matrix (CM are also consistent with the PCA results. The spatial distributions of groundwater quality parameters are determined by geostatistical modeling. The exponential semivariagram model is validated as the best fitted models for most of the indices values. It is expected that outcomes of the study will provide insights for decision makers taking proper measures for groundwater quality management in central Bangladesh.

  19. Spatially explicit Schistosoma infection risk in eastern Africa using Bayesian geostatistical modelling

    DEFF Research Database (Denmark)

    Schur, Nadine; Hürlimann, Eveline; Stensgaard, Anna-Sofie

    2013-01-01

    Schistosomiasis remains one of the most prevalent parasitic diseases in the tropics and subtropics, but current statistics are outdated due to demographic and ecological transformations and ongoing control efforts. Reliable risk estimates are important to plan and evaluate interventions in a spat......Schistosomiasis remains one of the most prevalent parasitic diseases in the tropics and subtropics, but current statistics are outdated due to demographic and ecological transformations and ongoing control efforts. Reliable risk estimates are important to plan and evaluate interventions...... in a spatially explicit and cost-effective manner. We analysed a large ensemble of georeferenced survey data derived from an open-access neglected tropical diseases database to create smooth empirical prevalence maps for Schistosoma mansoni and Schistosoma haematobium for a total of 13 countries of eastern...... Africa. Bayesian geostatistical models based on climatic and other environmental data were used to account for potential spatial clustering in spatially structured exposures. Geostatistical variable selection was employed to reduce the set of covariates. Alignment factors were implemented to combine...

  20. geoCount: An R Package for the Analysis of Geostatistical Count Data

    Directory of Open Access Journals (Sweden)

    Liang Jing

    2015-02-01

    Full Text Available We describe the R package geoCount for the analysis of geostatistical count data. The package performs Bayesian analysis for the Poisson-lognormal and binomial-logitnormal spatial models, which are subclasses of the class of generalized linear spatial models proposed by Diggle, Tawn, and Moyeed (1998. The package implements the computational intensive tasks in C++ using an R/C++ interface, and has parallel computation capabilities to speed up the computations. geoCount also implements group updating, Langevin- Hastings algorithms and a data-based parameterization, algorithmic approaches proposed by Christensen, Roberts, and Sko ?ld (2006 to improve the efficiency of the Markov chain Monte Carlo algorithms. In addition, the package includes functions for simulation and visualization, as well as three geostatistical count datasets taken from the literature. One of those is used to illustrate the package capabilities. Finally, we provide a side-by-side comparison between geoCount and the R packages geoRglm and INLA.

  1. Evaluation of statistical and geostatistical models of digital soil properties mapping in tropical mountain regions

    Directory of Open Access Journals (Sweden)

    Waldir de Carvalho Junior

    2014-06-01

    Full Text Available Soil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR and geostatistical (ordinary kriging and co-kriging. The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap. Continuous covariates and categorical predictors were used and their contributions to the model assessed. Only the environmental covariates elevation, aspect, stream power index (SPI, soil wetness index (SWI, normalized difference vegetation index (NDVI, and b3/b2 band ratio were significantly correlated with soil properties. The predictive models had a mean coefficient of determination of 0.21. Best results were obtained with the geostatistical predictive models, where the highest coefficient of determination 0.43 was associated with sand properties between 60 to 100 cm deep. The use of a sparse data set of soil properties for digital mapping can explain only part of the spatial variation of these properties. The results may be related to the sampling density and the quantity and quality of the environmental covariates and predictive models used.

  2. Geostatistics: a common link between medical geography, mathematical geology, and medical geology.

    Science.gov (United States)

    Goovaerts, P

    2014-08-01

    Since its development in the mining industry, geostatistics has emerged as the primary tool for spatial data analysis in various fields, ranging from earth and atmospheric sciences to agriculture, soil science, remote sensing, and more recently environmental exposure assessment. In the last few years, these tools have been tailored to the field of medical geography or spatial epidemiology, which is concerned with the study of spatial patterns of disease incidence and mortality and the identification of potential 'causes' of disease, such as environmental exposure, diet and unhealthy behaviours, economic or socio-demographic factors. On the other hand, medical geology is an emerging interdisciplinary scientific field studying the relationship between natural geological factors and their effects on human and animal health. This paper provides an introduction to the field of medical geology with an overview of geostatistical methods available for the analysis of geological and health data. Key concepts are illustrated using the mapping of groundwater arsenic concentration across eleven Michigan counties and the exploration of its relationship to the incidence of prostate cancer at the township level.

  3. Geostatistical Analysis on the Temporal Patterns of the Yellow Rice Borer, Tryporyza incertulas

    Institute of Scientific and Technical Information of China (English)

    YUAN Zhe-ming; WANG Zhi; HU Xiang-yue

    2005-01-01

    In order to comprehend temporal pattern of the larvae population of the yellow rice borer, Tryporyza incertulas, and provide valuable information for its forecast model, the data series of the population for each generation and the over-wintered larvae from 1960 to 1990 in Dingcheng District, Changde City, Hunan Province, were analyzed with geostatistics. The data series of total number,the 1 st generation, the 3rd generation and the over-wintered larvae year to year displayed rather better autocorrelation and prediction.The data series of generation to generation, the 2nd generation and the 4th generation year to year, however, demonstrated poor autocorrelation, especially for the 4th generation, whose autocorrelation degree was zero. The population dynamics of the yellow rice borer was obviously intermittent. A remarkable cycle of four generations, one year, was observed in the population of generation to generation. Omitting the certain generation or interposing the over-wintered larvae only resulted in a less or slight change of autocorrelation of the whole data series generation to generation. Crop system, food, climate and natural enemies, therefore, played more important roles in regulating the population dynamics than base number of the larvae. The basic techniques of geostatistics applied in analyzing temporal population dynamics were outlined.

  4. Geostatistical borehole image-based mapping of karst-carbonate aquifer pores

    Science.gov (United States)

    Michael Sukop,; Cunningham, Kevin J.

    2016-01-01

    Quantification of the character and spatial distribution of porosity in carbonate aquifers is important as input into computer models used in the calculation of intrinsic permeability and for next-generation, high-resolution groundwater flow simulations. Digital, optical, borehole-wall image data from three closely spaced boreholes in the karst-carbonate Biscayne aquifer in southeastern Florida are used in geostatistical experiments to assess the capabilities of various methods to create realistic two-dimensional models of vuggy megaporosity and matrix-porosity distribution in the limestone that composes the aquifer. When the borehole image data alone were used as the model training image, multiple-point geostatistics failed to detect the known spatial autocorrelation of vuggy megaporosity and matrix porosity among the three boreholes, which were only 10 m apart. Variogram analysis and subsequent Gaussian simulation produced results that showed a realistic conceptualization of horizontal continuity of strata dominated by vuggy megaporosity and matrix porosity among the three boreholes.

  5. Acceleration of the Geostatistical Software Library (GSLIB) by code optimization and hybrid parallel programming

    Science.gov (United States)

    Peredo, Oscar; Ortiz, Julián M.; Herrero, José R.

    2015-12-01

    The Geostatistical Software Library (GSLIB) has been used in the geostatistical community for more than thirty years. It was designed as a bundle of sequential Fortran codes, and today it is still in use by many practitioners and researchers. Despite its widespread use, few attempts have been reported in order to bring this package to the multi-core era. Using all CPU resources, GSLIB algorithms can handle large datasets and grids, where tasks are compute- and memory-intensive applications. In this work, a methodology is presented to accelerate GSLIB applications using code optimization and hybrid parallel processing, specifically for compute-intensive applications. Minimal code modifications are added decreasing as much as possible the elapsed time of execution of the studied routines. If multi-core processing is available, the user can activate OpenMP directives to speed up the execution using all resources of the CPU. If multi-node processing is available, the execution is enhanced using MPI messages between the compute nodes.Four case studies are presented: experimental variogram calculation, kriging estimation, sequential gaussian and indicator simulation. For each application, three scenarios (small, large and extra large) are tested using a desktop environment with 4 CPU-cores and a multi-node server with 128 CPU-nodes. Elapsed times, speedup and efficiency results are shown.

  6. Geostatistical analysis of soil moisture distribution in a part of Solani River catchment

    Science.gov (United States)

    Kumar, Kamal; Arora, M. K.; Hariprasad, K. S.

    2016-03-01

    The aim of this paper is to estimate soil moisture at spatial level by applying geostatistical techniques on the point observations of soil moisture in parts of Solani River catchment in Haridwar district of India. Undisturbed soil samples were collected at 69 locations with soil core sampler at a depth of 0-10 cm from the soil surface. Out of these, discrete soil moisture observations at 49 locations were used to generate a spatial soil moisture distribution map of the region. Two geostatistical techniques, namely, moving average and kriging, were adopted. Root mean square error (RMSE) between observed and estimated soil moisture at remaining 20 locations was determined to assess the accuracy of the estimated soil moisture. Both techniques resulted in low RMSE at small limiting distance, which increased with the increase in the limiting distance. The root mean square error varied from 7.42 to 9.77 in moving average method, while in case of kriging it varied from 7.33 to 9.99 indicating similar performance of the two techniques.

  7. Redesigning rain gauges network in Johor using geostatistics and simulated annealing

    Energy Technology Data Exchange (ETDEWEB)

    Aziz, Mohd Khairul Bazli Mohd, E-mail: mkbazli@yahoo.com [Centre of Preparatory and General Studies, TATI University College, 24000 Kemaman, Terengganu, Malaysia and Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor (Malaysia); Yusof, Fadhilah, E-mail: fadhilahy@utm.my [Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor (Malaysia); Daud, Zalina Mohd, E-mail: zalina@ic.utm.my [UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia, UTM KL, 54100 Kuala Lumpur (Malaysia); Yusop, Zulkifli, E-mail: zulyusop@utm.my [Institute of Environmental and Water Resource Management (IPASA), Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor (Malaysia); Kasno, Mohammad Afif, E-mail: mafifkasno@gmail.com [Malaysia - Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, UTM KL, 54100 Kuala Lumpur (Malaysia)

    2015-02-03

    Recently, many rainfall network design techniques have been developed, discussed and compared by many researchers. Present day hydrological studies require higher levels of accuracy from collected data. In numerous basins, the rain gauge stations are located without clear scientific understanding. In this study, an attempt is made to redesign rain gauge network for Johor, Malaysia in order to meet the required level of accuracy preset by rainfall data users. The existing network of 84 rain gauges in Johor is optimized and redesigned into a new locations by using rainfall, humidity, solar radiation, temperature and wind speed data collected during the monsoon season (November - February) of 1975 until 2008. This study used the combination of geostatistics method (variance-reduction method) and simulated annealing as the algorithm of optimization during the redesigned proses. The result shows that the new rain gauge location provides minimum value of estimated variance. This shows that the combination of geostatistics method (variance-reduction method) and simulated annealing is successful in the development of the new optimum rain gauge system.

  8. Selective remediation of contaminated sites using a two-level multiphase strategy and geostatistics.

    Science.gov (United States)

    Saito, Hirotaka; Goovaerts, Pierre

    2003-05-01

    Selective soil remediation aims to reduce costs by cleaning only the fraction of an exposure unit (EU) necessary to lower the average concentration below the regulatory threshold. This approach requires a prior stratification of each EU into smaller remediation units (RU) which are then selected according to various criteria. This paper presents a geostatistical framework to account for uncertainties attached to both RU and EU average concentrations in selective remediation. The selection of RUs is based on their impact on the postremediation probability for the EU average concentration to exceed the regulatory threshold, which is assessed using geostatistical stochastic simulation. Application of the technique to a set of 600 dioxin concentrations collected at Piazza Road EPA Superfund site in Missouri shows a substantial decrease in the number of RU remediated compared with single phase remediation. The lower remediation costs achieved by the new strategy are obtained to the detriment of a higher risk of false negatives, yet for this data set this risk remains below the 5% rate set by EPA region 7.

  9. Source Apportionment of Heavy Metals in Soils Using Multivariate Statistics and Geostatistics

    Institute of Scientific and Technical Information of China (English)

    QU Ming-Kai; LI Wei-Dong; ZHANG Chuan-Rong; WANG Shan-Qin; YANG Yong; HE Li-Yuan

    2013-01-01

    The main objectives of this study were to introduce an integrated method for effectively identifying soil heavy metal pollution sources and apportioning their contributions,and apply it to a case study.The method combines the principal component analysis/absolute principal component scores (PCA/APCS) receptor model and geostatistics.The case study was conducted in an area of 31 km2 in the urban-rural transition zone of Wuhan,a metropolis of central China.124 topsoil samples were collected for measuring the concentrations of eight heavy metal elements (Mn,Cu,Zn,Pb,Cd,Cr,Ni and Co).PCA results revealed that three major factors were responsible for soil heavy metal pollution,which were initially identified as "steel production","agronomic input" and "coal consumption".The APCS technique,combined with multiple linear regression analysis,was then applied for source apportionment.Steel production appeared to be the main source for Ni,Co,Cd,Zn and Mn,agronomic input for Cu,and coal consumption for Pb and Cr.Geostatistical interpolation using ordinary kriging was finally used to map the spatial distributions of the contributions of pollution sources and further confirm the result interpretations.The introduced method appears to be an effective tool in soil pollution source apportionment and identification,and might provide valuable reference information for pollution control and environmental management.

  10. Demonstration of a geostatistical approach to physically consistent downscaling of climate modeling simulations

    KAUST Repository

    Jha, Sanjeev Kumar

    2013-01-01

    A downscaling approach based on multiple-point geostatistics (MPS) is presented. The key concept underlying MPS is to sample spatial patterns from within training images, which can then be used in characterizing the relationship between different variables across multiple scales. The approach is used here to downscale climate variables including skin surface temperature (TSK), soil moisture (SMOIS), and latent heat flux (LH). The performance of the approach is assessed by applying it to data derived from a regional climate model of the Murray-Darling basin in southeast Australia, using model outputs at two spatial resolutions of 50 and 10 km. The data used in this study cover the period from 1985 to 2006, with 1985 to 2005 used for generating the training images that define the relationships of the variables across the different spatial scales. Subsequently, the spatial distributions for the variables in the year 2006 are determined at 10 km resolution using the 50 km resolution data as input. The MPS geostatistical downscaling approach reproduces the spatial distribution of TSK, SMOIS, and LH at 10 km resolution with the correct spatial patterns over different seasons, while providing uncertainty estimates through the use of multiple realizations. The technique has the potential to not only bridge issues of spatial resolution in regional and global climate model simulations but also in feature sharpening in remote sensing applications through image fusion, filling gaps in spatial data, evaluating downscaled variables with available remote sensing images, and aggregating/disaggregating hydrological and groundwater variables for catchment studies.

  11. Geostatistics applied to the study of the spatial distribution of Tibraca limbativentris in flooded rice fields

    Directory of Open Access Journals (Sweden)

    Juliano de Bastos Pazini

    2015-06-01

    Full Text Available Tibraca limbativentris (rice stem bug is an insect highly injurious to the rice crop in Brazil. The aim of this research was to define the spatial distribution of the T. limbativentris and improve the sampling process by means of geostatistical application techniques and construction of prediction maps in a flooded rice field located in the "Planalto da Campanha" Region, Rio Grande do Sul (RS, Brazil. The experiments were conducted in rice crop in the municipality of Itaqui - RS, in the crop years of 2009/10, 2010/11 and 2011/12, counting fortnightly the number of nymphs and adults in a georeferenced grid with points spaced at 50m in the first year and in 10m in the another years. It was performed a geostatistical analysis by means adjusting semivariogram and interpolation of numeric data by kriging to verify the spatial dependence and the subsequent mapping population. The results obtained indicated that the rice stem bug, T. limbativentris, has a strong spatial dependence. The prediction maps allow estimating population density of the pest and visualization of the spatial distribution in flooded rice fields, enabling the improvement of the traditional method of sampling for rice stem bug

  12. COMBINING NEURAL NETWORKS AND GEOSTATISTICS FOR LANDSLIDE HAZARD ASSESSMENT OF SAN SALVADOR METROPOLITAN AREA, EL SALVADOR

    Directory of Open Access Journals (Sweden)

    Ricardo Ríos

    2017-04-01

    Full Text Available This contribution describes the creation of a landslide hazard assessment model for San Salvador, a department in El Salvador. The analysis started with an aerial photointerpretation from Ministry of Environment and Natural Resources of El Salvador (MARN Spanish acronym, where 4792 landslides were identified and georeferenced along with 7 conditioning factors including: geomorphology, geology, rainfall intensity, peak ground acceleration, slope angle, distance to road, and distance to geological fault. Artificial Neural Networks (ANN were utilized to assess the susceptibility to landslides, achieving results where more than 80% of landslide were properly classified using in-sample and out of sample criteria. Logistic regression was used as base of comparison. Logistic regression obtained a lower performance. To complete the analysis we have performed interpolation of the points using the kriging method from geostatistical approach. Finally, the results show that is possible to derive a landslide hazard map, making use of a combination of ANNs and geostatistical techniques, thus the present study can help landslide mitigation in El Salvador.

  13. Approaches in highly parameterized inversion: bgaPEST, a Bayesian geostatistical approach implementation with PEST: documentation and instructions

    Science.gov (United States)

    Fienen, Michael N.; D'Oria, Marco; Doherty, John E.; Hunt, Randall J.

    2013-01-01

    The application bgaPEST is a highly parameterized inversion software package implementing the Bayesian Geostatistical Approach in a framework compatible with the parameter estimation suite PEST. Highly parameterized inversion refers to cases in which parameters are distributed in space or time and are correlated with one another. The Bayesian aspect of bgaPEST is related to Bayesian probability theory in which prior information about parameters is formally revised on the basis of the calibration dataset used for the inversion. Conceptually, this approach formalizes the conditionality of estimated parameters on the specific data and model available. The geostatistical component of the method refers to the way in which prior information about the parameters is used. A geostatistical autocorrelation function is used to enforce structure on the parameters to avoid overfitting and unrealistic results. Bayesian Geostatistical Approach is designed to provide the smoothest solution that is consistent with the data. Optionally, users can specify a level of fit or estimate a balance between fit and model complexity informed by the data. Groundwater and surface-water applications are used as examples in this text, but the possible uses of bgaPEST extend to any distributed parameter applications.

  14. Reservoir simulation and up-scaling of a waterflooding process using geostatistical simulated Dan-field data. Final report

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1996-12-01

    The geostatistical model represents a section of the Dan field in the Danish part of the North See. The Dan-field is a low permeability medium porosity oil reservoir. The section is placed on the southern flank of the Dan field. Using Annealing cosimulation technique (ACS) permeability and porosity distribution was derived from core samples of 15 wells (as hard data) and seismic impedances as secondary (soft) data. In this report 2 different 3D-sections of the geostatistical model are upscaled according to the principles of Stiles. A horizontal model consisting of the 3 top layers in the geostatistical model and a 3-D vertical segment was chosen. A single porosity BlackOil reservoir model is used as simulation model (i.e. gas soluted in the oil phase but no oil soluted in the gas phase). The following fluid- well- and initial state reservoir-data are used as input for the simulation of the geostatistical models: Oil formation volume factor; Oil compressibility; Oil viscosity. For the upscaled models the well data are adjusted to account for the upscaled grid size. Furthermore the relative permeabilities, the absolute permeabilities and the porosity are changed, according to the Stiles upscaling procedure. (EG)

  15. Geostatistical estimation of the transmissivity in a highly fractured metamorphic and crystalline aquifer (Man-Danane Region, Western Ivory Coast)

    Science.gov (United States)

    Razack, Moumtaz; Lasm, Théophile

    2006-06-01

    This work is aimed at estimating the transmissivity of highly fractured hard rock aquifers using a geostatistical approach. The studied aquifer is formed by the crystalline and metamorphic rocks of the Western Ivory Coast (West Africa), in the Man Danané area. The study area covers 7290 km 2 (90 km×81 km). The fracturing network is dense and well connected, without a marked fracture direction. A data base comprising 118 transmissivity ( T) values and 154 specific capacity ( Q/ s) values was compiled. A significant empirical relationship between T and Q/ s was found, which enabled the transmissivity data to be supplemented. The variographic analysis of the two variables showed that the variograms of T and Q/ s (which are lognormal variables) are much more structured than those of log T and log Q/ s (which are normal variables). This result is contrary to what was previously published and raises the question whether normality is necessary in geostatistical analysis. Several input and geostatistical estimations of the transmissivity were tested using the cross validation procedure: measured transmissivity data; supplemented transmissivity data; kriging; cokriging. The cross validation results showed that the best estimation is provided using the kriging procedure, the transmissivity field represented by the whole data sample (measured+estimated using specific capacity) and the structural model evaluated solely on the measured transmissivity. The geostatistical approach provided in fine a reliable estimation of the transmissivity of the Man Danané aquifer, which will be used as an input in forthcoming modelling.

  16. Local Geostatistical Models and Big Data in Hydrological and Ecological Applications

    Science.gov (United States)

    Hristopulos, Dionissios

    2015-04-01

    The advent of the big data era creates new opportunities for environmental and ecological modelling but also presents significant challenges. The availability of remote sensing images and low-cost wireless sensor networks implies that spatiotemporal environmental data to cover larger spatial domains at higher spatial and temporal resolution for longer time windows. Handling such voluminous data presents several technical and scientific challenges. In particular, the geostatistical methods used to process spatiotemporal data need to overcome the dimensionality curse associated with the need to store and invert large covariance matrices. There are various mathematical approaches for addressing the dimensionality problem, including change of basis, dimensionality reduction, hierarchical schemes, and local approximations. We present a Stochastic Local Interaction (SLI) model that can be used to model local correlations in spatial data. SLI is a random field model suitable for data on discrete supports (i.e., regular lattices or irregular sampling grids). The degree of localization is determined by means of kernel functions and appropriate bandwidths. The strength of the correlations is determined by means of coefficients. In the "plain vanilla" version the parameter set involves scale and rigidity coefficients as well as a characteristic length. The latter determines in connection with the rigidity coefficient the correlation length of the random field. The SLI model is based on statistical field theory and extends previous research on Spartan spatial random fields [2,3] from continuum spaces to explicitly discrete supports. The SLI kernel functions employ adaptive bandwidths learned from the sampling spatial distribution [1]. The SLI precision matrix is expressed explicitly in terms of the model parameter and the kernel function. Hence, covariance matrix inversion is not necessary for parameter inference that is based on leave-one-out cross validation. This property

  17. Geostatistical upscaling of rain gauge data to support uncertainty analysis of lumped urban hydrological models

    Science.gov (United States)

    Muthusamy, Manoranjan; Schellart, Alma; Tait, Simon; Heuvelink, Gerard B. M.

    2017-02-01

    In this study we develop a method to estimate the spatially averaged rainfall intensity together with associated level of uncertainty using geostatistical upscaling. Rainfall data collected from a cluster of eight paired rain gauges in a 400 m × 200 m urban catchment are used in combination with spatial stochastic simulation to obtain optimal predictions of the spatially averaged rainfall intensity at any point in time within the urban catchment. The uncertainty in the prediction of catchment average rainfall intensity is obtained for multiple combinations of intensity ranges and temporal averaging intervals. The two main challenges addressed in this study are scarcity of rainfall measurement locations and non-normality of rainfall data, both of which need to be considered when adopting a geostatistical approach. Scarcity of measurement points is dealt with by pooling sample variograms of repeated rainfall measurements with similar characteristics. Normality of rainfall data is achieved through the use of normal score transformation. Geostatistical models in the form of variograms are derived for transformed rainfall intensity. Next spatial stochastic simulation which is robust to nonlinear data transformation is applied to produce realisations of rainfall fields. These realisations in transformed space are first back-transformed and next spatially aggregated to derive a random sample of the spatially averaged rainfall intensity. Results show that the prediction uncertainty comes mainly from two sources: spatial variability of rainfall and measurement error. At smaller temporal averaging intervals both these effects are high, resulting in a relatively high uncertainty in prediction. With longer temporal averaging intervals the uncertainty becomes lower due to stronger spatial correlation of rainfall data and relatively smaller measurement error. Results also show that the measurement error increases with decreasing rainfall intensity resulting in a higher

  18. Precipitation estimation in mountainous terrain using multivariate geostatistics. Part II: isohyetal maps

    Science.gov (United States)

    Hevesi, Joseph A.; Flint, Alan L.; Istok, Jonathan D.

    1992-01-01

    Values of average annual precipitation (AAP) may be important for hydrologic characterization of a potential high-level nuclear-waste repository site at Yucca Mountain, Nevada. Reliable measurements of AAP are sparse in the vicinity of Yucca Mountain, and estimates of AAP were needed for an isohyetal mapping over a 2600-square-mile watershed containing Yucca Mountain. Estimates were obtained with a multivariate geostatistical model developed using AAP and elevation data from a network of 42 precipitation stations in southern Nevada and southeastern California. An additional 1531 elevations were obtained to improve estimation accuracy. Isohyets representing estimates obtained using univariate geostatistics (kriging) defined a smooth and continuous surface. Isohyets representing estimates obtained using multivariate geostatistics (cokriging) defined an irregular surface that more accurately represented expected local orographic influences on AAP. Cokriging results included a maximum estimate within the study area of 335 mm at an elevation of 7400 ft, an average estimate of 157 mm for the study area, and an average estimate of 172 mm at eight locations in the vicinity of the potential repository site. Kriging estimates tended to be lower in comparison because the increased AAP expected for remote mountainous topography was not adequately represented by the available sample. Regression results between cokriging estimates and elevation were similar to regression results between measured AAP and elevation. The position of the cokriging 250-mm isohyet relative to the boundaries of pinyon pine and juniper woodlands provided indirect evidence of improved estimation accuracy because the cokriging result agreed well with investigations by others concerning the relationship between elevation, vegetation, and climate in the Great Basin. Calculated estimation variances were also mapped and compared to evaluate improvements in estimation accuracy. Cokriging estimation variances

  19. Precipitation estimation in mountainous terrain using multivariate geostatistics. Part I: structural analysis

    Science.gov (United States)

    Hevesi, Joseph A.; Istok, Jonathan D.; Flint, Alan L.

    1992-01-01

    Values of average annual precipitation (AAP) are desired for hydrologic studies within a watershed containing Yucca Mountain, Nevada, a potential site for a high-level nuclear-waste repository. Reliable values of AAP are not yet available for most areas within this watershed because of a sparsity of precipitation measurements and the need to obtain measurements over a sufficient length of time. To estimate AAP over the entire watershed, historical precipitation data and station elevations were obtained from a network of 62 stations in southern Nevada and southeastern California. Multivariate geostatistics (cokriging) was selected as an estimation method because of a significant (p = 0.05) correlation of r = .75 between the natural log of AAP and station elevation. A sample direct variogram for the transformed variable, TAAP = ln [(AAP) 1000], was fitted with an isotropic, spherical model defined by a small nugget value of 5000, a range of 190 000 ft, and a sill value equal to the sample variance of 163 151. Elevations for 1531 additional locations were obtained from topographic maps to improve the accuracy of cokriged estimates. A sample direct variogram for elevation was fitted with an isotropic model consisting of a nugget value of 5500 and three nested transition structures: a Gaussian structure with a range of 61 000 ft, a spherical structure with a range of 70 000 ft, and a quasi-stationary, linear structure. The use of an isotropic, stationary model for elevation was considered valid within a sliding-neighborhood radius of 120 000 ft. The problem of fitting a positive-definite, nonlinear model of coregionalization to an inconsistent sample cross variogram for TAAP and elevation was solved by a modified use of the Cauchy-Schwarz inequality. A selected cross-variogram model consisted of two nested structures: a Gaussian structure with a range of 61 000 ft and a spherical structure with a range of 190 000 ft. Cross validation was used for model selection and for

  20. Introduction to this Special Issue on Geostatistics and Scaling of Remote Sensing

    Science.gov (United States)

    Quattrochi, Dale A.

    1999-01-01

    The germination of this special PE&RS issue began at the Royal Geographical Society (with the Institute of British Geographers)(RCS-IBC) annual meeting in January, 1997 held at the University of Exeter in Exeter, England. The cold and snow of an England winter were greatly tempered by the friendly and cordial discussions that ensued at the meeting on possible ways to foster both dialog and research across "the Big Pond" between geographers in the US and the UK on the use of geostatistics and geospatial techniques for remote sensing of land surface processes. It was decided that one way to stimulate and enhance cooperation on the application of geostatistics and geospatial methods in remote sensing was to hold parallel sessions on these topics at appropriate meeting venues in 1998 in both the US and the UK Selected papers given at these sessions would be published as a special issue of PE&RS on the US side, and as a special issue of Computers and Geosciences (C&G) on the UK side, to highlight the commonality in research on geostatistics and geospatial methods in remote sensing and spatial data analysis on both sides of the Atlantic Ocean. As a consequence, a session on "Ceostatistics and Geospatial Techniques for Remote Sensing of Land Surface Processes" was held at the Association of American Geographers (AAG) annual meeting in Boston, Massachusetts in March, 1998, sponsored by the AAG's Remote Sensing Specialty Group (RSSG). A similar session was held at the RGS-IBG annual meeting in Guildford, Surrey, England in January 1998, organized by the Modeling and Advanced Techniques Special Interest Group (MAT SIG) of the Remote Sensing Society (RSS). The six papers that in part, comprise this issue of PE&RS, are the US complement to such a dual journal publication effort. Both of us are co-editors of each of the journal special issues, with the lead editor of each journal being from their respective side of the Atlantic where the journals are published. The special

  1. Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging

    Directory of Open Access Journals (Sweden)

    Goovaerts Pierre

    2005-12-01

    Full Text Available Abstract Background Cancer mortality maps are used by public health officials to identify areas of excess and to guide surveillance and control activities. Quality of decision-making thus relies on an accurate quantification of risks from observed rates which can be very unreliable when computed from sparsely populated geographical units or recorded for minority populations. This paper presents a geostatistical methodology that accounts for spatially varying population sizes and spatial patterns in the processing of cancer mortality data. Simulation studies are conducted to compare the performances of Poisson kriging to a few simple smoothers (i.e. population-weighted estimators and empirical Bayes smoothers under different scenarios for the disease frequency, the population size, and the spatial pattern of risk. A public-domain executable with example datasets is provided. Results The analysis of age-adjusted mortality rates for breast and cervix cancers illustrated some key features of commonly used smoothing techniques. Because of the small weight assigned to the rate observed over the entity being smoothed (kernel weight, the population-weighted average leads to risk maps that show little variability. Other techniques assign larger and similar kernel weights but they use a different piece of auxiliary information in the prediction: global or local means for global or local empirical Bayes smoothers, and spatial combination of surrounding rates for the geostatistical estimator. Simulation studies indicated that Poisson kriging outperforms other approaches for most scenarios, with a clear benefit when the risk values are spatially correlated. Global empirical Bayes smoothers provide more accurate predictions under the least frequent scenario of spatially random risk. Conclusion The approach presented in this paper enables researchers to incorporate the pattern of spatial dependence of mortality rates into the mapping of risk values and the

  2. A geostatistical method applied to the geochemical study of the Chichinautzin Volcanic Field in Mexico

    Science.gov (United States)

    Robidoux, P.; Roberge, J.; Urbina Oviedo, C. A.

    2011-12-01

    The origin of magmatism and the role of the subducted Coco's Plate in the Chichinautzin volcanic field (CVF), Mexico is still a subject of debate. It has been established that mafic magmas of alkali type (subduction) and calc-alkali type (OIB) are produced in the CVF and both groups cannot be related by simple fractional crystallization. Therefore, many geochemical studies have been done, and many models have been proposed. The main goal of the work present here is to provide a new tool for the visualization and interpretation of geochemical data using geostatistics and geospatial analysis techniques. It contains a complete geodatabase built from referred samples over the 2500 km2 area of CVF and its neighbour stratovolcanoes (Popocatepetl, Iztaccihuatl and Nevado de Toluca). From this database, map of different geochemical markers were done to visualise geochemical signature in a geographical manner, to test the statistic distribution with a cartographic technique and highlight any spatial correlations. The distribution and regionalization of the geochemical signatures can be viewed in a two-dimensional space using a specific spatial analysis tools from a Geographic Information System (GIS). The model of spatial distribution is tested with Linear Decrease (LD) and Inverse Distance Weight (IDW) interpolation technique because they best represent the geostatistical characteristics of the geodatabase. We found that ratio of Ba/Nb, Nb/Ta, Th/Nb show first order tendency, which means visible spatial variation over a large scale area. Monogenetic volcanoes in the center of the CVF have distinct values compare to those of the Popocatepetl-Iztaccihuatl polygenetic complex which are spatially well defined. Inside the Valley of Mexico, a large quantity of monogenetic cone in the eastern portion of CVF has ratios similar to the Iztaccihuatl and Popocatepetl complex. Other ratios like alkalis vs SiO2, V/Ti, La/Yb, Zr/Y show different spatial tendencies. In that case, second

  3. Geostatistical Evaluation of Spring Water Quality in an Urbanizing Carbonate Aquifer

    Science.gov (United States)

    McGinty, A.; Welty, C.

    2003-04-01

    As part of an investigation of the impacts of urbanization on the hydrology and ecology of Valley Creek watershed near Philadelphia, Pennsylvania, we have analyzed the chemical composition of 110 springs to assess the relative influence of geology and anthropogenic activities on water quality. The 60 km^2 watershed is underlain by productive fractured rock aquifers composed of Cambrian and Ordovician carbonate rocks in the central valley and Cambrian crystalline and siliciclastic rocks (quartzite and phyllite) in the north and south hills that border the valley. All tributaries of the surface water system originate in the crystalline and siliciclastic hills. The watershed is covered by 17% impervious area and contains 6 major hazardous waste sites, one active quarrying operation and one golf course; 25% of the area utilizes septic systems for sewage disposal. We identified 172 springs, 110 of which had measurable flow rates ranging from 0.002 to 5 l/s. The mapped surficial geology appears as an anisotropic pattern, with long bands of rock formations paralleling the geographic orientation of the valley. Mapped development appears as a more isotropic pattern, characterized by isolated patches of land use that are not coincident with the evident geologic pattern. Superimposed upon these characteristics is a dense array of depressions and shallow sinkholes in the carbonate rocks, and a system of major faults at several formation contacts. We used indicator geostatistics to quantitatively characterize the spatial extent of the major geologic formations and patterns of land use. Maximum correlation scales for the rock types corresponded with strike direction and ranged from 1000 to 3000 m. Anisotropy ratios ranged from 2 to 4. Land-use correlation scales were generally smaller (200 to 500 m) with anisotropy ratios of around 1.2, i.e., nearly isotropic as predicted. Geostatistical analysis of spring water quality parameters related to geology (pH, specific conductance

  4. Investigating spatial resolutions of imagery for intertidal sediment characterization using geostatistics

    Science.gov (United States)

    Ibrahim, Elsy; Adam, Stefanie; De Wever, Aaike; Govaerts, Annelies; Vervoort, Andre; Monbaliu, Jaak

    2014-08-01

    To investigate bio-chemical processes of intertidal sediments, variations in sediment properties such as moisture content, mud content, and chlorophyll a content need to be understood. Remote sensing has been an efficient alternative to traditional data collection methods for such properties. Yet, with the availability of various types of useful sensors, choosing a suitable spatial resolution is challenging, especially that each type has its own cost, availability, and data specifications. This paper investigates the losses in spatial information of sediment properties on the Molenplaat, an intertidal flat on the Western-Scheldt estuary, upon the use of various resolutions. This was carried out using a synergy between remote sensing and geostatistics. The results showed that for the Molenplaat, chlorophyll a content can be well represented by low to medium resolutions. Yet, for moisture and mud content, spatial structures would be lost upon any decrease of resolution from a 4 m×4 m pixel size.

  5. Testing geostatistical methods to combine radar and rain gauges for precipitation mapping in a mountainous region

    Science.gov (United States)

    Erdin, R.; Frei, C.; Sideris, I.; Kuensch, H.-R.

    2010-09-01

    There is an increasing demand for accurate mapping of precipitation at a spatial resolution of kilometers. Radar and rain gauges - the two main precipitation measurement systems - exhibit complementary strengths and weaknesses. Radar offers high spatial and temporal resolution but lacks accuracy of absolute values, whereas rain gauges provide accurate values at their specific point location but suffer from poor spatial representativeness. Methods of geostatistical mapping have been proposed to combine radar and rain gauge data for quantitative precipitation estimation (QPE). The aim is to combine the respective strengths and compensate for the respective weaknesses of the two observation platforms. Several studies have demonstrated the potential of these methods over topography of moderate complexity, but their performance remains unclear for high-mountain regions where rainfall patterns are complex, the representativeness of rain gauge measurements is limited and radar observations are obstructed. In this study we examine the potential and limitations of two frequently used geostatistical mapping methods for the territory of Switzerland, where the mountain chain of the Alps poses particular challenges to QPE. The two geostatistical methods explored are kriging with external drift (KED) using radar as drift variable and ordinary kriging of radar errors (OKRE). The radar data is a composite from three C-band radars using a constant Z-R relationship, advanced correction processings for visibility, ground clutter and beam shielding and a climatological bias adjustment. The rain gauge data originates from an automatic network with a typical inter-station distance of 25 km. Both combination methods are applied to a set of case examples representing typical rainfall situations in the Alps with their inherent challenges at daily and hourly time resolution. The quality of precipitation estimates is assessed by several skill scores calculated from cross validation errors at

  6. An assessment of gas emanation hazard using a geographic information system and geostatistics.

    Science.gov (United States)

    Astorri, F; Beaubien, S E; Ciotoli, G; Lombardi, S

    2002-03-01

    This paper describes the use of geostatistical analysis and GIS techniques to assess gas emanation hazards. The Mt. Vulsini volcanic district was selected for this study because of the wide range of natural phenomena locally present that affect gas migration in the near surface. In addition, soil gas samples that were collected in this area should allow for a calibration between the generated risk/hazard models and the measured distribution of toxic gas species at surface. The approach used during this study consisted of three general stages. First data were digitally organized into thematic layers, then software functions in the GIS program "ArcView" were used to compare and correlate these various layers, and then finally the produced "potential-risk" map was compared with radon soil gas data in order to validate the model and/or to select zones for further, more-detailed soil gas investigations.

  7. Geostatistics as a validation tool for setting ozone standards for durum wheat.

    Science.gov (United States)

    De Marco, Alessandra; Screpanti, Augusto; Paoletti, Elena

    2010-02-01

    Which is the best standard for protecting plants from ozone? To answer this question, we must validate the standards by testing biological responses vs. ambient data in the field. A validation is missing for European and USA standards, because the networks for ozone, meteorology and plant responses are spatially independent. We proposed geostatistics as validation tool, and used durum wheat in central Italy as a test. The standards summarized ozone impact on yield better than hourly averages. Although USA criteria explained ozone-induced yield losses better than European criteria, USA legal level (75 ppb) protected only 39% of sites. European exposure-based standards protected > or =90%. Reducing the USA level to the Canadian 65 ppb or using W126 protected 91% and 97%, respectively. For a no-threshold accumulated stomatal flux, 22 mmol m(-2) was suggested to protect 97% of sites. In a multiple regression, precipitation explained 22% and ozone explained <0.9% of yield variability.

  8. [Spatial structure analysis and distribution simulation of Therioaphis trifolii population based on geostatistics and GIS].

    Science.gov (United States)

    Zhang, Rong; Leng, Yun-fa; Zhu, Meng-meng; Wang, Fang

    2007-11-01

    Based on geographic information system and geostatistics, the spatial structure of Therioaphis trifolii population of different periods in Yuanzhou district of Guyuan City, the southern Ningxia Province, was analyzed. The spatial distribution of Therioaphis trifolii population was also simulated by ordinary Kriging interpretation. The results showed that Therioaphis trifolii population of different periods was correlated spatially in the study area. The semivariograms of Therioaphis trifolii could be described by exponential model, indicating an aggregated spatial arrangement. The spatial variance varied from 34.13%-48.77%, and the range varied from 8.751-12.049 km. The degree and direction of aggregation showed that the trend was increased gradually from southwest to northeast. The dynamic change of Therioaphis trifolii population in different periods could be analyzed intuitively on the simulated maps of the spatial distribution from the two aspects of time and space, The occurrence position and degree of Therioaphis trifolii to a state of certain time could be determined easily.

  9. Soil risk assessment of As and Zn contamination in a coal mining region using geostatistics [corrected].

    Science.gov (United States)

    Komnitsas, Kostas; Modis, Kostas

    2006-12-01

    The present paper aims to map As and Zn contamination and assess the risk for agricultural soils in a wider disposal site containing wastes derived from coal beneficiation. Geochemical data related to environmental studies show that the waste characteristics favor solubilisation and mobilization of inorganic contaminants and in some cases the generation of acidic leachates. 135 soil samples were collected from a 34 km(2) area and analysed by using geostatistics under the maximum entropy principle in order to produce risk assessment maps and estimate the probability of soil contamination. In addition, the present paper discusses the main issues related to risk assessment in wider mining and waste disposal sites in order to assist decision makers in selecting feasible rehabilitation schemes.

  10. [Evaluation on environmental quality of heavy metals in soils and vegetables based on geostatistics and GIS].

    Science.gov (United States)

    Xie, Zheng-miao; Li, Jing; Wang, Bi-ling; Chen, Jian-jun

    2006-10-01

    Contents of heavy metals (Pb, Zn, Cd, Cu) in soils and vegetables from Dongguan town in Shangyu city, China were studied using geostatistical analysis and GIS technique to evaluate environmental quality. Based on the evaluation criteria, the distribution of the spatial variability of heavy metals in soil-vegetable system was mapped and analyzed. The results showed that the distribution of soil heavy metals in a large number of soil samples in Dongguan town was asymmetric. The contents of Zn and Cu were lower than those of Cd and Pb. The concentrations distribution of Pb, Zn, Cd and Cu in soils and vegetables were different in spatial variability. There was a close relationship between total and available contents of heavy metals in soil. The contents of Pb and Cd in green vegetables were higher than those of Zn and Cu and exceeded the national sanitation standards for vegetables.

  11. Supervised restoration of degraded medical images using multiple-point geostatistics.

    Science.gov (United States)

    Pham, Tuan D

    2012-06-01

    Reducing noise in medical images has been an important issue of research and development for medical diagnosis, patient treatment, and validation of biomedical hypotheses. Noise inherently exists in medical and biological images due to the acquisition and transmission in any imaging devices. Being different from image enhancement, the purpose of image restoration is the process of removing noise from a degraded image in order to recover as much as possible its original version. This paper presents a statistically supervised approach for medical image restoration using the concept of multiple-point geostatistics. Experimental results have shown the effectiveness of the proposed technique which has potential as a new methodology for medical and biological image processing.

  12. The Importance of the Range Parameter for Estimation and Prediction in Geostatistics

    CERN Document Server

    Kaufman, Cari

    2011-01-01

    Two canonical problems in geostatistics are estimating the parameters in a specified family of stochastic process models and predicting the process at new locations. A number of asymptotic results for these problems over a fixed spatial domain indicate that, for a Gaussian process with Mat\\'ern covariance function, one can fix the range parameter controlling the rate of decay of the process and obtain results that are asymptotically equivalent to the case that the range parameter is known. We discuss why these results do not always provide the appropriate intuition for finite samples. Moreover, we prove that a number of these asymptotic results may be extended to the case that the variance and range parameters are jointly estimated via maximum likelihood or maximum tapered likelihood. Our simulation results show that performance on a variety of metrics is improved and asymptotic approximations are applicable for smaller sample sizes when the range parameter is estimated. These effects are particularly apparen...

  13. Restricted spatial regression in practice: Geostatistical models, confounding, and robustness under model misspecification

    Science.gov (United States)

    Hanks, Ephraim M.; Schliep, Erin M.; Hooten, Mevin B.; Hoeting, Jennifer A.

    2015-01-01

    In spatial generalized linear mixed models (SGLMMs), covariates that are spatially smooth are often collinear with spatially smooth random effects. This phenomenon is known as spatial confounding and has been studied primarily in the case where the spatial support of the process being studied is discrete (e.g., areal spatial data). In this case, the most common approach suggested is restricted spatial regression (RSR) in which the spatial random effects are constrained to be orthogonal to the fixed effects. We consider spatial confounding and RSR in the geostatistical (continuous spatial support) setting. We show that RSR provides computational benefits relative to the confounded SGLMM, but that Bayesian credible intervals under RSR can be inappropriately narrow under model misspecification. We propose a posterior predictive approach to alleviating this potential problem and discuss the appropriateness of RSR in a variety of situations. We illustrate RSR and SGLMM approaches through simulation studies and an analysis of malaria frequencies in The Gambia, Africa.

  14. [Geostatistical analysis on distribution pattern of the tobacco budworm larva in Enshi, Hubei, China].

    Science.gov (United States)

    Xia, Peng-Liang; Wang, Rui; Tan, Jun

    2014-03-01

    Tobacco budworm (Helicoverpa assulta) larvae feed on tobacco leaves (Nicotiana sp.), resulting in significant loss in tobacco production. Geostatistical method was used to analyze H. assulta spatial patterns and dynamics in this paper. The results showed that, H. assulta larvae appeared 40 days after the tobacco plants transplanting, and reached its peak at the early-mature period. The nested spherical and exponential model was the major model for tobacco budworm larva in the field, suggesting its aggregated distribution. The spatial variability C/(C0 + C) was larger than 0.75, which indicated H. assulta larva had wider structural variation and narrower random variation. There was a massive migration of tobacco budworm larva in the fast-growing stage of tobacco. Its quantity became stable after that, especially at the mature stage of tobacco.

  15. Geostatistical stability analysis of co-depositional sand-thickened tailings embankments

    Energy Technology Data Exchange (ETDEWEB)

    Elkateb, T. [Thurber Engineering Ltd., Edmonton, AB (Canada); Chalaturnyk, R.; Robertson, P.K. [Alberta Univ., Edmonton, AB (Canada). Dept. of Civil and Environmental Engineering

    2003-07-01

    Co-deposition is a novel technique for the disposal of thickened tailings pockets. In co-deposition, tailings are randomly distributed within a bigger mass of sand. The oil sands industry of Alberta is currently considering using this technique. This paper describes the attempt that was made to assess the engineering behaviour of this tailing disposal system in a probabilistic analysis framework. Several realizations of co-depositional embankments were generated using geostatistical theories. In turn, the stability of the disposal system expressed in terms of factors of safety against shear failure and the associated vertical deformations was assessed using these realizations and FLAC software. A sensitivity to embankment characteristics was revealed by failure probabilities and vertical displacements, such as embankment height and side slopes, and undrained shear strength of thickened tailings. The authors proposed an allowable failure probability of 17 per cent for these embankments to avoid irreparable excessive deformations. 11 refs., 1 tab., 8 figs.

  16. Soil moisture estimation by assimilating L-band microwave brightness temperature with geostatistics and observation localization.

    Science.gov (United States)

    Han, Xujun; Li, Xin; Rigon, Riccardo; Jin, Rui; Endrizzi, Stefano

    2015-01-01

    The observation could be used to reduce the model uncertainties with data assimilation. If the observation cannot cover the whole model area due to spatial availability or instrument ability, how to do data assimilation at locations not covered by observation? Two commonly used strategies were firstly described: One is covariance localization (CL); the other is observation localization (OL). Compared with CL, OL is easy to parallelize and more efficient for large-scale analysis. This paper evaluated OL in soil moisture profile characterizations, in which the geostatistical semivariogram was used to fit the spatial correlated characteristics of synthetic L-Band microwave brightness temperature measurement. The fitted semivariogram model and the local ensemble transform Kalman filter algorithm are combined together to weight and assimilate the observations within a local region surrounding the grid cell of land surface model to be analyzed. Six scenarios were compared: 1_Obs with one nearest observation assimilated, 5_Obs with no more than five nearest local observations assimilated, and 9_Obs with no more than nine nearest local observations assimilated. The scenarios with no more than 16, 25, and 36 local observations were also compared. From the results we can conclude that more local observations involved in assimilation will improve estimations with an upper bound of 9 observations in this case. This study demonstrates the potentials of geostatistical correlation representation in OL to improve data assimilation of catchment scale soil moisture using synthetic L-band microwave brightness temperature, which cannot cover the study area fully in space due to vegetation effects.

  17. Mapping aboveground woody biomass using forest inventory, remote sensing and geostatistical techniques.

    Science.gov (United States)

    Yadav, Bechu K V; Nandy, S

    2015-05-01

    Mapping forest biomass is fundamental for estimating CO₂ emissions, and planning and monitoring of forests and ecosystem productivity. The present study attempted to map aboveground woody biomass (AGWB) integrating forest inventory, remote sensing and geostatistical techniques, viz., direct radiometric relationships (DRR), k-nearest neighbours (k-NN) and cokriging (CoK) and to evaluate their accuracy. A part of the Timli Forest Range of Kalsi Soil and Water Conservation Division, Uttarakhand, India was selected for the present study. Stratified random sampling was used to collect biophysical data from 36 sample plots of 0.1 ha (31.62 m × 31.62 m) size. Species-specific volumetric equations were used for calculating volume and multiplied by specific gravity to get biomass. Three forest-type density classes, viz. 10-40, 40-70 and >70% of Shorea robusta forest and four non-forest classes were delineated using on-screen visual interpretation of IRS P6 LISS-III data of December 2012. The volume in different strata of forest-type density ranged from 189.84 to 484.36 m(3) ha(-1). The total growing stock of the forest was found to be 2,024,652.88 m(3). The AGWB ranged from 143 to 421 Mgha(-1). Spectral bands and vegetation indices were used as independent variables and biomass as dependent variable for DRR, k-NN and CoK. After validation and comparison, k-NN method of Mahalanobis distance (root mean square error (RMSE) = 42.25 Mgha(-1)) was found to be the best method followed by fuzzy distance and Euclidean distance with RMSE of 44.23 and 45.13 Mgha(-1) respectively. DRR was found to be the least accurate method with RMSE of 67.17 Mgha(-1). The study highlighted the potential of integrating of forest inventory, remote sensing and geostatistical techniques for forest biomass mapping.

  18. Geostatistical regionalization of low-flow indices: PSBI and Top-Kriging

    Directory of Open Access Journals (Sweden)

    S. Castiglioni

    2010-09-01

    Full Text Available Recent studies highlight that geostatistical interpolation, which has been originally developed for the spatial interpolation of point data, can be effectively applied to the problem of regionalization of hydrometric information. This study compares two innovative geostatistical approaches for the prediction of low-flows in ungauged basins. The first one, named Physiographic-Space Based Interpolation (PSBI, performs the spatial interpolation of the desired streamflow index (e.g., annual streamflow, low-flow index, flood quantile, etc. in the space of catchment descriptors. The second technique, named Topological kriging or Top-Kriging, predicts the variable of interest along river networks taking both the area and nested nature of catchments into account. PSBI and Top-Kriging are applied for the regionalization of Q355 (i.e., the streamflow that is equalled or exceeded 355 days in a year, on average over a broad geographical region in central Italy, which contains 51 gauged catchments. Both techniques are cross-validated through a leave-one-out procedure at all available gauges and applied to a subregion to produce a continuous estimation of Q355 along the river network extracted from a 90 m DEM. The results of the study show that Top-Kriging and PSBI present complementary features and have comparable performances (Nash-Sutcliffe efficiencies in cross-validation of 0.89 and 0.83, respectively. Both techniques provide plausible and accurate predictions of Q355 in ungauged basins and represent promising opportunities for regionalization of low-flows.

  19. Geostatistical three-dimensional modeling of oolite shoals, St. Louis Limestone, southwest Kansas

    Science.gov (United States)

    Qi, L.; Carr, T.R.; Goldstein, R.H.

    2007-01-01

    In the Hugoton embayment of southwestern Kansas, reservoirs composed of relatively thin (oil. The geometry and distribution of oolitic deposits control the heterogeneity of the reservoirs, resulting in exploration challenges and relatively low recovery. Geostatistical three-dimensional (3-D) models were constructed to quantify the geometry and spatial distribution of oolitic reservoirs, and the continuity of flow units within Big Bow and Sand Arroyo Creek fields. Lithofacies in uncored wells were predicted from digital logs using a neural network. The tilting effect from the Laramide orogeny was removed to construct restored structural surfaces at the time of deposition. Well data and structural maps were integrated to build 3-D models of oolitic reservoirs using stochastic simulations with geometry data. Three-dimensional models provide insights into the distribution, the external and internal geometry of oolitic deposits, and the sedimentologic processes that generated reservoir intervals. The structural highs and general structural trend had a significant impact on the distribution and orientation of the oolitic complexes. The depositional pattern and connectivity analysis suggest an overall aggradation of shallow-marine deposits during pulses of relative sea level rise followed by deepening near the top of the St. Louis Limestone. Cemented oolitic deposits were modeled as barriers and baffles and tend to concentrate at the edge of oolitic complexes. Spatial distribution of porous oolitic deposits controls the internal geometry of rock properties. Integrated geostatistical modeling methods can be applicable to other complex carbonate or siliciclastic reservoirs in shallow-marine settings. Copyright ?? 2007. The American Association of Petroleum Geologists. All rights reserved.

  20. Geostatistical prediction of flow-duration curves in an index-flow framework

    Science.gov (United States)

    Pugliese, A.; Castellarin, A.; Brath, A.

    2014-09-01

    An empirical period-of-record flow-duration curve (FDC) describes the percentage of time (duration) in which a given streamflow was equaled or exceeded over an historical period of time. In many practical applications one has to construct FDCs in basins that are ungauged or where very few observations are available. We present an application strategy of top-kriging, which makes the geostatistical procedure capable of predicting FDCs in ungauged catchments. Previous applications of top-kriging mainly focused on the prediction of point streamflow indices (e.g. flood quantiles, low-flow indices, etc.); here the procedure is used to predict the entire curve in ungauged sites as a weighted average of standardised empirical FDCs through the traditional linear-weighting scheme of kriging methods. In particular, we propose to standardise empirical FDCs by a reference index-flow value (i.e. mean annual flow, or mean annual precipitation × the drainage area) and to compute the overall negative deviation of the curves from this reference value. We then propose to use these values, which we term total negative deviation (TND), for expressing the hydrological similarity between catchments and for deriving the geostatistical weights. We focus on the prediction of FDCs for 18 unregulated catchments located in central Italy, and we quantify the accuracy of the proposed technique under various operational conditions through an extensive cross-validation and sensitivity analysis. The cross-validation points out that top-kriging is a reliable approach for predicting FDCs with Nash-Sutcliffe efficiency measures ranging from 0.85 to 0.96 (depending on the model settings) very low biases over the entire duration range, and an enhanced representation of the low-flow regime relative to other regionalisation models that were recently developed for the same study region.

  1. Geostatistics and Its Application in Geological Engineering%地质统计学及其在地质工程中的应用分析

    Institute of Scientific and Technical Information of China (English)

    常维

    2011-01-01

    随着科技日益发展,地质统计学在地质工程中也发挥着重要的作用,这就对地质统计提出了更高的要求.本文从地质统计方法入手,简要阐述了地质统计学发展的现状,并分析了统计方法在地质工程中的重要作用及其应用.%With the development of science and technology, geostatistics plays an important role in geological engineering, which puts forward higher demand for geostatistics. Starting from geostatistics, this article illustrates the status quo of geostatistics development, and analyzes the important role of statistical methods and its applications in the geological engineering.

  2. Geostatistical modeling of a fluviodeltaic reservoir in the Huyapari Field, Hamaca area, in the Faja Petrolifera del Orinoco, Venezuela

    Energy Technology Data Exchange (ETDEWEB)

    De Ascencao, Erika M.; Munckton, Toni; Digregorio, Ricardo [Petropiar (Venezuela)

    2011-07-01

    The Huyapari field, situated within the Faja Petrolifera del Orinoco (FPO) of Venezuela presents unique problems in terms of modeling. This field is spread over a wide area and is therefore subject to variable oil quality and complex fluvial facies architecture. Ameriven and PDVSA have been working on characterizing the ld's reservoirs in this field since 2000 and the aim of this paper is to present these efforts. Among others, a 3-D seismic survey completed in 1998 and a stratigraphic framework built from 149 vertical wells were used for reservoir characterization. Geostatistical techniques such as sequential Gaussian simulation with locally varying mean and cloud transform were also used. Results showed that these geostatistical methods accurately represented the architecture and properties of the reservoir and its fluid distribution. This paper showed that the application of numerous different techniques in the Hamasca area permitted reservoir complexity to be captured.

  3. Bayesian and Geostatistical Approaches to Combining Categorical Data Derived from Visual and Digital Processing of Remotely Sensed Images

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jingxiong; LI Deren

    2005-01-01

    This paper seeks a synthesis of Bayesian and geostatistical approaches to combining categorical data in the context of remote sensing classification.By experiment with aerial photographs and Landsat TM data, accuracy of spectral, spatial, and combined classification results was evaluated.It was confirmed that the incorporation of spatial information in spectral classification increases accuracy significantly.Secondly, through test with a 5-class and a 3-class classification schemes, it was revealed that setting a proper semantic framework for classification is fundamental to any endeavors of categorical mapping and the most important factor affecting accuracy.Lastly, this paper promotes non-parametric methods for both definition of class membership profiling based on band-specific histograms of image intensities and derivation of spatial probability via indicator kriging, a non-parametric geostatistical technique.

  4. Comparison of kriging and cokriging for the geostatistical estimation of specific capacity in the Newark Basin (NJ) aquifer system.

    Science.gov (United States)

    Carter, Gail P; Miskewitz, Robert J; Isukapalli, Sastry; Mun, Yuri; Vyas, Vikram; Yoon, Sungwon; Georgeopoulos, Panos; Uchrin, Christopher G

    2011-01-01

    Groundwater is a major water source in New Jersey; hence, accurate hydrogeologic data are extremely important. However, most measured data have inadequate spatial density and their locations are often clustered. Our study focuses on implementing geostatistical methods to generate the spatial distribution of specific capacity over the Newark Basin in New Jersey. Two geostatistical methods, ordinary kriging and cokriging, were employed and compared. Ordinary kriging was employed to estimate the spatial distribution of specific capacity by using measured values. Cokriging incorporated the spatial variability of fracture density into the estimation with the spatial variability of specific capacity, as groundwater flow in fractured rock aquifers depends on the fracture characteristics in the Newark Basin. Results indicate that cokriging manifested substantial improvements over ordinary kriging including a larger areal coverage, a more detailed variation of specific capacity, and reduction in the variance of its estimates.

  5. Genetic Geostatistical Framework for Spatial Analysis of Fine-Scale Genetic Heterogeneity in Modern Populations: Results from the KORA Study

    OpenAIRE

    Diaz-Lacava, A. N.; Walier, M; D. Holler; Steffens, M; Gieger, C; C. Furlanello; Lamina, C; Wichmann, H E; Becker, T

    2015-01-01

    Aiming to investigate fine-scale patterns of genetic heterogeneity in modern humans from a geographic perspective, a genetic geostatistical approach framed within a geographic information system is presented. A sample collected for prospective studies in a small area of southern Germany was analyzed. None indication of genetic heterogeneity was detected in previous analysis. Socio-demographic and genotypic data of German citizens were analyzed (212 SNPs; n = 728). Genetic heterogeneity was ev...

  6. Efficient Geostatistical Inversion under Transient Flow Conditions in Heterogeneous Porous Media

    Science.gov (United States)

    Klein, Ole; Cirpka, Olaf A.; Bastian, Peter; Ippisch, Olaf

    2014-05-01

    The assessment of hydraulic aquifer parameters is important for the evaluation of anthropogenic impacts on groundwater resources. The distribution of these parameters determines flow paths and solute travel times and is therefore critical for the successful design and deployment of remediation schemes at contaminated sites. Direct measurement of these properties is not possible, making indirect observations through dependent quantities and parameter estimation a necessity. The geostatistical approach characterizes these hydraulic parameters without predetermined zonation. The parameter fields are treated as stochastic processes, optionally incorporating a priori information in the probability distribution. Maximizing the likelihood of the parameters with regard to the given observations yields a parameter estimate with high spatial resolution. This approach naturally leads to nonlinear least squares optimization problems, namely objective functions of the form L(Y ) = 1(Y ')TQ -Y1YY ' + 1[F(Y) - z]T Q-z1z [F(Y )- z], 2 2 where Y are the parameters, Y ' their deviations from the a priori estimate, QY Y their covariance matrix, z the measurements, Qzz their covariance matrix and F the forward model mapping parameters to observations. In theory, this objective function may be minimized using standard gradient-based techniques like Gauss-Newton. Due to the typically high number of parameters, however, this is not practical. Let nY be the number of parameters and nz the number of observations. Then QY Y and its inverse are both dense nY ×nY matrices, and the sensitivity matrix Hz := δz/δY is a nz ×nY matrix that has to be assembled using forward or adjoint model runs. Specialized schemes have been developed to reduce the dimensionality of the problem and avoid the high cost of handling products with QY Y -1. This enables efficient inversion in the case of a moderate number of observations as encountered in stationary inversion, where the cost of assembling Hz is in

  7. Optimized Field Sampling and Monitoring of Airborne Hazardous Transport Plumes; A Geostatistical Simulation Approach

    Energy Technology Data Exchange (ETDEWEB)

    Chen, DI-WEN

    2001-11-21

    Airborne hazardous plumes inadvertently released during nuclear/chemical/biological incidents are mostly of unknown composition and concentration until measurements are taken of post-accident ground concentrations from plume-ground deposition of constituents. Unfortunately, measurements often are days post-incident and rely on hazardous manned air-vehicle measurements. Before this happens, computational plume migration models are the only source of information on the plume characteristics, constituents, concentrations, directions of travel, ground deposition, etc. A mobile ''lighter than air'' (LTA) system is being developed at Oak Ridge National Laboratory that will be part of the first response in emergency conditions. These interactive and remote unmanned air vehicles will carry light-weight detectors and weather instrumentation to measure the conditions during and after plume release. This requires a cooperative computationally organized, GPS-controlled set of LTA's that self-coordinate around the objectives in an emergency situation in restricted time frames. A critical step before an optimum and cost-effective field sampling and monitoring program proceeds is the collection of data that provides statistically significant information, collected in a reliable and expeditious manner. Efficient aerial arrangements of the detectors taking the data (for active airborne release conditions) are necessary for plume identification, computational 3-dimensional reconstruction, and source distribution functions. This report describes the application of stochastic or geostatistical simulations to delineate the plume for guiding subsequent sampling and monitoring designs. A case study is presented of building digital plume images, based on existing ''hard'' experimental data and ''soft'' preliminary transport modeling results of Prairie Grass Trials Site. Markov Bayes Simulation, a coupled Bayesian/geostatistical

  8. Factors affecting paddy soil arsenic concentration in Bangladesh: prediction and uncertainty of geostatistical risk mapping.

    Science.gov (United States)

    Ahmed, Zia U; Panaullah, Golam M; DeGloria, Stephen D; Duxbury, John M

    2011-12-15

    Knowledge of the spatial correlation of soil arsenic (As) concentrations with environmental variables is needed to assess the nature and extent of the risk of As contamination from irrigation water in Bangladesh. We analyzed 263 paired groundwater and paddy soil samples covering highland (HL) and medium highland-1 (MHL-1) land types for geostatistical mapping of soil As and delineation of As contaminated areas in Tala Upazilla, Satkhira district. We also collected 74 non-rice soil samples to assess the baseline concentration of soil As for this area. The mean soil As concentrations (mg/kg) for different land types under rice and non-rice crops were: rice-MHL-1 (21.2)>rice-HL (14.1)>non-rice-MHL-1 (11.9)>non-rice-HL (7.2). Multiple regression analyses showed that irrigation water As, Fe, land elevation and years of tubewell operation are the important factors affecting the concentrations of As in HL paddy soils. Only years of tubewell operation affected As concentration in the MHL-1 paddy soils. Quantitatively similar increases in soil As above the estimated baseline-As concentration were observed for rice soils on HL and MHL-1 after 6-8 years of groundwater irrigation, implying strong retention of As added in irrigation water in both land types. Application of single geostatistical methods with secondary variables such as regression kriging (RK) and ordinary co-kriging (OCK) gave little improvement in prediction of soil As over ordinary kriging (OK). Comparing single prediction methods, kriging within strata (KWS), the combination of RK for HL and OCK for MHL-1, gave more accurate soil As predictions and showed the lowest misclassification of declaring a location "contaminated" with respect to 14.8 mg As/kg, the highest value obtained for the baseline soil As concentration. Prediction of soil As buildup over time indicated that 75% or the soils cropped to rice would contain at least 30 mg/L As by the year 2020. Copyright © 2011 Elsevier B.V. All rights reserved.

  9. Indoor terrestrial gamma dose rate mapping in France: a case study using two different geostatistical models.

    Science.gov (United States)

    Warnery, E; Ielsch, G; Lajaunie, C; Cale, E; Wackernagel, H; Debayle, C; Guillevic, J

    2015-01-01

    Terrestrial gamma dose rates show important spatial variations in France. Previous studies resulted in maps of arithmetic means of indoor terrestrial gamma dose rates by "departement" (French district). However, numerous areas could not be characterized due to the lack of data. The aim of our work was to obtain more precise estimates of the spatial variability of indoor terrestrial gamma dose rates in France by using a more recent and complete data base and geostatistics. The study was based on the exploitation of 97,595 measurements results distributed in 17,404 locations covering all of France. Measurements were done by the Institute for Radioprotection and Nuclear Safety (IRSN) using RPL (Radio Photo Luminescent) dosimeters, exposed during several months between years 2011 and 2012 in French dentist surgeries and veterinary clinics. The data used came from dosimeters which were not exposed to anthropic sources. After removing the cosmic rays contribution in order to study only the telluric gamma radiation, it was decided to work with the arithmetic means of the time-series measurements, weighted by the time-exposure of the dosimeters, for each location. The values varied between 13 and 349 nSv/h, with an arithmetic mean of 76 nSv/h. The observed statistical distribution of the gamma dose rates was skewed to the right. Firstly, ordinary kriging was performed in order to predict the gamma dose rate on cells of 1*1 km(2), all over the domain. The second step of the study was to use an auxiliary variable in estimates. The IRSN achieved in 2010 a classification of the French geological formations, characterizing their uranium potential on the bases of geology and local measurement results of rocks uranium content. This information is georeferenced in a map at the scale 1:1,000,000. The geological uranium potential (GUP) was classified in 5 qualitative categories. As telluric gamma rays mostly come from the progenies of the (238)Uranium series present in rocks, this

  10. Integration of dynamical data in a geostatistical model of reservoir; Integration des donnees dynamiques dans un modele geostatistique de reservoir

    Energy Technology Data Exchange (ETDEWEB)

    Costa Reis, L.

    2001-01-01

    We have developed in this thesis a methodology of integrated characterization of heterogeneous reservoirs, from geologic modeling to history matching. This methodology is applied to the reservoir PBR, situated in Campos Basin, offshore Brazil, which has been producing since June 1979. This work is an extension of two other thesis concerning geologic and geostatistical modeling of the reservoir PBR from well data and seismic information. We extended the geostatistical litho-type model to the whole reservoir by using a particular approach of the non-stationary truncated Gaussian simulation method. This approach facilitated the application of the gradual deformation method to history matching. The main stages of the methodology for dynamic data integration in a geostatistical reservoir model are presented. We constructed a reservoir model and the initial difficulties in the history matching led us to modify some choices in the geological, geostatistical and flow models. These difficulties show the importance of dynamic data integration in reservoir modeling. The petrophysical property assignment within the litho-types was done by using well test data. We used an inversion procedure to evaluate the petrophysical parameters of the litho-types. The up-scaling is a necessary stage to reduce the flow simulation time. We compared several up-scaling methods and we show that the passage from the fine geostatistical model to the coarse flow model should be done very carefully. The choice of the fitting parameter depends on the objective of the study. In the case of the reservoir PBR, where water is injected in order to improve the oil recovery, the water rate of the producing wells is directly related to the reservoir heterogeneity. Thus, the water rate was chosen as the fitting parameter. We obtained significant improvements in the history matching of the reservoir PBR. First, by using a method we have proposed, called patchwork. This method allows us to built a coherent

  11. The importance of geostatistics in pyschical geographyFiziki coğrafyada jeoistatistiğin önemi

    Directory of Open Access Journals (Sweden)

    Olgu Aydın

    2015-11-01

    Full Text Available Geostatistic in geographical science is an important method used to consistently determine the spatial variation of an event. Geostatistics look at where the geographical variables take place, i.e. the location, the spatial interaction and the effects of geographical variables affecting the distribution of variables at the location. In short, geostatistics are interested in the spatial organization of the related research subject. Therefore, it has an important place in the geographical study of events that occured in geographical space with the aid of geostatistical techniques. The aim of this study is to provide a general look at the basic concepts and techniques of geostatistics as a part of applications to physical geography studies using a case study.   Özet Coğrafya biliminde jeoistatistik, bir olayın mekânsal değişkenliğini tutarlı bir şekilde ortaya koyabilmek için kullanılan önemli bir yöntemdir. Jeoistatistik, coğrafi değişkenlerin nerede yer aldığı, yani lokasyonu, değişkenlerin mekânsal etkileşimi ve değişkenlerin bulunduğu alanda dağılımlarını belirleyen diğer coğrafi değişkenlerin etkilerini inceler. Kısaca jeoistatistik, ilgili olduğu konuya ait sistemin mekânsal organizasyonu ile ilgilenmektedir. Bu nedenle coğrafi mekânda meydana gelen olayların jeoistatistik teknikleri yardımıyla araştırılması coğrafya çalışmalarında önemli bir yer tutmaktadır. Bu çalışmanın amacı jeoistatistik tekniklerini fiziki coğrafya uygulamaları açısından kısa bir literatür dâhilinde gözden geçirerek, temel kavram ve teknikler açısından genel bir bakış açısı sağlamaktır.

  12. Epidemiological study of hazelnut bacterial blight in central Italy by using laboratory analysis and geostatistics.

    Directory of Open Access Journals (Sweden)

    Jay Ram Lamichhane

    Full Text Available Incidence of Xanthomonas arboricola pv. corylina, the causal agent of hazelnut bacterial blight, was analyzed spatially in relation to the pedoclimatic factors. Hazelnut grown in twelve municipalities situated in the province of Viterbo, central Italy was studied. A consistent number of bacterial isolates were obtained from the infected tissues of hazelnut collected in three years (2010-2012. The isolates, characterized by phenotypic tests, did not show any difference among them. Spatial patterns of pedoclimatic data, analyzed by geostatistics showed a strong positive correlation of disease incidence with higher values of rainfall, thermal shock and soil nitrogen; a weak positive correlation with soil aluminium content and a strong negative correlation with the values of Mg/K ratio. No correlation of the disease incidence was found with soil pH. Disease incidence ranged from very low (<1% to very high (almost 75% across the orchards. Young plants (4-year old were the most affected by the disease confirming a weak negative correlation of the disease incidence with plant age. Plant cultivars did not show any difference in susceptibility to the pathogen. Possible role of climate change on the epidemiology of the disease is discussed. Improved management practices are recommended for effective control of the disease.

  13. Assessing TCE source bioremediation by geostatistical analysis of a flux fence.

    Science.gov (United States)

    Cai, Zuansi; Wilson, Ryan D; Lerner, David N

    2012-01-01

    Mass discharge across transect planes is increasingly used as a metric for performance assessment of in situ groundwater remediation systems. Mass discharge estimates using concentrations measured in multilevel transects are often made by assuming a uniform flow field, and uncertainty contributions from spatial concentration and flow field variability are often overlooked. We extend our recently developed geostatistical approach to estimate mass discharge using transect data of concentration and hydraulic conductivity, so accounting for the spatial variability of both datasets. The magnitude and uncertainty of mass discharge were quantified by conditional simulation. An important benefit of the approach is that uncertainty is quantified as an integral part of the mass discharge estimate. We use this approach for performance assessment of a bioremediation experiment of a trichloroethene (TCE) source zone. Analyses of dissolved parent and daughter compounds demonstrated that the engineered bioremediation has elevated the degradation rate of TCE, resulting in a two-thirds reduction in the TCE mass discharge from the source zone. The biologically enhanced dissolution of TCE was not significant (~5%), and was less than expected. However, the discharges of the daughter products cis-1,2, dichloroethene (cDCE) and vinyl chloride (VC) increased, probably because of the rapid transformation of TCE from the source zone to the measurement transect. This suggests that enhancing the biodegradation of cDCE and VC will be crucial to successful engineered bioremediation of TCE source zones.

  14. Geostatistical Procedures for Developing Three-Dimensional Aquifer Models from Drillers' Logs

    Science.gov (United States)

    Bohling, G.; Helm, C.

    2013-12-01

    The Hydrostratigraphic Drilling Record Assessment (HyDRA) project is developing procedures for employing the vast but highly qualitative hydrostratigraphic information contained in drillers' logs in the development of quantitative three-dimensional (3D) depictions of subsurface properties for use in flow and transport models to support groundwater management practices. One of the project's objectives is to develop protocols for 3D interpolation of lithological data from drillers' logs, properly accounting for the categorical nature of these data. This poster describes the geostatistical procedures developed to accomplish this objective. Using a translation table currently containing over 62,000 unique sediment descriptions encountered during the transcription of over 15,000 logs in the Kansas High Plains aquifer, the sediment descriptions are translated into 71 standardized terms, which are then mapped into a small number of categories associated with different representative property (e.g., hydraulic conductivity [K]) values. Each log is partitioned into regular intervals and the proportion of each K category within each interval is computed. To properly account for their compositional nature, a logratio transform is applied to the proportions. The transformed values are then kriged to the 3D model grid and backtransformed to determine the proportion of each category within each model cell. Various summary measures can then be computed from the proportions, including a proportion-weighted average K and an entropy measure representing the degree of mixing of categories within each cell. We also describe a related cross-validation procedure for assessing log quality.

  15. Geostatistical methods for rock mass quality prediction using borehole and geophysical survey data

    Science.gov (United States)

    Chen, J.; Rubin, Y.; Sege, J. E.; Li, X.; Hehua, Z.

    2015-12-01

    For long, deep tunnels, the number of geotechnical borehole investigations during the preconstruction stage is generally limited. Yet tunnels are often constructed in geological structures with complex geometries, and in which the rock mass is fragmented from past structural deformations. Tunnel Geology Prediction (TGP) is a geophysical technique widely used during tunnel construction in China to ensure safety during construction and to prevent geological disasters. In this paper, geostatistical techniques were applied in order to integrate seismic velocity from TGP and borehole information into spatial predictions of RMR (Rock Mass Rating) in unexcavated areas. This approach is intended to apply conditional probability methods to transform seismic velocities to directly observed RMR values. The initial spatial distribution of RMR, inferred from the boreholes, was updated by including geophysical survey data in a co-kriging approach. The method applied to a real tunnel project shows significant improvements in rock mass quality predictions after including geophysical survey data, leading to better decision-making for construction safety design.

  16. Applying Geostatistical Analysis to Crime Data: Car-Related Thefts in the Baltic States

    Science.gov (United States)

    Kerry, Ruth; Goovaerts, Pierre; Haining, Robert P.; Ceccato, Vania

    2011-01-01

    Geostatistical methods have rarely been applied to area-level offense data. This article demonstrates their potential for improving the interpretation and understanding of crime patterns using previously analyzed data about car-related thefts for Estonia, Latvia, and Lithuania in 2000. The variogram is used to inform about the scales of variation in offense, social, and economic data. Area-to-area and area-to-point Poisson kriging are used to filter the noise caused by the small number problem. The latter is also used to produce continuous maps of the estimated crime risk (expected number of crimes per 10,000 habitants), thereby reducing the visual bias of large spatial units. In seeking to detect the most likely crime clusters, the uncertainty attached to crime risk estimates is handled through a local cluster analysis using stochastic simulation. Factorial kriging analysis is used to estimate the local- and regional-scale spatial components of the crime risk and explanatory variables. Then regression modeling is used to determine which factors are associated with the risk of car-related theft at different scales. PMID:22190762

  17. Delineation of estuarine management areas using multivariate geostatistics: the case of Sado Estuary.

    Science.gov (United States)

    Caeiro, Sandra; Goovaerts, Pierre; Painho, Marco; Costa, M Helena

    2003-09-15

    The Sado Estuary is a coastal zone located in the south of Portugal where conflicts between conservation and development exist because of its location near industrialized urban zones and its designation as a natural reserve. The aim of this paper is to evaluate a set of multivariate geostatistical approaches to delineate spatially contiguous regions of sediment structure for Sado Estuary. These areas will be the supporting infrastructure of an environmental management system for this estuary. The boundaries of each homogeneous area were derived from three sediment characterization attributes through three different approaches: (1) cluster analysis of dissimilarity matrix function of geographical separation followed by indicator kriging of the cluster data, (2) discriminant analysis of kriged values of the three sediment attributes, and (3) a combination of methods 1 and 2. Final maximum likelihood classification was integrated into a geographical information system. All methods generated fairly spatially contiguous management areas that reproduce well the environment of the estuary. Map comparison techniques based on kappa statistics showed thatthe resultant three maps are similar, supporting the choice of any of the methods as appropriate for management of the Sado Estuary. However, the results of method 1 seem to be in better agreement with estuary behavior, assessment of contamination sources, and previous work conducted at this site.

  18. Epidemiological study of hazelnut bacterial blight in central Italy by using laboratory analysis and geostatistics.

    Science.gov (United States)

    Lamichhane, Jay Ram; Fabi, Alfredo; Ridolfi, Roberto; Varvaro, Leonardo

    2013-01-01

    Incidence of Xanthomonas arboricola pv. corylina, the causal agent of hazelnut bacterial blight, was analyzed spatially in relation to the pedoclimatic factors. Hazelnut grown in twelve municipalities situated in the province of Viterbo, central Italy was studied. A consistent number of bacterial isolates were obtained from the infected tissues of hazelnut collected in three years (2010-2012). The isolates, characterized by phenotypic tests, did not show any difference among them. Spatial patterns of pedoclimatic data, analyzed by geostatistics showed a strong positive correlation of disease incidence with higher values of rainfall, thermal shock and soil nitrogen; a weak positive correlation with soil aluminium content and a strong negative correlation with the values of Mg/K ratio. No correlation of the disease incidence was found with soil pH. Disease incidence ranged from very low (<1%) to very high (almost 75%) across the orchards. Young plants (4-year old) were the most affected by the disease confirming a weak negative correlation of the disease incidence with plant age. Plant cultivars did not show any difference in susceptibility to the pathogen. Possible role of climate change on the epidemiology of the disease is discussed. Improved management practices are recommended for effective control of the disease.

  19. Geostatistics and the representative elementary volume of gamma ray tomography attenuation in rocks cores

    Science.gov (United States)

    Vogel, J.R.; Brown, G.O.

    2003-01-01

    Semivariograms of samples of Culebra Dolomite have been determined at two different resolutions for gamma ray computed tomography images. By fitting models to semivariograms, small-scale and large-scale correlation lengths are determined for four samples. Different semivariogram parameters were found for adjacent cores at both resolutions. Relative elementary volume (REV) concepts are related to the stationarity of the sample. A scale disparity factor is defined and is used to determine sample size required for ergodic stationarity with a specified correlation length. This allows for comparison of geostatistical measures and representative elementary volumes. The modifiable areal unit problem is also addressed and used to determine resolution effects on correlation lengths. By changing resolution, a range of correlation lengths can be determined for the same sample. Comparison of voxel volume to the best-fit model correlation length of a single sample at different resolutions reveals a linear scaling effect. Using this relationship, the range of the point value semivariogram is determined. This is the range approached as the voxel size goes to zero. Finally, these results are compared to the regularization theory of point variables for borehole cores and are found to be a better fit for predicting the volume-averaged range.

  20. Using Predictions Based on Geostatistics to Monitor Trends in Aspergillus flavus Strain Composition.

    Science.gov (United States)

    Orum, T V; Bigelow, D M; Cotty, P J; Nelson, M R

    1999-09-01

    ABSTRACT Aspergillus flavus is a soil-inhabiting fungus that frequently produces aflatoxins, potent carcinogens, in cottonseed and other seed crops. A. flavus S strain isolates, characterized on the basis of sclerotial morphology, are highly toxigenic. Spatial and temporal characteristics of the percentage of the A. flavus isolates that are S strain (S strain incidence) were used to predict patterns across areas of more than 30 km(2). Spatial autocorrelation in S strain incidence in Yuma County, AZ, was shown to extend beyond field boundaries to adjacent fields. Variograms revealed both short-range (2 to 6 km) and long-range (20 to 30 km) spatial structure in S strain incidence. S strain incidence at 36 locations sampled in July 1997 was predicted with a high correlation between expected and observed values (R = 0.85, P = 0.0001) by kriging data from July 1995 and July 1996. S strain incidence at locations sampled in October 1997 and March 1998 was markedly less than predicted by kriging data from the same months in prior years. Temporal analysis of four locations repeatedly sampled from April 1995 through July 1998 also indicated a major reduction in S strain incidence in the Texas Hill area after July 1997. Surface maps generated by kriging point data indicated a similarity in the spatial pattern of S strain incidence among all sampling dates despite temporal changes in the overall S strain incidence. Geostatistics provided useful descriptions of variability in S strain incidence over space and time.

  1. Spatial variability of selected physicochemical parameters within peat deposits in small valley mire: a geostatistical approach

    Directory of Open Access Journals (Sweden)

    Pawłowski Dominik

    2014-12-01

    Full Text Available Geostatistical methods for 2D and 3D modelling spatial variability of selected physicochemical properties of biogenic sediments were applied to a small valley mire in order to identify the processes that lead to the formation of various types of peat. A sequential Gaussian simulation was performed to reproduce the statistical distribution of the input data (pH and organic matter and their semivariances, as well as to honouring of data values, yielding more ‘realistic’ models that show microscale spatial variability, despite the fact that the input sample cores were sparsely distributed in the X-Y space of the study area. The stratigraphy of peat deposits in the Ldzań mire shows a record of long-term evolution of water conditions, which is associated with the variability in water supply over time. Ldzań is a fen (a rheotrophic mire with a through-flow of groundwater. Additionally, the vicinity of the Grabia River is marked by seasonal inundations of the southwest part of the mire and increased participation of mineral matter in the peat. In turn, the upper peat layers of some of the central part of Ldzań mire are rather spongy, and these peat-forming phytocoenoses probably formed during permanent waterlogging.

  2. Integrating Ensemble Data Assimilation and Indicator Geostatistics to Delineate Hydrofacies Spatial Distribution

    Science.gov (United States)

    Song, X.; Chen, X.; Ye, M.; Dai, Z.; Hammond, G. E.

    2015-12-01

    We present a new framework for delineating spatial distributions of hydrofacies from indirect data by linking ensemble-based data assimilation method (e.g., Ensemble Kalman filter, EnKF) with indicator geostatistics based on transition probability. The nature of ensemble data assimilation makes the framework efficient and flexible to integrate various types of observation data. We leveraged the level set concept to establish transformations between discrete hydrofacies and continuous variables, which is a critical element to implement ensemble data assimilation methods for hydrofacies delineation. T-PROGS is used to generate realizations of hydrofacies fields given conditioning points. An additional quenching step of T-PROGS is taken to preserve spatial structure of updated hydrofacies after each data assimilation step. This new method is illustrated by a two-dimensional (2-D) synthetic study in which transient hydraulic head data resulting from pumping is assimilated to delineate hydrofacies distribution. Our results showed that the proposed framework was able to characterize hydrofacies distribution and their associated permeability with adequate accuracy even with limited direct hydrofacies data. This method may find broader applications in facies delineation using other types of indirect measurements, such as tracer tests and geophysical surveys.

  3. The geostatistical approach for structural and stratigraphic framework analysis of offshore NW Bonaparte Basin, Australia

    Science.gov (United States)

    Wahid, Ali; Salim, Ahmed Mohamed Ahmed; Gaafar, Gamal Ragab; Yusoff, Wan Ismail Wan

    2016-02-01

    Geostatistics or statistical approach is based on the studies of temporal and spatial trend, which depend upon spatial relationships to model known information of variable(s) at unsampled locations. The statistical technique known as kriging was used for petrophycial and facies analysis, which help to assume spatial relationship to model the geological continuity between the known data and the unknown to produce a single best guess of the unknown. Kriging is also known as optimal interpolation technique, which facilitate to generate best linear unbiased estimation of each horizon. The idea is to construct a numerical model of the lithofacies and rock properties that honor available data and further integrate with interpreting seismic sections, techtonostratigraphy chart with sea level curve (short term) and regional tectonics of the study area to find the structural and stratigraphic growth history of the NW Bonaparte Basin. By using kriging technique the models were built which help to estimate different parameters like horizons, facies, and porosities in the study area. The variograms were used to determine for identification of spatial relationship between data which help to find the depositional history of the North West (NW) Bonaparte Basin.

  4. [Geostatistics analyzing to cause of formation of circle distribution of plant communities in Horqin Sandy Land].

    Science.gov (United States)

    He, Xingdong; Gao, Yubao; Zhao, Wenzhi; Cong, Zili

    2004-09-01

    Investigation results in the present study showed that plant communities took typical concentric circles distribution patterns along habitat gradient from top, slope to interdune on a few large fixed dunes in middle part of Korqin Sandy Land. In order to explain this phenomenon, analysis of water content and its spatial heterogeneity in sand layers on different locations of dunes was conducted. In these dunes, water contents in sand layers of the tops were lower than those of the slopes; both of them were lower than those of the interdunes. According to the results of geostatistics analysis, whether shifting dune or fixed dune, spatial heterogeneity of water contents in sand layers took on regular changes, such as ratios between nugget and sill and ranges reduced gradually, fractal dimension increased gradually, the regular changes of these parameters indicated that random spatial heterogeneity reduced gradually, and autocorrelation spatial heterogeneity increased gradually from the top, the slope to the interdune. The regular changes of water contents in sand layers and their spatial heterogeneity of different locations of the dunes, thus, might be an important cause resulted in the formation of the concentric circles patterns of the plant communities on these fixed dunes.

  5. Geostatistical investigations for suitable mapping of the water table: the Bordeaux case (France)

    Science.gov (United States)

    Guekie simo, Aubin Thibaut; Marache, Antoine; Lastennet, Roland; Breysse, Denys

    2016-02-01

    Methodologies have been developed to establish realistic water-table maps using geostatistical methods: ordinary kriging (OK), cokriging (CoK), collocated cokriging (CoCoK), and kriging with external drift (KED). In fact, in a hilly terrain, when piezometric data are sparsely distributed over large areas, the water-table maps obtained by these methods provide exact water levels at monitoring wells but fail to represent the groundwater flow system, manifested through an interpolated water table above the topography. A methodology is developed in order to rebuild water-table maps for urban areas at the city scale. The interpolation methodology is presented and applied in a case study where water levels are monitored at a set of 47 points for a part urban domain covering 25.6 km2 close to Bordeaux city, France. To select the best method, a geographic information system was used to visualize surfaces reconstructed with each method. A cross-validation was carried out to evaluate the predictive performances of each kriging method. KED proves to be the most accurate and yields a better description of the local fluctuations induced by the topography (natural occurrence of ridges and valleys).

  6. Usage of multivariate geostatistics in interpolation processes for meteorological precipitation maps

    Science.gov (United States)

    Gundogdu, Ismail Bulent

    2017-01-01

    Long-term meteorological data are very important both for the evaluation of meteorological events and for the analysis of their effects on the environment. Prediction maps which are constructed by different interpolation techniques often provide explanatory information. Conventional techniques, such as surface spline fitting, global and local polynomial models, and inverse distance weighting may not be adequate. Multivariate geostatistical methods can be more significant, especially when studying secondary variables, because secondary variables might directly affect the precision of prediction. In this study, the mean annual and mean monthly precipitations from 1984 to 2014 for 268 meteorological stations in Turkey have been used to construct country-wide maps. Besides linear regression, the inverse square distance and ordinary co-Kriging (OCK) have been used and compared to each other. Also elevation, slope, and aspect data for each station have been taken into account as secondary variables, whose use has reduced errors by up to a factor of three. OCK gave the smallest errors (1.002 cm) when aspect was included.

  7. Spatial analysis of lettuce downy mildew using geostatistics and geographic information systems.

    Science.gov (United States)

    Wu, B M; van Bruggen, A H; Subbarao, K V; Pennings, G G

    2001-02-01

    ABSTRACT The epidemiology of lettuce downy mildew has been investigated extensively in coastal California. However, the spatial patterns of the disease and the distance that Bremia lactucae spores can be transported have not been determined. During 1995 to 1998, we conducted several field- and valley-scale surveys to determine spatial patterns of this disease in the Salinas valley. Geostatistical analyses of the survey data at both scales showed that the influence range of downy mildew incidence at one location on incidence at other locations was between 80 and 3,000 m. A linear relationship was detected between semivariance and lag distance at the field scale, although no single statistical model could fit the semi-variograms at the valley scale. Spatial interpolation by the inverse distance weighting method with a power of 2 resulted in plausible estimates of incidence throughout the valley. Cluster analysis in geographic information systems on the interpolated disease incidence from different dates demonstrated that the Salinas valley could be divided into two areas, north and south of Salinas City, with high and low disease pressure, respectively. Seasonal and spatial trends along the valley suggested that the distinction between the downy mildew conducive and nonconducive areas might be determined by environmental factors.

  8. Stochastic simulation of geological data using isometric mapping and multiple-point geostatistics with data incorporation

    Science.gov (United States)

    Zhang, Ting; Du, Yi; Huang, Tao; Li, Xue

    2016-02-01

    Constrained by current hardware equipment and techniques, acquisition of geological data sometimes is difficult or even impossible. Stochastic simulation for geological data is helpful to address this issue, providing multiple possible results of geological data for resource prediction and risk evaluation. Multiple-point geostatistics (MPS) being one of the main branches of stochastic simulation can extract the intrinsic features of patterns from training images (TIs) that provide prior information to limit the under-determined simulated results, and then copy them to the simulated regions. Because the generated models from TIs are not always linear, some MPS methods using linear dimensionality reduction are not suitable to deal with nonlinear models of TIs. A new MPS method named ISOMAPSIM was proposed to resolve this issue, which reduces the dimensionality of patterns from TIs using isometric mapping (ISOMAP) and then classifies these low-dimensional patterns for simulation. Since conditional models including hard data and soft data influence the simulated results greatly, this paper further studies ISOMAPSIM using hard data and soft data to obtain more accurate simulations for geological modeling. Stochastic simulation of geological data is processed respectively under several conditions according to different situations of conditional models. The tests show that the proposed method can reproduce the structural characteristics of TIs under all conditions, but the condition using soft data and hard data together performs best in simulation quality; moreover, the proposed method shows its advantages over other MPS methods that use linear dimensionality reduction.

  9. The applications of model-based geostatistics in helminth epidemiology and control.

    Science.gov (United States)

    Magalhães, Ricardo J Soares; Clements, Archie C A; Patil, Anand P; Gething, Peter W; Brooker, Simon

    2011-01-01

    Funding agencies are dedicating substantial resources to tackle helminth infections. Reliable maps of the distribution of helminth infection can assist these efforts by targeting control resources to areas of greatest need. The ability to define the distribution of infection at regional, national and subnational levels has been enhanced greatly by the increased availability of good quality survey data and the use of model-based geostatistics (MBG), enabling spatial prediction in unsampled locations. A major advantage of MBG risk mapping approaches is that they provide a flexible statistical platform for handling and representing different sources of uncertainty, providing plausible and robust information on the spatial distribution of infections to inform the design and implementation of control programmes. Focussing on schistosomiasis and soil-transmitted helminthiasis, with additional examples for lymphatic filariasis and onchocerciasis, we review the progress made to date with the application of MBG tools in large-scale, real-world control programmes and propose a general framework for their application to inform integrative spatial planning of helminth disease control programmes.

  10. Geostatistics as a tool to study mite dispersion in physic nut plantations.

    Science.gov (United States)

    Rosado, J F; Picanço, M C; Sarmento, R A; Pereira, R M; Pedro-Neto, M; Galdino, T V S; de Sousa Saraiva, A; Erasmo, E A L

    2015-08-01

    Spatial distribution studies in pest management identify the locations where pest attacks on crops are most severe, enabling us to understand and predict the movement of such pests. Studies on the spatial distribution of two mite species, however, are rather scarce. The mites Polyphagotarsonemus latus and Tetranychus bastosi are the major pests affecting physic nut plantations (Jatropha curcas). Therefore, the objective of this study was to measure the spatial distributions of P. latus and T. bastosi in the physic nut plantations. Mite densities were monitored over 2 years in two different plantations. Sample locations were georeferenced. The experimental data were analyzed using geostatistical analyses. The total mite density was found to be higher when only one species was present (T. bastosi). When both the mite species were found in the same plantation, their peak densities occurred at different times. These mites, however, exhibited uniform spatial distribution when found at extreme densities (low or high). However, the mites showed an aggregated distribution in intermediate densities. Mite spatial distribution models were isotropic. Mite colonization commenced at the periphery of the areas under study, whereas the high-density patches extended until they reached 30 m in diameter. This has not been reported for J. curcas plants before.

  11. Study on the spatial pattern of rainfall erosivity based on geostatistics in Hebei Province,China

    Institute of Scientific and Technical Information of China (English)

    Mingxin MEN; Zhenrong YU; Hao XU

    2008-01-01

    The objective of this article was to study the spatial distribution pattern of rainfall erosivity.The precipitation data at each climatological station in Hebei Province,China were collected and analyzed and modeled with SPSS and ArcGIS.A simple model of estimating rainfall erosivity was developed based on the weather station data.Also,the annual average rainfall erosivity was calculated with this model.The predicted errors,statistical feature values and prediction maps obtained by using different interpolation methods were compared.The result indicated that second-order ordinary Kriging method performed better than both zero and first-order ordinary Kriging methods.Within the method of second-order trend,Gaussian semi-variogram model performed better than other interpolation methods with the spherical or exponential models.Applying geostatistics to study rainfall erosivity spatial pattern will help to accurately and quantitatively evaluate soil erosion risk.Our research also provides digital maps that can assist in policy making in the regional soil and water conservation planning and management strategies.

  12. Geostatistical analysis of tritium, groundwater age and other noble gas derived parameters in California.

    Science.gov (United States)

    Visser, A; Moran, J E; Hillegonds, Darren; Singleton, M J; Kulongoski, Justin T; Belitz, Kenneth; Esser, B K

    2016-03-15

    Key characteristics of California groundwater systems related to aquifer vulnerability, sustainability, recharge locations and mechanisms, and anthropogenic impact on recharge are revealed in a spatial geostatistical analysis of a unique data set of tritium, noble gases and other isotopic analyses unprecedented in size at nearly 4000 samples. The correlation length of key groundwater residence time parameters varies between tens of kilometers ((3)H; age) to the order of a hundred kilometers ((4)Heter; (14)C; (3)Hetrit). The correlation length of parameters related to climate, topography and atmospheric processes is on the order of several hundred kilometers (recharge temperature; δ(18)O). Young groundwater ages that highlight regional recharge areas are located in the eastern San Joaquin Valley, in the southern Santa Clara Valley Basin, in the upper LA basin and along unlined canals carrying Colorado River water, showing that much of the recent recharge in central and southern California is dominated by river recharge and managed aquifer recharge. Modern groundwater is found in wells with the top open intervals below 60 m depth in the southeastern San Joaquin Valley, Santa Clara Valley and Los Angeles basin, as the result of intensive pumping and/or managed aquifer recharge operations. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Geostatistics for Mapping Leaf Area Index over a Cropland Landscape: Efficiency Sampling Assessment

    Directory of Open Access Journals (Sweden)

    Javier Garcia-Haro

    2010-11-01

    Full Text Available This paper evaluates the performance of spatial methods to estimate leaf area index (LAI fields from ground-based measurements at high-spatial resolution over a cropland landscape. Three geostatistical model variants of the kriging technique, the ordinary kriging (OK, the collocated cokriging (CKC and kriging with an external drift (KED are used. The study focused on the influence of the spatial sampling protocol, auxiliary information, and spatial resolution in the estimates. The main advantage of these models lies in the possibility of considering the spatial dependence of the data and, in the case of the KED and CKC, the auxiliary information for each location used for prediction purposes. A high-resolution NDVI image computed from SPOT TOA reflectance data is used as an auxiliary variable in LAI predictions. The CKC and KED predictions have proven the relevance of the auxiliary information to reproduce the spatial pattern at local scales, proving the KED model to be the best estimator when a non-stationary trend is observed. Advantages and limitations of the methods in LAI field predictions for two systematic and two stratified spatial samplings are discussed for high (20 m, medium (300 m and coarse (1 km spatial scales. The KED has exhibited the best observed local accuracy for all the spatial samplings. Meanwhile, the OK model provides comparable results when a well stratified sampling scheme is considered by land cover.

  14. Integrating address geocoding, land use regression, and spatiotemporal geostatistical estimation for groundwater tetrachloroethylene.

    Science.gov (United States)

    Messier, Kyle P; Akita, Yasuyuki; Serre, Marc L

    2012-03-06

    Geographic information systems (GIS) based techniques are cost-effective and efficient methods used by state agencies and epidemiology researchers for estimating concentration and exposure. However, budget limitations have made statewide assessments of contamination difficult, especially in groundwater media. Many studies have implemented address geocoding, land use regression, and geostatistics independently, but this is the first to examine the benefits of integrating these GIS techniques to address the need of statewide exposure assessments. A novel framework for concentration exposure is introduced that integrates address geocoding, land use regression (LUR), below detect data modeling, and Bayesian Maximum Entropy (BME). A LUR model was developed for tetrachloroethylene that accounts for point sources and flow direction. We then integrate the LUR model into the BME method as a mean trend while also modeling below detects data as a truncated Gaussian probability distribution function. We increase available PCE data 4.7 times from previously available databases through multistage geocoding. The LUR model shows significant influence of dry cleaners at short ranges. The integration of the LUR model as mean trend in BME results in a 7.5% decrease in cross validation mean square error compared to BME with a constant mean trend.

  15. Comparison of geostatistical kriging algorithms for intertidal surface sediment facies mapping with grain size data.

    Science.gov (United States)

    Park, No-Wook; Jang, Dong-Ho

    2014-01-01

    This paper compares the predictive performance of different geostatistical kriging algorithms for intertidal surface sediment facies mapping using grain size data. Indicator kriging, which maps facies types from conditional probabilities of predefined facies types, is first considered. In the second approach, grain size fractions are first predicted using cokriging and the facies types are then mapped. As grain size fractions are compositional data, their characteristics should be considered during spatial prediction. For efficient prediction of compositional data, additive log-ratio transformation is applied before cokriging analysis. The predictive performance of cokriging of the transformed variables is compared with that of cokriging of raw fractions in terms of both prediction errors of fractions and facies mapping accuracy. From a case study of the Baramarae tidal flat, Korea, the mapping method based on cokriging of log-ratio transformation of fractions outperformed the one based on cokriging of untransformed fractions in the prediction of fractions and produced the best facies mapping accuracy. Indicator kriging that could not account for the variation of fractions within each facies type showed the worst mapping accuracy. These case study results indicate that the proper processing of grain size fractions as compositional data is important for reliable facies mapping.

  16. Comparison of Geostatistical Kriging Algorithms for Intertidal Surface Sediment Facies Mapping with Grain Size Data

    Directory of Open Access Journals (Sweden)

    No-Wook Park

    2014-01-01

    Full Text Available This paper compares the predictive performance of different geostatistical kriging algorithms for intertidal surface sediment facies mapping using grain size data. Indicator kriging, which maps facies types from conditional probabilities of predefined facies types, is first considered. In the second approach, grain size fractions are first predicted using cokriging and the facies types are then mapped. As grain size fractions are compositional data, their characteristics should be considered during spatial prediction. For efficient prediction of compositional data, additive log-ratio transformation is applied before cokriging analysis. The predictive performance of cokriging of the transformed variables is compared with that of cokriging of raw fractions in terms of both prediction errors of fractions and facies mapping accuracy. From a case study of the Baramarae tidal flat, Korea, the mapping method based on cokriging of log-ratio transformation of fractions outperformed the one based on cokriging of untransformed fractions in the prediction of fractions and produced the best facies mapping accuracy. Indicator kriging that could not account for the variation of fractions within each facies type showed the worst mapping accuracy. These case study results indicate that the proper processing of grain size fractions as compositional data is important for reliable facies mapping.

  17. Radon risk mapping in southern Belgium: an application of geostatistical and GIS techniques.

    Science.gov (United States)

    Zh, H C; Charlet, J M; Poffijn, A

    2001-05-14

    A data set of long-term radon measurements in approximately 2200 houses in southern Belgium has been collected in an on-going national radon survey. The spatial variation of indoor Rn concentrations is modelled by variograms. A radon distribution map is produced using the log-normal kriging technique. A GIS is used to digitise, process and integrate a variety of data, including geological maps, Rn concentrations associated with house locations and an administrative map, etc. It also allows evaluation of the relationships between various spatial data sets with the goal of producing radon risk maps. Based on geostatistical mapping and spatial analysis, we define three categories of risk areas: high risk, medium risk and low risk area. The correlation between radon concentrations and geological features is proved in this study. High and medium Rn risk zones are dominantly situated in bedrock from the Cambrian to Lower Devonian, although a few medium risk zones are within the Jurassic. It is evident that high-risk zones are related to a strongly folded and fractured context.

  18. Fast Geostatistical Inversion using Randomized Matrix Decompositions and Sketchings for Heterogeneous Aquifer Characterization

    Science.gov (United States)

    O'Malley, D.; Le, E. B.; Vesselinov, V. V.

    2015-12-01

    We present a fast, scalable, and highly-implementable stochastic inverse method for characterization of aquifer heterogeneity. The method utilizes recent advances in randomized matrix algebra and exploits the structure of the Quasi-Linear Geostatistical Approach (QLGA), without requiring a structured grid like Fast-Fourier Transform (FFT) methods. The QLGA framework is a more stable version of Gauss-Newton iterates for a large number of unknown model parameters, but provides unbiased estimates. The methods are matrix-free and do not require derivatives or adjoints, and are thus ideal for complex models and black-box implementation. We also incorporate randomized least-square solvers and data-reduction methods, which speed up computation and simulate missing data points. The new inverse methodology is coded in Julia and implemented in the MADS computational framework (http://mads.lanl.gov). Julia is an advanced high-level scientific programing language that allows for efficient memory management and utilization of high-performance computational resources. Inversion results based on series of synthetic problems with steady-state and transient calibration data are presented.

  19. Unified Geostatistical Modeling for Data Fusion and Spatial Heteroskedasticity with R Package ramps

    Directory of Open Access Journals (Sweden)

    Brian J. Smith

    2008-03-01

    Full Text Available This article illustrates usage of the ramps R package, which implements the reparameterized and marginalized posterior sampling (RAMPS algorithm for complex Bayesian geostatistical models. The RAMPS methodology allows joint modeling of areal and point-source data arising from the same underlying spatial process. A reparametrization of variance parameters facilitates slice sampling based on simplexes, which can be useful in general when multiple variances are present. Prediction at arbitrary points can be made, which is critical in applications where maps are needed. Our implementation takes advantage of sparse matrix operations in the Matrix package and can provide substantial savings in computing time for large datasets. A user-friendly interface, similar to the nlme mixed effects models package, enables users to analyze datasets with little programming effort. Support is provided for numerous spatial and spatiotemporal correlation structures, user-defined correlation structures, and non-spatial random effects. The package features are illustrated via a synthetic dataset of spatially correlated observation distributed across the state of Iowa, USA.

  20. Geostatistical Approach to Find ‘Hotspots’ Where Biodiversity is at Risk in a Transition Country

    Directory of Open Access Journals (Sweden)

    Petrişor Alexandru-Ionuţ

    2014-10-01

    Full Text Available Global change‟ is a relatively recent concept, related to the energy - land use - climate change nexus, and designated to include all changes produced by the human species and the consequences of its activities over natural ecological complexes and biodiversity. The joint effects of these drivers of change are particularly relevant to understanding the changes of biodiversity. This study overlaps results of previous studies developed in Romania to find, explain and predict potential threats on biodiversity, including the effects of very high temperatures and low precipitations, urban sprawl and deforestation in order to identify „hotspots‟ of high risk for the loss of biodiversity using geostatistical tools. The results found two hotspots, one in the center and the other one in the south, and show that the area affected by three factors simultaneously represents 0.2% of the national territory, while paired effects cover 4% of it. The methodological advantage of this approach is its capacity to pinpoint hotspots with practical relevance. Nevertheless, its generalizing character impairs its use at the local scale..

  1. Mapping soil gas radon concentration: a comparative study of geostatistical methods.

    Science.gov (United States)

    Buttafuoco, Gabriele; Tallarico, Adalisa; Falcone, Giovanni

    2007-08-01

    Understanding soil gas radon spatial variations can allow the constructor of a new house to prevent radon gas flowing from the ground. Indoor radon concentration distribution depends on many parameters and it is difficult to use its spatial variation to assess radon potential. Many scientists use to measure outdoor soil gas radon concentrations to assess the radon potential. Geostatistical methods provide us a valuable tool to study spatial structure of radon concentration and mapping. To explore the structure of soil gas radon concentration within an area in south Italy and choice a kriging algorithm, we compared the prediction performances of four different kriging algorithms: ordinary kriging, lognormal kriging, ordinary multi-Gaussian kriging, and ordinary indicator cokriging. Their results were compared using an independent validation data set. The comparison of predictions was based on three measures of accuracy: (1) the mean absolute error, (2) the mean-squared error of prediction; (3) the mean relative error, and a measure of effectiveness: the goodness-of-prediction estimate. The results obtained in this case study showed that the multi-Gaussian kriging was the most accurate approach among those considered. Comparing radon anomalies with lithology and fault locations, no evidence of a strict correlation between type of outcropping terrain and radon anomalies was found, except in the western sector where there were granitic and gneissic terrain. Moreover, there was a clear correlation between radon anomalies and fault systems.

  2. The geostatistical approach for structural and stratigraphic framework analysis of offshore NW Bonaparte Basin, Australia

    Energy Technology Data Exchange (ETDEWEB)

    Wahid, Ali, E-mail: ali.wahid@live.com; Salim, Ahmed Mohamed Ahmed, E-mail: mohamed.salim@petronas.com.my; Yusoff, Wan Ismail Wan, E-mail: wanismail-wanyusoff@petronas.com.my [Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Tronoh, Perak (Malaysia); Gaafar, Gamal Ragab, E-mail: gaafargr@gmail.com [Petroleum Engineering Division, PETRONAS Carigali Sdn Bhd, Kuala Lumpur (Malaysia)

    2016-02-01

    Geostatistics or statistical approach is based on the studies of temporal and spatial trend, which depend upon spatial relationships to model known information of variable(s) at unsampled locations. The statistical technique known as kriging was used for petrophycial and facies analysis, which help to assume spatial relationship to model the geological continuity between the known data and the unknown to produce a single best guess of the unknown. Kriging is also known as optimal interpolation technique, which facilitate to generate best linear unbiased estimation of each horizon. The idea is to construct a numerical model of the lithofacies and rock properties that honor available data and further integrate with interpreting seismic sections, techtonostratigraphy chart with sea level curve (short term) and regional tectonics of the study area to find the structural and stratigraphic growth history of the NW Bonaparte Basin. By using kriging technique the models were built which help to estimate different parameters like horizons, facies, and porosities in the study area. The variograms were used to determine for identification of spatial relationship between data which help to find the depositional history of the North West (NW) Bonaparte Basin.

  3. Spatial Distribution of Some Soil Properties, Using Geostatistical Methods in Khezrabad Region (Yazd of Iran

    Directory of Open Access Journals (Sweden)

    AKBARZADEH A.

    2010-08-01

    Full Text Available Soil is an important compartment of the environment that is particularly easy to compromise, sensitive to shortand long-term pollution and directly affects sustainability of ecosystems and human health. A prerequisite of ecosystemmanagement decisions is monitoring of the spatial distribution of soil characteristics that geostatistics methods are oneof the most advanced techniques. In the present study, kriging, cokriging and IDW methods were used for prediction ofspatial distribution of salinity, water at saturation percentage, sodium adsorption ratio and percentage of sand, silt andclay in soils of Khezrabad region in Yazd province of Iran. After data normalization, the variograme was developed.For selecting the best model for competing on experimental variograme, the lower RSS value was used. The best modelfor interpretative was selected by means of cross validation and error evaluation methods, such as RMSE method. Theresults showed that kriging and cokriging methods were better than IDW method for prediction of soil properties.Moreover, soil texture and saturation percentage were better predicted by kriging method, where on, soil salinity andsodium adsorption ratio were better determined by cokriging method. The sum of Ca2++Mg2+ and Na+ concentrationwhich were highly correlated with soil salinity and sodium adsorption ratio, respectively, are used as auxiliaryparameters in this study. Finally, the soil characteristics maps were prepared, using the best interpolation method in GISenvironment.

  4. Comparison of different Geostatistical Approaches to map Sea Surface Temperature (SST) of Southern South China Sea

    Science.gov (United States)

    Ali, Azizi; Mohd Muslim, Aidy; Lokman Husain, Mohd; Fadzil Akhir, Mohd

    2013-04-01

    Sea surface temperature (SST) variation provides vital information for weather and ocean forecasting especially when studying climate change. Conventional methods of collecting ocean parameters such as SST, remains expensive and labor intensive due to the large area coverage and complex analytical procedure required. Therefore, some studies need to be conducted on the spatial and temporal distribution of ocean parameters. This study looks at Geo-statisctical methods in interpolating SST values and its impact on accuracy. Two spatial Geo-statistical techniques, mainly kriging and inverse distance functions (IDW) were applied to create variability distribution maps of SST for the Southern South China Sea (SCS). Data from 72 sampling station was collected in July 2012 covering an area of 270 km x 100 km and 263 km away from shore. This data provide the basis for the interpolation and accuracy analysis. After normalization, variograms were computed to fit the data sets producing models with the least RSS value. The accuracy were later evaluated based on on root mean squared error (RMSE) and root mean kriging variance (RMKV). Results show that Kriging with exponential model produced most accuracy estimates, reducing error in 17.3% compared with inverse distance functions.

  5. GEOSTATISTICAL MODEL EVALUATION OF LIMING ON OSIJEK-BARANYA COUNTY EXAMPLE

    Directory of Open Access Journals (Sweden)

    Vladimir Vukadinović

    2008-12-01

    Full Text Available Unfavorable pH of soil is the main reason for several different problems in debalance of mineral nutrition which can cause many problems in plant growth; such as leaves and fruit chlorosis and necrosis; etc. Therefore; liming as a measure for improving amount of acids soils must be conducted very carefully; with detail chemical soil analyses. This paper presents a segment of computer model for liming recommendation at the example of Osijek-Baranya County. Results of liming recommendation were obtained by geostatistical interpolation method – kriging. Totals of 9023 soil samples were analyzed in the period 2003–2007. The substitution acidity average was 5.49 (minimum 3.41 to maximum 8.20. Kriging shown that 241 379 ha (58.3% area of Osijek-Baranya County were acids soil. Therefore 90 593 ha have substitution acidity lower than 4.5 and 150 786 ha have pH KCl between 4.5 and 5.5. Except carbocalk; other "slowly-effect" materials can be recommended for liming; especially for vineyards and orchards.

  6. Determination of homogeneous zones for liming recommendations of black pepper using geostatistics

    Directory of Open Access Journals (Sweden)

    Ivoney Gontijo

    Full Text Available ABSTRACT Studies aimed at determining homogeneous zones and the spatial variability of soil characteristics may improve the efficiency of agricultural input applications. The purpose of this study was to determine homogeneous zones for liming applications and to characterize the spatial variability of characteristics related to soil acidity and productivity in an Oxisol cultivated with black pepper (Piper nigrum L.. This study was carried out in São Mateus, state of Espírito Santo, Brazil. The experimental site was 100 x 120 m. A grid with 126 sampling points was established. Three soil sub-samples were collected at each sampling point in the black pepper canopy areas, at a 0-0.20 m depth. Crop productivity was estimated by harvesting the three plants neighboring each sampling point. Descriptive statistics and geostatistical analyses were performed. Homogeneous management zones were defined based on map of liming needs. Mathematical models adjusted to semivariograms indicated that all of the studied variables exhibited spatial dependency. An analysis of the spatial variability together with the definition of homogeneous zones can be used to increase the efficiency of soil liming.

  7. A geostatistical approach to data harmonization - Application to radioactivity exposure data

    Science.gov (United States)

    Baume, O.; Skøien, J. O.; Heuvelink, G. B. M.; Pebesma, E. J.; Melles, S. J.

    2011-06-01

    Environmental issues such as air, groundwater pollution and climate change are frequently studied at spatial scales that cross boundaries between political and administrative regions. It is common for different administrations to employ different data collection methods. If these differences are not taken into account in spatial interpolation procedures then biases may appear and cause unrealistic results. The resulting maps may show misleading patterns and lead to wrong interpretations. Also, errors will propagate when these maps are used as input to environmental process models. In this paper we present and apply a geostatistical model that generalizes the universal kriging model such that it can handle heterogeneous data sources. The associated best linear unbiased estimation and prediction (BLUE and BLUP) equations are presented and it is shown that these lead to harmonized maps from which estimated biases are removed. The methodology is illustrated with an example of country bias removal in a radioactivity exposure assessment for four European countries. The application also addresses multicollinearity problems in data harmonization, which arise when both artificial bias factors and natural drifts are present and cannot easily be distinguished. Solutions for handling multicollinearity are suggested and directions for further investigations proposed.

  8. The detection of thermophilous forest hotspots in Poland using geostatistical interpolation of plant richness

    Directory of Open Access Journals (Sweden)

    Marcin Kiedrzyński

    2014-07-01

    Full Text Available Attempts to study biodiversity hotspots on a regional scale should combine compositional and functionalist criteria. The detection of hotspots in this study uses one ecologically similar group of high conservation value species as hotspot indicators, as well as focal habitat indicators, to detect the distribution of suitable environmental conditions. The method is assessed with reference to thermophilous forests in Poland – key habitats for many rare and relict species. Twenty-six high conservation priority species were used as hotspot indicators, and ten plant taxa characteristic of the Quercetalia pubescenti-petraeae phytosociological order were used as focal habitat indicators. Species distribution data was based on a 10 × 10 km grid. The number of species per grid square was interpolated by the ordinary kriging geostatistical method. Our analysis largely determined the distribution of areas with concentration of thermophilous forest flora, but also regional disjunctions and geographical barriers. Indicator species richness can be interpreted as a reflection of the actual state of habitat conditions. It can also be used to determine the location of potential species refugia and possible past and future migration routes.

  9. A Bayesian spatio-temporal geostatistical model with an auxiliary lattice for large datasets

    KAUST Repository

    Xu, Ganggang

    2015-01-01

    When spatio-temporal datasets are large, the computational burden can lead to failures in the implementation of traditional geostatistical tools. In this paper, we propose a computationally efficient Bayesian hierarchical spatio-temporal model in which the spatial dependence is approximated by a Gaussian Markov random field (GMRF) while the temporal correlation is described using a vector autoregressive model. By introducing an auxiliary lattice on the spatial region of interest, the proposed method is not only able to handle irregularly spaced observations in the spatial domain, but it is also able to bypass the missing data problem in a spatio-temporal process. Because the computational complexity of the proposed Markov chain Monte Carlo algorithm is of the order O(n) with n the total number of observations in space and time, our method can be used to handle very large spatio-temporal datasets with reasonable CPU times. The performance of the proposed model is illustrated using simulation studies and a dataset of precipitation data from the coterminous United States.

  10. A Resampling-Based Stochastic Approximation Method for Analysis of Large Geostatistical Data

    KAUST Repository

    Liang, Faming

    2013-03-01

    The Gaussian geostatistical model has been widely used in modeling of spatial data. However, it is challenging to computationally implement this method because it requires the inversion of a large covariance matrix, particularly when there is a large number of observations. This article proposes a resampling-based stochastic approximation method to address this challenge. At each iteration of the proposed method, a small subsample is drawn from the full dataset, and then the current estimate of the parameters is updated accordingly under the framework of stochastic approximation. Since the proposed method makes use of only a small proportion of the data at each iteration, it avoids inverting large covariance matrices and thus is scalable to large datasets. The proposed method also leads to a general parameter estimation approach, maximum mean log-likelihood estimation, which includes the popular maximum (log)-likelihood estimation (MLE) approach as a special case and is expected to play an important role in analyzing large datasets. Under mild conditions, it is shown that the estimator resulting from the proposed method converges in probability to a set of parameter values of equivalent Gaussian probability measures, and that the estimator is asymptotically normally distributed. To the best of the authors\\' knowledge, the present study is the first one on asymptotic normality under infill asymptotics for general covariance functions. The proposed method is illustrated with large datasets, both simulated and real. Supplementary materials for this article are available online. © 2013 American Statistical Association.

  11. Research on the reconstruction method of porous media using multiple-point geostatistics

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    The pore structural characteristics have been the key to the studies on the mechanisms of fluids flow in porous media. With the development of experimental technology, the modern high-resolution equipments are capable of capturing pore structure images with a resolution of microns. But so far only 3D volume data of millimeter-scale rock samples can be obtained losslessly. It is necessary to explore the way of virtually reconstructing larger volume digital samples of porous media with the representative structural characteristics of the pore space. This paper proposes a reconstruction method of porous media using the structural characteristics captured by the data templates of multiple-point geostatistics. In this method, the probability of each structural characteristic of a pore space is acquired first, and then these characteristics are reproduced according to the probabilities to present the real structural characteristics in the reconstructed images. Our experimental results have shown that: (i) the deviation of LBM computed permeability respectively on the virtually reconstructed sandstone and the original sample is less than 1.2%; (ii) the reconstructed sandstone and the original sample have similar structural characteristics demonstrated by the variogram curves.

  12. Geostatistical investigation into the temporal evolution of spatial structure in a shallow water table

    Directory of Open Access Journals (Sweden)

    S. W. Lyon

    2006-01-01

    Full Text Available Shallow water tables near-streams often lead to saturated, overland flow generating areas in catchments in humid climates. While these saturated areas are assumed to be principal biogeochemical hot-spots and important for issues such as non-point pollution sources, the spatial and temporal behavior of shallow water tables, and associated saturated areas, is not completely understood. This study demonstrates how geostatistical methods can be used to characterize the spatial and temporal variation of the shallow water table for the near-stream region. Event-based and seasonal changes in the spatial structure of the shallow water table, which influences the spatial pattern of surface saturation and related runoff generation, can be identified and used in conjunction to characterize the hydrology of an area. This is accomplished through semivariogram analysis and indicator kriging to produce maps combining soft data (i.e., proxy information to the variable of interest representing general shallow water table patterns with hard data (i.e., actual measurements that represent variation in the spatial structure of the shallow water table per rainfall event. The area used was a hillslope in the Catskill Mountains region of New York State. The shallow water table was monitored for a 120 m×180 m near-stream region at 44 sampling locations on 15-min intervals. Outflow of the area was measured at the same time interval. These data were analyzed at a short time interval (15 min and at a long time interval (months to characterize the changes in the hydrologic behavior of the hillslope. Indicator semivariograms based on binary-transformed ground water table data (i.e., 1 if exceeding the time-variable median depth to water table and 0 if not were created for both short and long time intervals. For the short time interval, the indicator semivariograms showed a high degree of spatial structure in the shallow water table for the spring, with increased range

  13. UNCERT: geostatistics, uncertainty analysis and visualization software applied to groundwater flow and contaminant transport modeling

    Science.gov (United States)

    Wingle, William L.; Poeter, Eileen P.; McKenna, Sean A.

    1999-05-01

    UNCERT is a 2D and 3D geostatistics, uncertainty analysis and visualization software package applied to ground water flow and contaminant transport modeling. It is a collection of modules that provides tools for linear regression, univariate statistics, semivariogram analysis, inverse-distance gridding, trend-surface analysis, simple and ordinary kriging and discrete conditional indicator simulation. Graphical user interfaces for MODFLOW and MT3D, ground water flow and contaminant transport models, are provided for streamlined data input and result analysis. Visualization tools are included for displaying data input and output. These include, but are not limited to, 2D and 3D scatter plots, histograms, box and whisker plots, 2D contour maps, surface renderings of 2D gridded data and 3D views of gridded data. By design, UNCERT's graphical user interface and visualization tools facilitate model design and analysis. There are few built in restrictions on data set sizes and each module (with two exceptions) can be run in either graphical or batch mode. UNCERT is in the public domain and is available from the World Wide Web with complete on-line and printable (PDF) documentation. UNCERT is written in ANSI-C with a small amount of FORTRAN77, for UNIX workstations running X-Windows and Motif (or Lesstif). This article discusses the features of each module and demonstrates how they can be used individually and in combination. The tools are applicable to a wide range of fields and are currently used by researchers in the ground water, mining, mathematics, chemistry and geophysics, to name a few disciplines.

  14. A space-time geostatistical framework for ensemble nowcasting using rainfall radar fields and gauge data

    Science.gov (United States)

    Caseri, Angelica; Ramos, Maria Helena; Javelle, Pierre; Leblois, Etienne

    2016-04-01

    Floods are responsible for a major part of the total damage caused by natural disasters. Nowcasting systems providing public alerts to flash floods are very important to prevent damages from extreme events and reduce their socio-economic impacts. The major challenge of these systems is to capture high-risk situations in advance, with good accuracy in the intensity, location and timing of future intense precipitation events. Flash flood forecasting has been studied by several authors in different affected areas. The majority of the studies combines rain gauge data with radar imagery advection to improve prediction for the next few hours. Outputs of Numerical Weather Prediction (NWP) models have also been increasingly used to predict ensembles of extreme precipitation events that might trigger flash floods. One of the challenges of the use of NWP for ensemble nowcasting is to successfully generate ensemble forecasts of precipitation in a short time calculation period to enable the production of flood forecasts with sufficient advance to issue flash flood alerts. In this study, we investigate an alternative space-time geostatistical framework to generate multiple scenarios of future rainfall for flash floods nowcasting. The approach is based on conditional simulation and an advection method applied within the Turning Bands Method (TBM). Ensemble forecasts of precipitation fields are generated based on space-time properties given by radar images and precipitation data collected from rain gauges during the development of the rainfall event. The results show that the approach developed can be an interesting alternative to capture precipitation uncertainties in location and intensity and generate ensemble forecasts of rainfall that can be useful to improve alerts for flash floods, especially in small areas.

  15. Quantifying the Relationship between Dynamical Cores and Physical Parameterizations by Geostatistical Methods

    Science.gov (United States)

    Yorgun, M. S.; Rood, R. B.

    2010-12-01

    The behavior of atmospheric models is sensitive to the algorithms that are used to represent the equations of motion. Typically, comprehensive models are conceived in terms of the resolved fluid dynamics (i.e. the dynamical core) and subgrid, unresolved physics represented by parameterizations. Deterministic weather predictions are often validated with feature-by-feature comparison. Probabilistic weather forecasts and climate projects are evaluated with statistical methods. We seek to develop model evaluation strategies that identify like “objects” - coherent systems with an associated set of measurable parameters. This makes it possible to evaluate processes in models without needing to reproduce the time and location of, for example, a particular observed cloud system. Process- and object-based evaluation preserves information in the observations by avoiding the need for extensive spatial and temporal averaging. As a concrete example, we focus on analyzing how the choice of dynamical core impacts the representation of precipitation in the Pacific Northwest of the United States, Western Canada, and Alaska; this brings attention to the interaction of the resolved and the parameterized components of the model. Two dynamical cores are considered within the Community Atmosphere Model. These are the Spectral (Eulerian), which relies on global basis functions and the Finite Volume (FV), which uses only local information. We introduce the concept of "meteorological realism" that is, do local representations of large-scale phenomena, for example, fronts and orographic precipitation, look like the observations? A follow on question is, does the representation of these phenomena improve with resolution? Our approach to quantify meteorological realism starts with methods of geospatial statistics. Specifically, we employ variography, which is a geostatistical method which is used to measure the spatial continuity of a regionalized variable, and principle component

  16. A geostatistical synthesis study of factors affecting gross primary productivity in various ecosystems of North America

    Directory of Open Access Journals (Sweden)

    V. Yadav

    2010-02-01

    Full Text Available A coupled Bayesian model selection and geostatistical regression modeling approach is adopted for empirical analysis of gross primary productivity (GPP at six AmeriFlux sites, including the Kennedy Space Center Scrub Oak, Vaira Ranch, Tonzi Ranch, Blodgett Forest, Morgan Monroe State Forest, and Harvard Forest sites. The analysis is performed at a continuum of temporal scales ranging from daily to monthly, for a period of seven years. A total of 10 covariates representing environmental stimuli and indices of plant physiology are considered in explaining variations in GPP. Similar to other statistical methods, the proposed approach estimates regression coefficients and uncertainties associated with the covariates in a selected regression model. However, unlike traditional regression methods, the presented approach also estimates the uncertainty associated with the selection of a single "best" model of GPP. In addition, the approach provides an enhanced understanding of how the importance of specific covariates changes with temporal resolutions. An examination of trends in the importance of specific covariates reveals scaling thresholds above or below which covariates become significant in explaining GPP. Results indicate that most sites (especially those with a stronger seasonal cycle exhibit at least one prominent scaling threshold between daily to 20-day temporal scale. This demonstrates that environmental variables that explain GPP at synoptic scales are different from those that capture its seasonality. At shorter time scales, radiation, temperature, and vapor pressure deficit exert most significant influence on GPP at most examined sites. However, at coarser time scales, the importance of these covariates in explaining GPP declines. Overall, unique best models are identified at most sites at the daily scale, whereas multiple competing models are identified at larger time scales. In addition, the selected models are able to explain a larger

  17. A geostatistical synthesis study of factors affecting gross primary productivity in various ecosystems of North America

    Directory of Open Access Journals (Sweden)

    V. Yadav

    2010-09-01

    Full Text Available A coupled Bayesian model selection and geostatistical regression modeling approach is adopted for empirical analysis of gross primary productivity (GPP at six AmeriFlux sites, including the Kennedy Space Center Scrub Oak, Vaira Ranch, Tonzi Ranch, Blodgett Forest, Morgan Monroe State Forest, and Harvard Forest sites. The analysis is performed at a continuum of temporal scales ranging from daily to monthly, for a period of seven years. A total of 10 covariates representing environmental stimuli and indices of plant physiology are considered in explaining variations in GPP. Similarly to other statistical methods, the presented approach estimates regression coefficients and uncertainties associated with the covariates in a selected regression model. Unlike traditional regression methods, however, the approach also estimates the uncertainty associated with the selection of a single "best" model of GPP. In addition, the approach provides an enhanced understanding of how the importance of specific covariates changes with the examined timescale (i.e. temporal resolution. An examination of changes in the importance of specific covariates across timescales reveals thresholds above or below which covariates become important in explaining GPP. Results indicate that most sites (especially those with a stronger seasonal cycle exhibit at least one prominent scaling threshold between the daily and 20-day temporal scales. This demonstrates that environmental variables that explain GPP at synoptic scales are different from those that capture its seasonality. At shorter time scales, radiation, temperature, and vapor pressure deficit exert the most significant influence on GPP at most examined sites. At coarser time scales, however, the importance of these covariates in explaining GPP declines. Overall, unique best models are identified at most sites at the daily scale, whereas multiple competing models are identified at longer time scales.

  18. Geostatistical analysis of variations in soil salinity in atypical irrigation area in Xinjiang, northwest China

    Institute of Scientific and Technical Information of China (English)

    2016-01-01

    Characterizing spatial and temporal variability of soil salinity is tremendously important for a variety of agronomic andenvironmental concerns in arid irrigation areas. This paper reviews the characteristics and spatial and temporal variationsof soil salinization in the Ili River Irrigation Area by applying a geostatistical approach. Results showed that: (1) the soilsalinity varied widely, with maximum value of 28.10 g/kg and minimum value of 0.10 g/kg, and was distributed mainly atthe surface soil layer. Anions were mainly SO42- and Cl-, while cations were mainly Na+ and Ca2+; (2) the abundance ofsalinity of the root zone soil layer for different land use types was in the following order: grassland 〉 cropland 〉 forestland.The abundance of salinity of root zone soil layers for different periods was in the following order: March 〉 June 〉 September;(3) the spherical model was the most suitable variogram model to describe the salinity of the 0-3 cm and 3-20 cmsoil layers in March and June, and the 3-20 cm soil layer in September, while the exponential model was the most suitablevariogram model to describe the salinity of the 0-3 cm soil layer in September. Relatively strong spatial and temporalstructure existed for soil salinity due to lower nugget effects; and (4) the maps of kriged soil salinity showed that higher soilsalinity was distributed in the central parts of the study area and lower soil salinity was distributed in the marginal parts.Soil salinity tended to increase from the marginal parts to the central parts across the study area. Applying the krigingmethod is very helpful in detecting the problematic areas and is a good tool for soil resources management. Managingefforts on the appropriate use of soil and water resources in such areas is very important for sustainable agriculture, andmore attention should be paid to these areas to prevent future problems.

  19. Geostatistical analysis of variations in soil salinity in a typical irrigation area in Xinjiang, northwest China

    Institute of Scientific and Technical Information of China (English)

    Mamattursun Eziz; Mihrigul Anwar; XinGuo Li

    2016-01-01

    Characterizing spatial and temporal variability of soil salinity is tremendously important for a variety of agronomic and environmental concerns in arid irrigation areas. This paper reviews the characteristics and spatial and temporal variations of soil salinization in the Ili River Irrigation Area by applying a geostatistical approach. Results showed that: (1) the soil salinity varied widely, with maximum value of 28.10 g/kg and minimum value of 0.10 g/kg, and was distributed mainly at the surface soil layer. Anions were mainly SO42− and Cl−, while cations were mainly Na+and Ca2+; (2) the abundance of salinity of the root zone soil layer for different land use types was in the following order: grassland > cropland > forestland. The abundance of salinity of root zone soil layers for different periods was in the following order: March > June > Sep-tember; (3) the spherical model was the most suitable variogram model to describe the salinity of the 0–3 cm and 3–20 cm soil layers in March and June, and the 3–20 cm soil layer in September, while the exponential model was the most suitable variogram model to describe the salinity of the 0–3 cm soil layer in September. Relatively strong spatial and temporal structure existed for soil salinity due to lower nugget effects; and (4) the maps of kriged soil salinity showed that higher soil salinity was distributed in the central parts of the study area and lower soil salinity was distributed in the marginal parts. Soil salinity tended to increase from the marginal parts to the central parts across the study area. Applying the kriging method is very helpful in detecting the problematic areas and is a good tool for soil resources management. Managing efforts on the appropriate use of soil and water resources in such areas is very important for sustainable agriculture, and more attention should be paid to these areas to prevent future problems.

  20. Mapping helminth co-infection and co-intensity: geostatistical prediction in ghana.

    Directory of Open Access Journals (Sweden)

    Ricardo J Soares Magalhães

    2011-06-01

    Full Text Available BACKGROUND: Morbidity due to Schistosoma haematobium and hookworm infections is marked in those with intense co-infections by these parasites. The development of a spatial predictive decision-support tool is crucial for targeting the delivery of integrated mass drug administration (MDA to those most in need. We investigated the co-distribution of S. haematobium and hookworm infection, plus the spatial overlap of infection intensity of both parasites, in Ghana. The aim was to produce maps to assist the planning and evaluation of national parasitic disease control programs. METHODOLOGY/PRINCIPAL FINDINGS: A national cross-sectional school-based parasitological survey was conducted in Ghana in 2008, using standardized sampling and parasitological methods. Bayesian geostatistical models were built, including a multinomial regression model for S. haematobium and hookworm mono- and co-infections and zero-inflated Poisson regression models for S. haematobium and hookworm infection intensity as measured by egg counts in urine and stool respectively. The resulting infection intensity maps were overlaid to determine the extent of geographical overlap of S. haematobium and hookworm infection intensity. In Ghana, prevalence of S. haematobium mono-infection was 14.4%, hookworm mono-infection was 3.2%, and S. haematobium and hookworm co-infection was 0.7%. Distance to water bodies was negatively associated with S. haematobium and hookworm co-infections, hookworm mono-infections and S. haematobium infection intensity. Land surface temperature was positively associated with hookworm mono-infections and S. haematobium infection intensity. While high-risk (prevalence >10-20% of co-infection was predicted in an area around Lake Volta, co-intensity was predicted to be highest in foci within that area. CONCLUSIONS/SIGNIFICANCE: Our approach, based on the combination of co-infection and co-intensity maps allows the identification of communities at increased risk of

  1. Mapping Helminth Co-Infection and Co-Intensity: Geostatistical Prediction in Ghana

    Science.gov (United States)

    Soares Magalhães, Ricardo J.; Biritwum, Nana-Kwadwo; Gyapong, John O.; Brooker, Simon; Zhang, Yaobi; Blair, Lynsey; Fenwick, Alan; Clements, Archie C. A.

    2011-01-01

    Background Morbidity due to Schistosoma haematobium and hookworm infections is marked in those with intense co-infections by these parasites. The development of a spatial predictive decision-support tool is crucial for targeting the delivery of integrated mass drug administration (MDA) to those most in need. We investigated the co-distribution of S. haematobium and hookworm infection, plus the spatial overlap of infection intensity of both parasites, in Ghana. The aim was to produce maps to assist the planning and evaluation of national parasitic disease control programs. Methodology/Principal Findings A national cross-sectional school-based parasitological survey was conducted in Ghana in 2008, using standardized sampling and parasitological methods. Bayesian geostatistical models were built, including a multinomial regression model for S. haematobium and hookworm mono- and co-infections and zero-inflated Poisson regression models for S. haematobium and hookworm infection intensity as measured by egg counts in urine and stool respectively. The resulting infection intensity maps were overlaid to determine the extent of geographical overlap of S. haematobium and hookworm infection intensity. In Ghana, prevalence of S. haematobium mono-infection was 14.4%, hookworm mono-infection was 3.2%, and S. haematobium and hookworm co-infection was 0.7%. Distance to water bodies was negatively associated with S. haematobium and hookworm co-infections, hookworm mono-infections and S. haematobium infection intensity. Land surface temperature was positively associated with hookworm mono-infections and S. haematobium infection intensity. While high-risk (prevalence >10–20%) of co-infection was predicted in an area around Lake Volta, co-intensity was predicted to be highest in foci within that area. Conclusions/Significance Our approach, based on the combination of co-infection and co-intensity maps allows the identification of communities at increased risk of severe morbidity and

  2. Geostatistical conditional simulation for the assessment of contaminated land by abandoned heavy metal mining.

    Science.gov (United States)

    Ersoy, Adem; Yunsel, Tayfun Yusuf; Atici, Umit

    2008-02-01

    Abandoned mine workings can undoubtedly cause varying degrees of contamination of soil with heavy metals such as lead and zinc has occurred on a global scale. Exposure to these elements may cause to harm human health and environment. In the study, a total of 269 soil samples were collected at 1, 5, and 10 m regular grid intervals of 100 x 100 m area of Carsington Pasture in the UK. Cell declustering technique was applied to the data set due to no statistical representativity. Directional experimental semivariograms of the elements for the transformed data showed that both geometric and zonal anisotropy exists in the data. The most evident spatial dependence structure of the continuity for the directional experimental semivariogram, characterized by spherical and exponential models of Pb and Zn were obtained. This study reports the spatial distribution and uncertainty of Pb and Zn concentrations in soil at the study site using a probabilistic approach. The approach was based on geostatistical sequential Gaussian simulation (SGS), which is used to yield a series of conditional images characterized by equally probable spatial distributions of the heavy elements concentrations across the area. Postprocessing of many simulations allowed the mapping of contaminated and uncontaminated areas, and provided a model for the uncertainty in the spatial distribution of element concentrations. Maps of the simulated Pb and Zn concentrations revealed the extent and severity of contamination. SGS was validated by statistics, histogram, variogram reproduction, and simulation errors. The maps of the elements might be used in the remediation studies, help decision-makers and others involved in the abandoned heavy metal mining site in the world.

  3. Simulation of Solute Flow and Transport in a Geostatistically Generated Fractured Porous System

    Science.gov (United States)

    Assteerawatt, A.; Helmig, R.; Haegland, H.; Bárdossy, A.

    2007-12-01

    Fractured aquifer systems have provided important natural resources such as petroleum, gas, water and geothermal energy and have also been recently under investigation for their suitability as storage sites for high-level nuclear waste. The resource exploitation and potential utilization have led to extensive studies aiming of understanding, characterizing and finally predicting the behavior of fractured aquifer systems. By applying a discrete model approach to study flow and transport processes, fractures are determined discretely and the effect of individual fractures can be explicitly investigated. The critical step for the discrete model is the generation of a representative fracture network since the development of flow paths within a fractured system strongly depends on its structure. The geostatistical fracture generation (GFG) developed in this study aims to create a representative fracture network, which combines the spatial structures and connectivity of a fracture network, and the statistical distribution of fracture geometries. The spatial characteristics are characterized from indicator fields, which are evaluated from fracture trace maps. A global optimization, Simulated annealing, is utilized as a generation technique and the spatial characteristics are formulated to its objective function. We apply the GFG to a case study at a Pliezhausen field block, which is a sandstone of a high fracture density. The generated fracture network from the GFG are compared with the statistically generated fracture network in term of structure and hydraulic behavior. As the GFG is based on a stochastic concept, several realizations of the same descriptions can be generated, hence, an overall behavior of the fracture-matrix system have to be investigated from various realizations which leads to a problem of computational demand. In order to overcome this problem, a streamline method for a solute transport in a fracture porous system is presented. The results obtained

  4. Sampling design optimisation for rainfall prediction using a non-stationary geostatistical model

    Science.gov (United States)

    Wadoux, Alexandre M. J.-C.; Brus, Dick J.; Rico-Ramirez, Miguel A.; Heuvelink, Gerard B. M.

    2017-09-01

    The accuracy of spatial predictions of rainfall by merging rain-gauge and radar data is partly determined by the sampling design of the rain-gauge network. Optimising the locations of the rain-gauges may increase the accuracy of the predictions. Existing spatial sampling design optimisation methods are based on minimisation of the spatially averaged prediction error variance under the assumption of intrinsic stationarity. Over the past years, substantial progress has been made to deal with non-stationary spatial processes in kriging. Various well-documented geostatistical models relax the assumption of stationarity in the mean, while recent studies show the importance of considering non-stationarity in the variance for environmental processes occurring in complex landscapes. We optimised the sampling locations of rain-gauges using an extension of the Kriging with External Drift (KED) model for prediction of rainfall fields. The model incorporates both non-stationarity in the mean and in the variance, which are modelled as functions of external covariates such as radar imagery, distance to radar station and radar beam blockage. Spatial predictions are made repeatedly over time, each time recalibrating the model. The space-time averaged KED variance was minimised by Spatial Simulated Annealing (SSA). The methodology was tested using a case study predicting daily rainfall in the north of England for a one-year period. Results show that (i) the proposed non-stationary variance model outperforms the stationary variance model, and (ii) a small but significant decrease of the rainfall prediction error variance is obtained with the optimised rain-gauge network. In particular, it pays off to place rain-gauges at locations where the radar imagery is inaccurate, while keeping the distribution over the study area sufficiently uniform.

  5. A connectionist-geostatistical approach for classification of deformation types in ice surfaces

    Science.gov (United States)

    Goetz-Weiss, L. R.; Herzfeld, U. C.; Hale, R. G.; Hunke, E. C.; Bobeck, J.

    2014-12-01

    Deformation is a class of highly non-linear geophysical processes from which one can infer other geophysical variables in a dynamical system. For example, in an ice-dynamic model, deformation is related to velocity, basal sliding, surface elevation changes, and the stress field at the surface as well as internal to a glacier. While many of these variables cannot be observed, deformation state can be an observable variable, because deformation in glaciers (once a viscosity threshold is exceeded) manifests itself in crevasses.Given the amount of information that can be inferred from observing surface deformation, an automated method for classifying surface imagery becomes increasingly desirable. In this paper a Neural Network is used to recognize classes of crevasse types over the Bering Bagley Glacier System (BBGS) during a surge (2011-2013-?). A surge is a spatially and temporally highly variable and rapid acceleration of the glacier. Therefore, many different crevasse types occur in a short time frame and in close proximity, and these crevasse fields hold information on the geophysical processes of the surge.The connectionist-geostatistical approach uses directional experimental (discrete) variograms to parameterize images into a form that the Neural Network can recognize. Recognizing that each surge wave results in different crevasse types and that environmental conditions affect the appearance in imagery, we have developed a semi-automated pre-training software to adapt the Neural Net to chaining conditions.The method is applied to airborne and satellite imagery to classify surge crevasses from the BBGS surge. This method works well for classifying spatially repetitive images such as the crevasses over Bering Glacier. We expand the network for less repetitive images in order to analyze imagery collected over the Arctic sea ice, to assess the percentage of deformed ice for model calibration.

  6. Examining the spatial distribution of flower thrips in southern highbush blueberries by utilizing geostatistical methods.

    Science.gov (United States)

    Rhodes, Elena M; Liburd, Oscar E; Grunwald, Sabine

    2011-08-01

    Flower thrips (Frankliniella spp.) are one of the key pests of southern highbush blueberries (Vaccinium corymbosum L. x V. darrowii Camp), a high-value crop in Florida. Thrips' feeding and oviposition injury to flowers can result in fruit scarring that renders the fruit unmarketable. Flower thrips often form areas of high population, termed "hot spots", in blueberry plantings. The objective of this study was to model thrips spatial distribution patterns with geostatistical techniques. Semivariogram models were used to determine optimum trap spacing and two commonly used interpolation methods, inverse distance weighting (IDW) and ordinary kriging (OK), were compared for their ability to model thrips spatial patterns. The experimental design consisted of a grid of 100 white sticky traps spaced at 15.24-m and 7.61-m intervals in 2008 and 2009, respectively. Thirty additional traps were placed randomly throughout the sampling area to collect information on distances shorter than the grid spacing. The semivariogram analysis indicated that, in most cases, spacing traps at least 28.8 m apart would result in spatially independent samples. Also, the 7.61-m grid spacing captured more of the thrips spatial variability than the 15.24-m grid spacing. IDW and OK produced maps with similar accuracy in both years, which indicates that thrips spatial distribution patterns, including "hot spots," can be modeled using either interpolation method. Future studies can use this information to determine if the formation of "hot spots" can be predicted using flower density, temperature, and other environmental factors. If so, this development would allow growers to spot treat the "hot spots" rather than their entire field.

  7. Using Geostatistical Data Fusion Techniques and MODIS Data to Upscale Simulated Wheat Yield

    Science.gov (United States)

    Castrignano, A.; Buttafuoco, G.; Matese, A.; Toscano, P.

    2014-12-01

    Population growth increases food request. Assessing food demand and predicting the actual supply for a given location are critical components of strategic food security planning at regional scale. Crop yield can be simulated using crop models because is site-specific and determined by weather, management, length of growing season and soil properties. Crop models require reliable location-specific data that are not generally available. Obtaining these data at a large number of locations is time-consuming, costly and sometimes simply not feasible. An upscaling method to extend coverage of sparse estimates of crop yield to an appropriate extrapolation domain is required. This work is aimed to investigate the applicability of a geostatistical data fusion approach for merging remote sensing data with the predictions of a simulation model of wheat growth and production using ground-based data. The study area is Capitanata plain (4000 km2) located in Apulia Region, mostly cropped with durum wheat. The MODIS EVI/NDVI data products for Capitanata plain were downloaded from the Land Processes Distributed Active Archive Center (LPDAAC) remote for the whole crop cycle of durum wheat. Phenological development, biomass growth and grain quantity of durum wheat were simulated by the Delphi system, based on a crop simulation model linked to a database including soil properties, agronomical and meteorological data. Multicollocated cokriging was used to integrate secondary exhaustive information (multi-spectral MODIS data) with primary variable (sparsely distributed biomass/yield model predictions of durum wheat). The model estimates looked strongly spatially correlated with the radiance data (red and NIR bands) and the fusion data approach proved to be quite suitable and flexible to integrate data of different type and support.

  8. Delineation of Management Zones in Precision Agriculture by Integration of Proximal Sensing with Multivariate Geostatistics. Examples of Sensor Data Fusion

    Directory of Open Access Journals (Sweden)

    Annamaria Castrignanò

    2015-07-01

    Full Text Available Fundamental to the philosophy of Precision Agriculture (PA is the concept of matching inputs to needs. Recent research in PA has focused on use of Management Zones (MZ that are field areas characterised by homogeneous attributes in landscape and soil conditions. Proximal sensing (such as Electromagnetic Induction (EMI, Ground Penetrating Radar (GPR and X-ray fluorescence can complement direct sampling and a multisensory platform can enable us to map soil features unambiguously. Several methods of multi-sensor data analysis have been developed to determine the location of subfield areas. Modern geostatistical techniques, treating variables as continua in a joint attribute and geographic space, offer the potential to analyse such data effectively. The objective of the paper is to show the potential of multivariate geostatistics to create MZ in the perspective of PA by integrating field data from different types of sensors, describing two study cases. In particular, in the first case study, cokriging and factorial cokriging were employed to produce thematic maps of soil trace elements and to delineate homogenous zones, respectively. In the second case, a multivariate geostatistical data-fusion technique (multi collocated cokriging was applied to different geophysical sensor data (GPR and EMI, for stationary estimation of soil water content and for delineating within-field zone with different wetting degree. The results have shown that linking sensors of different type improves the overall assessment of soil and sensor data fusion could be effectively applied to delineate MZs in Precision Agriculture. However, techniques of data integration are urgently required as a result of the proliferation of data from different sources.

  9. Mapping of soil salinity: a comparative study between deterministic and geostatistical methods, case of the Tadla plain (Morocco)

    Science.gov (United States)

    Barbouchi, Meriem; Chokmani, Karem; Ben Aissa, Nadhira; Lhissou, Rachid; El Harti, Abderrazak; Abdelfattah, Riadh

    2013-04-01

    Soil salinization hazard in semi-arid regions such as Central Morocco is increasingly affecting arable lands and this is due to combined effects of anthropogenic activities (development of irrigation) and climate change (Multiplying drought episodes). In a rational strategy of fight against this hazard, salinity mapping is a key step to ensure effective spatiotemporal monitoring. The objective of this study is to test the effectiveness of geostatistical approach in mapping soil salinity compared to more forward deterministic interpolation methods. Three soil salinity sampling campaigns (27 September, 24 October and 19 November 2011) were conducted over the irrigated area of the Tadla plain, situated between the High and Middle Atlasin Central Morocco. Each campaign was made of 38 surface soil samples (upper 5 cm). From each sample the electrical conductivity (EC) was determined in saturated paste extract and used subsequently as proxy of soil salinity. The potential of deterministic interpolation methods (IDW) and geostatistical techniques (Ordinary Kriging) in mapping surface soil salinity was evaluated in a GIS environment through cross-validation technique. Field measurements showed that the soil salinity was generally low except during the second campaign where a significant increase in EC values was recorded. Interpolation results showed a better performance with geostatistical approach compared to deterministic one. Indeed, for all the campaigns, cross-validation yielded lower RMSE and bias for Kriging than IDW. However, the performance of the two methods was dependent on the range and the structure of the spatial variability of salinity. Indeed, Kriging showed better accuracy for the second campaign in comparison with the two others. This could be explained by the wider range of values of soil salinity during this campaign, which has resulted in a greater range of spatial dependence and has a better modeling of the spatial variability of salinity, which 'was

  10. Analysis of vadose zone tritium transport from an underground storage tank release using numerical modeling and geostatistics

    Energy Technology Data Exchange (ETDEWEB)

    Lee, K.H.

    1997-09-01

    Numerical and geostatistical analyses show that the artificial smoothing effect of kriging removes high permeability flow paths from hydrogeologic data sets, reducing simulated contaminant transport rates in heterogeneous vadose zone systems. therefore, kriging alone is not recommended for estimating the spatial distribution of soil hydraulic properties for contaminant transport analysis at vadose zone sites. Vadose zone transport if modeled more effectively by combining kriging with stochastic simulation to better represent the high degree of spatial variability usually found in the hydraulic properties of field soils. However, kriging is a viable technique for estimating the initial mass distribution of contaminants in the subsurface.

  11. Assimilation of Satellite Soil Moisture observation with the Particle Filter-Markov Chain Monte Carlo and Geostatistical Modeling

    Science.gov (United States)

    Moradkhani, Hamid; Yan, Hongxiang

    2016-04-01

    Soil moisture simulation and prediction are increasingly used to characterize agricultural droughts but the process suffers from data scarcity and quality. The satellite soil moisture observations could be used to improve model predictions with data assimilation. Remote sensing products, however, are typically discontinuous in spatial-temporal coverages; while simulated soil moisture products are potentially biased due to the errors in forcing data, parameters, and deficiencies of model physics. This study attempts to provide a detailed analysis of the joint and separate assimilation of streamflow and Advanced Scatterometer (ASCAT) surface soil moisture into a fully distributed hydrologic model, with the use of recently developed particle filter-Markov chain Monte Carlo (PF-MCMC) method. A geostatistical model is introduced to overcome the satellite soil moisture discontinuity issue where satellite data does not cover the whole study region or is significantly biased, and the dominant land cover is dense vegetation. The results indicate that joint assimilation of soil moisture and streamflow has minimal effect in improving the streamflow prediction, however, the surface soil moisture field is significantly improved. The combination of DA and geostatistical approach can further improve the surface soil moisture prediction.

  12. Genetic Geostatistical Framework for Spatial Analysis of Fine-Scale Genetic Heterogeneity in Modern Populations: Results from the KORA Study

    Directory of Open Access Journals (Sweden)

    A. N. Diaz-Lacava

    2015-01-01

    Full Text Available Aiming to investigate fine-scale patterns of genetic heterogeneity in modern humans from a geographic perspective, a genetic geostatistical approach framed within a geographic information system is presented. A sample collected for prospective studies in a small area of southern Germany was analyzed. None indication of genetic heterogeneity was detected in previous analysis. Socio-demographic and genotypic data of German citizens were analyzed (212 SNPs; n=728. Genetic heterogeneity was evaluated with observed heterozygosity (HO. Best-fitting spatial autoregressive models were identified, using socio-demographic variables as covariates. Spatial analysis included surface interpolation and geostatistics of observed and predicted patterns. Prediction accuracy was quantified. Spatial autocorrelation was detected for both socio-demographic and genetic variables. Augsburg City and eastern suburban areas showed higher HO values. The selected model gave best predictions in suburban areas. Fine-scale patterns of genetic heterogeneity were observed. In accordance to literature, more urbanized areas showed higher levels of admixture. This approach showed efficacy for detecting and analyzing subtle patterns of genetic heterogeneity within small areas. It is scalable in number of loci, even up to whole-genome analysis. It may be suggested that this approach may be applicable to investigate the underlying genetic history that is, at least partially, embedded in geographic data.

  13. Using rank-order geostatistics for spatial interpolation of highly skewed data in a heavy-metal contaminated site.

    Science.gov (United States)

    Juang, K W; Lee, D Y; Ellsworth, T R

    2001-01-01

    The spatial distribution of a pollutant in contaminated soils is usually highly skewed. As a result, the sample variogram often differs considerably from its regional counterpart and the geostatistical interpolation is hindered. In this study, rank-order geostatistics with standardized rank transformation was used for the spatial interpolation of pollutants with a highly skewed distribution in contaminated soils when commonly used nonlinear methods, such as logarithmic and normal-scored transformations, are not suitable. A real data set of soil Cd concentrations with great variation and high skewness in a contaminated site of Taiwan was used for illustration. The spatial dependence of ranks transformed from Cd concentrations was identified and kriging estimation was readily performed in the standardized-rank space. The estimated standardized rank was back-transformed into the concentration space using the middle point model within a standardized-rank interval of the empirical distribution function (EDF). The spatial distribution of Cd concentrations was then obtained. The probability of Cd concentration being higher than a given cutoff value also can be estimated by using the estimated distribution of standardized ranks. The contour maps of Cd concentrations and the probabilities of Cd concentrations being higher than the cutoff value can be simultaneously used for delineation of hazardous areas of contaminated soils.

  14. Using direct current resistivity sounding and geostatistics to aid in hydrogeological studies in the Choshuichi alluvial fan, Taiwan.

    Science.gov (United States)

    Yang, Chieh-Hou; Lee, Wei-Feng

    2002-01-01

    Ground water reservoirs in the Choshuichi alluvial fan, central western Taiwan, were investigated using direct-current (DC) resistivity soundings at 190 locations, combined with hydrogeological measurements from 37 wells. In addition, attempts were made to calculate aquifer transmissivity from both surface DC resistivity measurements and geostatistically derived predictions of aquifer properties. DC resistivity sounding data are highly correlated to the hydraulic parameters in the Choshuichi alluvial fan. By estimating the spatial distribution of hydraulic conductivity from the kriged well data and the cokriged thickness of the correlative aquifer from both resistivity sounding data and well information, the transmissivity of the aquifer at each location can be obtained from the product of kriged hydraulic conductivity and computed thickness of the geoelectric layer. Thus, the spatial variation of the transmissivities in the study area is obtained. Our work is more comparable to Ahmed et al. (1988) than to the work of Niwas and Singhal (1981). The first "constraint" from Niwas and Singhal's work is a result of their use of linear regression. The geostatistical approach taken here (and by Ahmed et al. [1988]) is a natural improvement on the linear regression approach.

  15. Integration of Tracer Test Data to Refine Geostatistical Hydraulic Conductivity Fields Using Sequential Self-Calibration Method

    Institute of Scientific and Technical Information of China (English)

    Bill X Hu; Jiang Xiaowei; Wan Li

    2007-01-01

    On the basis of local measurements of hydraulic conductivity, geostatistical methods have been found to be useful in heterogeneity characterization of a hydraulic conductivity field on a regional scale. However, the methods are not suited to directly integrate dynamic production data, such as,hydraulic head and solute concentration, into the study of conductivity distribution. These data, which record the flow and transport processes in the medium, are closely related to the spatial distribution of hydraulic conductivity. In this study, a three-dimensional gradient-based inverse method-the sequential self-calibration (SSC) method-is developed to calibrate a hydraulic conductivity field,initially generated by a geostatistical simulation method, conditioned on tracer test results. The SSC method can honor both local hydraulic conductivity measurements and tracer test data. The mismatch between the simulated hydraulic conductivity field and the reference true one, measured by its mean square error (MSE), is reduced through the SSC conditional study. In comparison with the unconditional results, the SSC conditional study creates the mean breakthrough curve much closer to the reference true curve, and significantly reduces the prediction uncertainty of the solute transport in the observed locations. Further, the reduction of uncertainty is spatially dependent, which indicates that good locations, geological structure, and boundary conditions will affect the efficiency of the SSC study results.

  16. Assessment of nitrate pollution in the Grand Morin aquifers (France): Combined use of geostatistics and physically based modeling

    Energy Technology Data Exchange (ETDEWEB)

    Flipo, Nicolas [Centre de Geosciences, UMR Sisyphe, ENSMP, 35 rue Saint-Honore, F-77305 Fontainebleau (France)]. E-mail: nicolas.flipo@ensmp.fr; Jeannee, Nicolas [Geovariances, 49 bis, avenue Franklin Roosevelt, F-77212 Avon (France); Poulin, Michel [Centre de Geosciences, UMR Sisyphe, ENSMP, 35 rue Saint-Honore, F-77305 Fontainebleau (France); Even, Stephanie [Centre de Geosciences, UMR Sisyphe, ENSMP, 35 rue Saint-Honore, F-77305 Fontainebleau (France); Ledoux, Emmanuel [Centre de Geosciences, UMR Sisyphe, ENSMP, 35 rue Saint-Honore, F-77305 Fontainebleau (France)

    2007-03-15

    The objective of this work is to combine several approaches to better understand nitrate fate in the Grand Morin aquifers (2700 km{sup 2}), part of the Seine basin. CAWAQS results from the coupling of the hydrogeological model NEWSAM with the hydrodynamic and biogeochemical model of river PROSE. CAWAQS is coupled with the agronomic model STICS in order to simulate nitrate migration in basins. First, kriging provides a satisfactory representation of aquifer nitrate contamination from local observations, to set initial conditions for the physically based model. Then associated confidence intervals, derived from data using geostatistics, are used to validate CAWAQS results. Results and evaluation obtained from the combination of these approaches are given (period 1977-1988). Then CAWAQS is used to simulate nitrate fate for a 20-year period (1977-1996). The mean nitrate concentrations increase in aquifers is 0.09 mgN L{sup -1} yr{sup -1}, resulting from an average infiltration flux of 3500 kgN.km{sup -2} yr{sup -1}. - Combined use of geostatistics and physically based modeling allows assessment of nitrate concentrations in aquifer systems.

  17. Spatial evaluation of the risk of groundwater quality degradation. A comparison between disjunctive kriging and geostatistical simulation.

    Science.gov (United States)

    Barca, E; Passarella, G

    2008-02-01

    In some previous papers a probabilistic methodology was introduced to estimate a spatial index of risk of groundwater quality degradation, defined as the conditional probability of exceeding assigned thresholds of concentration of a generic chemical sampled in the studied water system. A crucial stage of this methodology was the use of geostatistical techniques to provide an estimation of the above-mentioned probability in a number of selected points by crossing spatial and temporal information. In this work, spatial risk values were obtained using alternatively stochastic conditional simulation and disjunctive kriging. A comparison between the resulting two sets of spatial risks, based on global and local statistical tests, showed that they do not come from the same statistical population and, consequently, they cannot be viewed as equivalent in a statistical sense. At a first glance, geostatistical conditional simulation may appear to represent the spatial variability of the phenomenon more effectively, as the latter tends to be smoothed by DK. However, a close examination of real case study results suggests that disjunctive kriging is more effective than simulation in estimating the spatial risk of groundwater quality degradation. In the study case, the potentially 'harmful event' considered, threatening a natural 'vulnerable groundwater system,' is fertilizer and manure application.

  18. Effective property determination for input to a geostatistical model of regional groundwater flow: Wellenberg T{yields}K

    Energy Technology Data Exchange (ETDEWEB)

    Lanyon, G.W. [GeoScience Ltd., Falmouth (United Kingdom); Marschall, P.; Vomvoris, S. [NAGRA, Wettingen (Switzerland); Jaquet, O. [Colenco Power Engineering AG, Baden (Switzerland); Mazurek, M. [Bern Univ. (Switzerland). Mineralogisch-petrographisches Inst.

    1998-09-01

    This paper describes the methodology used to estimate effective hydraulic properties for input into a regional geostatistical model of groundwater flow at the Wellenberg site in Switzerland. The methodology uses a geologically-based discrete fracture network model to calculate effective hydraulic properties for 100m blocks along each borehole. A description of the most transmissive features (Water Conducting Features or WCFs) in each borehole is used to determine local transmissivity distributions which are combined with descriptions of WCF extent, orientation and channelling to create fracture network models. WCF geometry is dependent on the class of WCF. WCF classes are defined for each type of geological structure associated with identified borehole inflows. Local to each borehole, models are conditioned on the observed transmissivity and occurrence of WCFs. Multiple realisations are calculated for each 100m block over approximately 400m of borehole. The results from the numerical upscaling are compared with conservative estimates of hydraulic conductivity. Results from unconditioned models are also compared to identify the consequences of conditioning and interval of boreholes that appear to be atypical. An inverse method is also described by which realisations of the geostatistical model can be used to condition discrete fracture network models away from the boreholes. The method can be used as a verification of the modelling approach by prediction of data at borehole locations. Applications of the models to estimation of post-closure repository performance, including cavern inflow and seal zone modelling, are illustrated 14 refs, 9 figs

  19. The effect of training image and secondary data integration with multiple-point geostatistics in groundwater modelling

    DEFF Research Database (Denmark)

    He, Xin; Sonnenborg, Torben; Jørgensen, F.

    2014-01-01

    Multiple-point geostatistical simulation (MPS) has recently become popular in stochastic hydrogeology, primarily because of its capability to derive multivariate distributions from a training image (TI). However, its application in three-dimensional (3-D) simulations has been constrained by the d......Multiple-point geostatistical simulation (MPS) has recently become popular in stochastic hydrogeology, primarily because of its capability to derive multivariate distributions from a training image (TI). However, its application in three-dimensional (3-D) simulations has been constrained...... by the difficulty of constructing a 3-D TI. The object-based unconditional simulation program TiGenerator may be a useful tool in this regard; yet the applicability of such parametric training images has not been documented in detail. Another issue in MPS is the integration of multiple geophysical data. The proper...... is a convenient and efficient way of integrating secondary data such as 3-D airborne electromagnetic data (SkyTEM), but over-conditioning has to be avoided....

  20. Geostatistical modelling of soil-transmitted helminth infection in Cambodia: do socioeconomic factors improve predictions?

    Science.gov (United States)

    Karagiannis-Voules, Dimitrios-Alexios; Odermatt, Peter; Biedermann, Patricia; Khieu, Virak; Schär, Fabian; Muth, Sinuon; Utzinger, Jürg; Vounatsou, Penelope

    2015-01-01

    Soil-transmitted helminth infections are intimately connected with poverty. Yet, there is a paucity of using socioeconomic proxies in spatially explicit risk profiling. We compiled household-level socioeconomic data pertaining to sanitation, drinking-water, education and nutrition from readily available Demographic and Health Surveys, Multiple Indicator Cluster Surveys and World Health Surveys for Cambodia and aggregated the data at village level. We conducted a systematic review to identify parasitological surveys and made every effort possible to extract, georeference and upload the data in the open source Global Neglected Tropical Diseases database. Bayesian geostatistical models were employed to spatially align the village-aggregated socioeconomic predictors with the soil-transmitted helminth infection data. The risk of soil-transmitted helminth infection was predicted at a grid of 1×1km covering Cambodia. Additionally, two separate individual-level spatial analyses were carried out, for Takeo and Preah Vihear provinces, to assess and quantify the association between soil-transmitted helminth infection and socioeconomic indicators at an individual level. Overall, we obtained socioeconomic proxies from 1624 locations across the country. Surveys focussing on soil-transmitted helminth infections were extracted from 16 sources reporting data from 238 unique locations. We found that the risk of soil-transmitted helminth infection from 2000 onwards was considerably lower than in surveys conducted earlier. Population-adjusted prevalences for school-aged children from 2000 onwards were 28.7% for hookworm, 1.5% for Ascaris lumbricoides and 0.9% for Trichuris trichiura. Surprisingly, at the country-wide analyses, we did not find any significant association between soil-transmitted helminth infection and village-aggregated socioeconomic proxies. Based also on the individual-level analyses we conclude that socioeconomic proxies might not be good predictors at an

  1. Clastic reservoir porosity mapping using seismic data and geostatistics: Callovian unit, West Lulu field

    Energy Technology Data Exchange (ETDEWEB)

    Vejbaek, O.V.

    1998-12-31

    The aim of this report was to demonstrate possible uses of seismic impedances as soft data for reservoir characterization. To illustrate the impact of the results and attempt to calculate oil in place was also carried out. It must, however, be emphasized that these results only apply to the Callovian portion of the Middle Jurassic West Lulu reservoir, and thus do not provide estimates of the entire Middle Jurassic West Lulu accumulation. It is important to realise that stochastic simulations does not provide exact predictions in areas outside the control of hard data. It is, however, offering possibilities to exploit every known or surmised property about the desired (target) data population. These properties include f.ex., mean, spread, spatial continuity (measured by variograms), horixontal and vertical trends, correlation to supporting soft data (e.g. seismic impedances) etc. Neither are predictions exact even through the term `narrowed solution space` is applied. This term merely implies that the error in prediction at any point may be less than the full range of the parameter. The quality of the predictions mainly depend on meticulous handling of data, avoiding errors like bad stratigraphic alignment of the data, obtaining good variograms, avoiding errors in the construction of the target populations and including every pertinent attribute about the data. The result is thus also depending on a full geological understanding of the problem (and moral of the modeller). The most important quality is the ability to provide a great number of equi-probable realisation that equally well satisfies any known or surmised property about the target data population. The goal of this study was to investigate the use of inversion derived seismic impedances for geostatistical reservoir characterisation in a complex clastic reservoir exemplified with the West Lulu reservoir of the Harald Field. The well database is rather modest, so substantial support has been gained from the

  2. Regional-scale geostatistical inverse modeling of North American CO2 fluxes: a synthetic data study

    Directory of Open Access Journals (Sweden)

    A. M. Michalak

    2010-07-01

    Full Text Available A series of synthetic data experiments is performed to investigate the ability of a regional atmospheric inversion to estimate grid-scale CO2 fluxes during the growing season over North America. The inversions are performed within a geostatistical framework without the use of any prior flux estimates or auxiliary variables, in order to focus on the atmospheric constraint provided by the nine towers collecting continuous, calibrated CO2 measurements in 2004. Using synthetic measurements and their associated concentration footprints, flux and model-data mismatch covariance parameters are first optimized, and then fluxes and their uncertainties are estimated at three different temporal resolutions. These temporal resolutions, which include a four-day average, a four-day-average diurnal cycle with 3-hourly increments, and 3-hourly fluxes, are chosen to help assess the impact of temporal aggregation errors on the estimated fluxes and covariance parameters. Estimating fluxes at a temporal resolution that can adjust the diurnal variability is found to be critical both for recovering covariance parameters directly from the atmospheric data, and for inferring accurate ecoregion-scale fluxes. Accounting for both spatial and temporal a priori covariance in the flux distribution is also found to be necessary for recovering accurate a posteriori uncertainty bounds on the estimated fluxes. Overall, the results suggest that even a fairly sparse network of 9 towers collecting continuous CO2 measurements across the continent, used with no auxiliary information or prior estimates of the flux distribution in time or space, can be used to infer relatively accurate monthly ecoregion scale CO2 surface fluxes over North America within estimated uncertainty bounds. Simulated random transport error is shown to decrease the quality of flux estimates in under-constrained areas at the ecoregion scale, although the uncertainty bounds remain realistic. While these synthetic

  3. Spatial Variability and Geostatistical Prediction of Some Soil Hydraulic Coefficients of a Calcareous Soil

    Directory of Open Access Journals (Sweden)

    Ali Akbar Moosavi

    2017-02-01

    Full Text Available Introduction: Saturated hydraulic conductivity and the other hydraulic properties of soils are essential vital soil attributes that play role in the modeling of hydrological phenomena, designing irrigation-drainage systems, transportation of salts and chemical and biological pollutants within the soil. Measurement of these hydraulic properties needs some special instruments, expert technician, and are time consuming and expensive and due to their high temporal and spatial variability, a large number of measurements are needed. Nowadays, prediction of these attributes using the readily available soil data using pedotransfer functions or using the limited measurement with applying the geostatistical approaches has been receiving high attention. The study aimed to determine the spatial variability and prediction of saturated (Ks and near saturated (Kfs hydraulic conductivity, the power of Gardner equation (α, sorptivity (S, hydraulic diffusivity (D and matric flux potential (Фm of a calcareous soil. Material and Methods: The study was carried out on the soil series of Daneshkadeh located in the Bajgah Agricultural Experimental Station of Agricultural College, Shiraz University, Shiraz, Iran (1852 m above the mean sea level. This soil series with about 745 ha is a deep yellowish brow calcareous soil with textural classes of loam to clay. In the studied soil series 50 sampling locations with the sampling distances of 16, 8 , and 4 m were selected on the relatively regular sampling design. The saturated hydraulic conductivity (Ks, near saturated hydraulic conductivity (Kfs, the power of Gardner equation (α, sorptivity (S, hydraulic diffusivity (D and matric flux potential (Фm of the aforementioned sampling locations was determined using the Single Ring and Droplet methods. After, initial statistical processing, including a normality test of data, trend and stationary analysis of data, the semivariograms of each studied hydraulic attributes were

  4. Integration of the history matching process with the geostatistical modeling in petroleum reservoirs; Integracao do processo de ajuste de historico com a modelagem geoestatistica em reservatorios de petroleo

    Energy Technology Data Exchange (ETDEWEB)

    Maschio, Celio; Schiozer, Denis Jose [Universidade Estadual de Campinas (FEM/UNICAMP), SP (Brazil). Faculdade de Engenharia Mecanica], Emails: celio@dep.fem.unicamp.br, denis@dep.fem.unicamp.br; Vidal, Alexandre Campane [Universidade Estadual de Campinas (IG/UNICAMP), SP (Brazil). Inst. de Geociencias. Dept. de Geologia e Recursos Naturais], E-mail: vidal@ige.unicamp.br

    2008-03-15

    The production history matching process, by which the numerical model is calibrated in order to reproduce the observed field production, is normally carried out separately from the geological modeling. Generally, the construction of the geological model and the history matching process are performed by different teams, such is common uncoupling or a weak coupling between the two areas. This can lead, in the history matching step, inadequate changes in the geological model, resulting sometimes models geologically inconsistent. This work proposes integration between the geostatistical modeling and the history matching through the incorporation of geostatistical realizations into the assisted process. In this way, reservoir parameters such as rock-fluid interaction properties, as well as the images resulted from the realizations are considered in the history matching. In order to find the best parameters combination that adjusts the model to the observed data, an optimization routine based on genetic algorithm is used. The proposed methodology is applied to a synthetic realistic reservoir model. The history matching is carried out in the conventional manner and considering the geostatistical images as history parameters, such the two processes are posteriorly compared. The results show the feasibility and the advantages resulting of this process of integration between the history matching and geostatistical modeling. (author)

  5. 多点地质统计学建模方法研究%Research on Multiple-point geostatistics modeling

    Institute of Scientific and Technical Information of China (English)

    王家华; 于海茂

    2012-01-01

    Multiple-point geostatistics modeling approach could integrate different types of dates, and captured geological structure from the training images, to generate reservoir model more in line with the geological conditions. This paper expound the principles and proce- dures of the multi-point geostatistics modeling. Use the muhi-point geostatistics modeling and variogram-based geostatistics modeling to simulate the same reservoir, and compare the results.%多点地质统计学建模方法能够灵活地整合不同类型的数据并从训练图像中捕获的地质构造,生成更符合地质情况的储层模型。本文论述了多点地质统计学建模的原理及步骤。结合实际案例,进行了多点地质统计学模拟与基于变异函数的两点地质统计学模拟,并将模拟结果进行分析对比。

  6. Analysis of Geostatistical and Deterministic Techniques in the Spatial Variation of Groundwater Depth in the North-western part of Bangladesh

    Directory of Open Access Journals (Sweden)

    Ibrahim Hassan

    2016-06-01

    Full Text Available Various geostatistical and deterministic techniques were used to analyse the spatial variations of groundwater depths. Two different geostatistical methods of ordinary kriging and co-kriging with four semivariogram models, spherical, exponential, circular, Gaussian, and four deterministic methods which are inverse distance weighted (IDW, global polynomial interpolation (GPI, local Polynomial Interpolation (LPI, radial basis function (RBF were used for the estimation of groundwater depths. The study area is in the three Northwestern districts of Bangladesh. Groundwater depth data were recorded from 132 observation wells in the study area over a period of 6 years (2004 to 2009 was considered for the analysis. The spatial interpolation of groundwater depths was then performed using the best-fit model which is geostatistical model selected by comparing the observed RMSE values predicted by the geostatistical and deterministic models and the empirical semi-variogram models. Out of the four semi-variogram models, spherical semi-variogram with cokriging model was considered as the best fitted model for the study area. Result of sensitivity analysis conducted on the input parameters shows that inputs have a strong influence on groundwater levels and the statistical indicators of RMSE and ME suggest that the Co-kriging work best with percolation in predicting the average groundwater table of the study area.

  7. Application of Geostatistical Modelling to Study the Exploration Adequacy of Uniaxial Compressive Strength of Intact Rock alongthe Behesht-Abad Tunnel Route

    Directory of Open Access Journals (Sweden)

    Mohammad Doustmohammadi

    2014-12-01

    Full Text Available Uniaxial compressive strength (UCS is one of the most significant factors on the stability of underground excavation projects. Most of the time, this factor can be obtained by exploratory boreholes evaluation. Due to the large distance between exploratory boreholes in the majority of geotechnical projects, the application of geostatistical methods has increased as an estimator of rock mass properties. The present paper ties the estimation of UCS values of intact rock to the distance between boreholes of the Behesht-Abad tunnel in central Iran, using SGEMS geostatistical program. Variography showed that UCS estimation of intact rock using geostatistical methods is reasonable. The model establishment and validation was done after assessment that the model was trustworthy. Cross validation proved the high accuracy (98% and reliability of the model to estimate uniaxial compressive strength. The UCS values were then estimated along the tunnel axis. Moreover, using geostatistical estimation led to better identification of the pros and cons of geotechnical explorations in each location of tunnel route.

  8. Geostatistical uncertainty of assessing air quality using high-spatial-resolution lichen data: A health study in the urban area of Sines, Portugal.

    Science.gov (United States)

    Ribeiro, Manuel C; Pinho, P; Branquinho, C; Llop, Esteve; Pereira, Maria J

    2016-08-15

    In most studies correlating health outcomes with air pollution, personal exposure assignments are based on measurements collected at air-quality monitoring stations not coinciding with health data locations. In such cases, interpolators are needed to predict air quality in unsampled locations and to assign personal exposures. Moreover, a measure of the spatial uncertainty of exposures should be incorporated, especially in urban areas where concentrations vary at short distances due to changes in land use and pollution intensity. These studies are limited by the lack of literature comparing exposure uncertainty derived from distinct spatial interpolators. Here, we addressed these issues with two interpolation methods: regression Kriging (RK) and ordinary Kriging (OK). These methods were used to generate air-quality simulations with a geostatistical algorithm. For each method, the geostatistical uncertainty was drawn from generalized linear model (GLM) analysis. We analyzed the association between air quality and birth weight. Personal health data (n=227) and exposure data were collected in Sines (Portugal) during 2007-2010. Because air-quality monitoring stations in the city do not offer high-spatial-resolution measurements (n=1), we used lichen data as an ecological indicator of air quality (n=83). We found no significant difference in the fit of GLMs with any of the geostatistical methods. With RK, however, the models tended to fit better more often and worse less often. Moreover, the geostatistical uncertainty results showed a marginally higher mean and precision with RK. Combined with lichen data and land-use data of high spatial resolution, RK is a more effective geostatistical method for relating health outcomes with air quality in urban areas. This is particularly important in small cities, which generally do not have expensive air-quality monitoring stations with high spatial resolution. Further, alternative ways of linking human activities with their

  9. Geostatistical modelling of the malaria risk in Mozambique: effect of the spatial resolution when using remotely-sensed imagery

    Directory of Open Access Journals (Sweden)

    Federica Giardina

    2015-11-01

    Full Text Available The study of malaria spatial epidemiology has benefited from recent advances in geographic information system and geostatistical modelling. Significant progress in earth observation technologies has led to the development of moderate, high and very high resolution imagery. Extensive literature exists on the relationship between malaria and environmental/climatic factors in different geographical areas, but few studies have linked human malaria parasitemia survey data with remote sensing-derived land cover/land use variables and very few have used Earth Observation products. Comparison among the different resolution products to model parasitemia has not yet been investigated. In this study, we probe a proximity measure to incorporate different land cover classes and assess the effect of the spatial resolution of remotely sensed land cover and elevation on malaria risk estimation in Mozambique after adjusting for other environmental factors at a fixed spatial resolution. We used data from the Demographic and Health survey carried out in 2011, which collected malaria parasitemia data on children from 0 to 5 years old, analysing them with a Bayesian geostatistical model. We compared the risk predicted using land cover and elevation at moderate resolution with the risk obtained employing the same variables at high resolution. We used elevation data at moderate and high resolution and the land cover layer from the Moderate Resolution Imaging Spectroradiometer as well as the one produced by MALAREO, a project covering part of Mozambique during 2010-2012 that was funded by the European Union’s 7th Framework Program. Moreover, the number of infected children was predicted at different spatial resolutions using AFRIPOP population data and the enhanced population data generated by the MALAREO project for comparison of estimates. The Bayesian geostatistical model showed that the main determinants of malaria presence are precipitation and day temperature

  10. GIS, geostatistics, metadata banking, and tree-based models for data analysis and mapping in environmental monitoring and epidemiology.

    Science.gov (United States)

    Schröder, Winfried

    2006-05-01

    By the example of environmental monitoring, some applications of geographic information systems (GIS), geostatistics, metadata banking, and Classification and Regression Trees (CART) are presented. These tools are recommended for mapping statistically estimated hot spots of vectors and pathogens. GIS were introduced as tools for spatially modelling the real world. The modelling can be done by mapping objects according to the spatial information content of data. Additionally, this can be supported by geostatistical and multivariate statistical modelling. This is demonstrated by the example of modelling marine habitats of benthic communities and of terrestrial ecoregions. Such ecoregionalisations may be used to predict phenomena based on the statistical relation between measurements of an interesting phenomenon such as, e.g., the incidence of medically relevant species and correlated characteristics of the ecoregions. The combination of meteorological data and data on plant phenology can enhance the spatial resolution of the information on climate change. To this end, meteorological and phenological data have to be correlated. To enable this, both data sets which are from disparate monitoring networks have to be spatially connected by means of geostatistical estimation. This is demonstrated by the example of transformation of site-specific data on plant phenology into surface data. The analysis allows for spatial comparison of the phenology during the two periods 1961-1990 and 1991-2002 covering whole Germany. The changes in both plant phenology and air temperature were proved to be statistically significant. Thus, they can be combined by GIS overlay technique to enhance the spatial resolution of the information on the climate change and use them for the prediction of vector incidences at the regional scale. The localisation of such risk hot spots can be done by geometrically merging surface data on promoting factors. This is demonstrated by the example of the

  11. Geostatistical modelling of the malaria risk in Mozambique: effect of the spatial resolution when using remotely-sensed imagery.

    Science.gov (United States)

    Giardina, Federica; Franke, Jonas; Vounatsou, Penelope

    2015-01-01

    The study of malaria spatial epidemiology has benefited from recent advances in geographic information system and geostatistical modelling. Significant progress in earth observation technologies has led to the development of moderate, high and very high resolution imagery. Extensive literature exists on the relationship between malaria and environmental/climatic factors in different geographical areas, but few studies have linked human malaria parasitemia survey data with remote sensing-derived land cover/land use variables and very few have used Earth Observation products. Comparison among the different resolution products to model parasitemia has not yet been investigated. In this study, we probe a proximity measure to incorporate different land cover classes and assess the effect of the spatial resolution of remotely sensed land cover and elevation on malaria risk estimation in Mozambique after adjusting for other environmental factors at a fixed spatial resolution. We used data from the Demographic and Health survey carried out in 2011, which collected malaria parasitemia data on children from 0 to 5 years old, analysing them with a Bayesian geostatistical model. We compared the risk predicted using land cover and elevation at moderate resolution with the risk obtained employing the same variables at high resolution. We used elevation data at moderate and high resolution and the land cover layer from the Moderate Resolution Imaging Spectroradiometer as well as the one produced by MALAREO, a project covering part of Mozambique during 2010-2012 that was funded by the European Union's 7th Framework Program. Moreover, the number of infected children was predicted at different spatial resolutions using AFRIPOP population data and the enhanced population data generated by the MALAREO project for comparison of estimates. The Bayesian geostatistical model showed that the main determinants of malaria presence are precipitation and day temperature. However, the presence

  12. Geostatistical evaluation of integrated marsh management impact on mosquito vectors using before-after-control-impact (BACI design

    Directory of Open Access Journals (Sweden)

    Dempsey Mary E

    2009-06-01

    Full Text Available Abstract Background In many parts of the world, salt marshes play a key ecological role as the interface between the marine and the terrestrial environments. Salt marshes are also exceedingly important for public health as larval habitat for mosquitoes that are vectors of disease and significant biting pests. Although grid ditching and pesticides have been effective in salt marsh mosquito control, marsh degradation and other environmental considerations compel a different approach. Targeted habitat modification and biological control methods known as Open Marsh Water Management (OMWM had been proposed as a viable alternative to marsh-wide physical alterations and chemical control. However, traditional larval sampling techniques may not adequately assess the impacts of marsh management on mosquito larvae. To assess the effectiveness of integrated OMWM and marsh restoration techniques for mosquito control, we analyzed the results of a 5-year OMWM/marsh restoration project to determine changes in mosquito larval production using GIS and geostatistical methods. Methods The following parameters were evaluated using "Before-After-Control-Impact" (BACI design: frequency and geographic extent of larval production, intensity of larval production, changes in larval habitat, and number of larvicide applications. The analyses were performed using Moran's I, Getis-Ord, and Spatial Scan statistics on aggregated before and after data as well as data collected over time. This allowed comparison of control and treatment areas to identify changes attributable to the OMWM/marsh restoration modifications. Results The frequency of finding mosquito larvae in the treatment areas was reduced by 70% resulting in a loss of spatial larval clusters compared to those found in the control areas. This effect was observed directly following OMWM treatment and remained significant throughout the study period. The greatly reduced frequency of finding larvae in the treatment

  13. Geostatistical Sampling Methods for Efficient Uncertainty Analysis in Flow and Transport Problems

    Science.gov (United States)

    Liodakis, Stylianos; Kyriakidis, Phaedon; Gaganis, Petros

    2015-04-01

    In hydrogeological applications involving flow and transport of in heterogeneous porous media the spatial distribution of hydraulic conductivity is often parameterized in terms of a lognormal random field based on a histogram and variogram model inferred from data and/or synthesized from relevant knowledge. Realizations of simulated conductivity fields are then generated using geostatistical simulation involving simple random (SR) sampling and are subsequently used as inputs to physically-based simulators of flow and transport in a Monte Carlo framework for evaluating the uncertainty in the spatial distribution of solute concentration due to the uncertainty in the spatial distribution of hydraulic con- ductivity [1]. Realistic uncertainty analysis, however, calls for a large number of simulated concentration fields; hence, can become expensive in terms of both time and computer re- sources. A more efficient alternative to SR sampling is Latin hypercube (LH) sampling, a special case of stratified random sampling, which yields a more representative distribution of simulated attribute values with fewer realizations [2]. Here, term representative implies realizations spanning efficiently the range of possible conductivity values corresponding to the lognormal random field. In this work we investigate the efficiency of alternative methods to classical LH sampling within the context of simulation of flow and transport in a heterogeneous porous medium. More precisely, we consider the stratified likelihood (SL) sampling method of [3], in which attribute realizations are generated using the polar simulation method by exploring the geometrical properties of the multivariate Gaussian distribution function. In addition, we propose a more efficient version of the above method, here termed minimum energy (ME) sampling, whereby a set of N representative conductivity realizations at M locations is constructed by: (i) generating a representative set of N points distributed on the

  14. Building a geological reference platform using sequence stratigraphy combined with geostatistical tools

    Science.gov (United States)

    Bourgine, Bernard; Lasseur, Éric; Leynet, Aurélien; Badinier, Guillaume; Ortega, Carole; Issautier, Benoit; Bouchet, Valentin

    2015-04-01

    In 2012 BRGM launched an extensive program to build the new French Geological Reference platform (RGF). Among the objectives of this program is to provide the public with validated, reliable and 3D-consistent geological data, with estimation of uncertainty. Approx. 100,000 boreholes over the whole French national territory provide a preliminary interpretation in terms of depths of main geological interfaces, but with an unchecked, unknown and often low reliability. The aim of this paper is to present the procedure that has been tested on two areas in France, in order to validate (or not) these boreholes, with the aim of being generalized as much as possible to the nearly 100,000 boreholes waiting for validation. The approach is based on the following steps, and includes the management of uncertainty at different steps: (a) Selection of a loose network of boreholes owning a logging or coring information enabling a reliable interpretation. This first interpretation is based on the correlation of well log data and allows defining 3D sequence stratigraphic framework identifying isochronous surfaces. A litho-stratigraphic interpretation is also performed. Be "A" the collection of all boreholes used for this step (typically 3 % of the total number of holes to be validated) and "B" the other boreholes to validate, (b) Geostatistical analysis of characteristic geological interfaces. The analysis is carried out firstly on the "A" type data (to validate the variogram model), then on the "B" type data and at last on "B" knowing "A". It is based on cross-validation tests and evaluation of the uncertainty associated to each geological interface. In this step, we take into account inequality constraints provided by boreholes that do not intersect all interfaces, as well as the "litho-stratigraphic pile" defining the formations and their relationships (depositing surfaces or erosion). The goal is to identify quickly and semi-automatically potential errors among the data, up to

  15. Geostatistical and Stochastic Study of Flow and Transport in the Unsaturated Zone at Yucca Mountain

    Energy Technology Data Exchange (ETDEWEB)

    Ye, Ming; Pan, Feng; Hu, Xiaolong; Zhu, Jianting

    2007-08-14

    Yucca Mountain has been proposed by the U.S. Department of Energy as the nation’s long-term, permanent geologic repository for spent nuclear fuel or high-level radioactive waste. The potential repository would be located in Yucca Mountain’s unsaturated zone (UZ), which acts as a critical natural barrier delaying arrival of radionuclides to the water table. Since radionuclide transport in groundwater can pose serious threats to human health and the environment, it is important to understand how much and how fast water and radionuclides travel through the UZ to groundwater. The UZ system consists of multiple hydrogeologic units whose hydraulic and geochemical properties exhibit systematic and random spatial variation, or heterogeneity, at multiple scales. Predictions of radionuclide transport under such complicated conditions are uncertain, and the uncertainty complicates decision making and risk analysis. This project aims at using geostatistical and stochastic methods to assess uncertainty of unsaturated flow and radionuclide transport in the UZ at Yucca Mountain. Focus of this study is parameter uncertainty of hydraulic and transport properties of the UZ. The parametric uncertainty arises since limited parameter measurements are unable to deterministically describe spatial variability of the parameters. In this project, matrix porosity, permeability and sorption coefficient of the reactive tracer (neptunium) of the UZ are treated as random variables. Corresponding propagation of parametric uncertainty is quantitatively measured using mean, variance, 5th and 95th percentiles of simulated state variables (e.g., saturation, capillary pressure, percolation flux, and travel time). These statistics are evaluated using a Monte Carlo method, in which a three-dimensional flow and transport model implemented using the TOUGH2 code is executed with multiple parameter realizations of the random model parameters. The project specifically studies uncertainty of unsaturated

  16. Spatial variability of isoproturon mineralizing activity within an agricultural field: geostatistical analysis of simple physicochemical and microbiological soil parameters.

    Science.gov (United States)

    El Sebai, T; Lagacherie, B; Soulas, G; Martin-Laurent, F

    2007-02-01

    We assessed the spatial variability of isoproturon mineralization in relation to that of physicochemical and biological parameters in fifty soil samples regularly collected along a sampling grid delimited across a 0.36 ha field plot (40 x 90 m). Only faint relationships were observed between isoproturon mineralization and the soil pH, microbial C biomass, and organic nitrogen. Considerable spatial variability was observed for six of the nine parameters tested (isoproturon mineralization rates, organic nitrogen, genetic structure of the microbial communities, soil pH, microbial biomass and equivalent humidity). The map of isoproturon mineralization rates distribution was similar to that of soil pH, microbial biomass, and organic nitrogen but different from those of structure of the microbial communities and equivalent humidity. Geostatistics revealed that the spatial heterogeneity in the rate of degradation of isoproturon corresponded to that of soil pH and microbial biomass.

  17. 地统计学的可视化研究%Research on the Visualization of Geostatistics

    Institute of Scientific and Technical Information of China (English)

    王玉虎; 赵文吉; 付宗堂

    2008-01-01

    以ArcGIS中Geostatistical Analyst作为平台,就如何利用GIS的图形化手段表达抽象的地统计学结果进行了深入地探讨,并结合怀柔生态规划项目,对居民地的聚集度和绿量率的分布趋势进行了分析.文章最后采用泰森多边形和半方差的方法对数据集内部之间的相关性给出了定量的可视化表达.

  18. Multiquadratic methods, collocation and kriging - comparison with geostatistical model assumptions; Multiquadratische Methode, Kollokation und Kriging - Vergleich unter geostatistischen Modellannahmen

    Energy Technology Data Exchange (ETDEWEB)

    Menz, J. [Technische Univ. Freiburg (Germany). Inst. fuer Markscheidewesen und Geodaesie; Bian Shaofeng [Technical Univ. of Surveying and Mapping, Wuhan (China)

    1998-10-01

    The contribution shows that Hardy`s multisquare method leads to results that are similar in their structure to the predictions by collocation. On the basis of geostatistical model assumptions, equations for calculating the prediction error are presented, and the multisquare method is compared with the collocation method on this basis. Equivalences between collocation and kriging are gone into, and information is presented on how predictions can be improved in the Bayesian sense. [Deutsch] In der folgenden Arbeit soll zuerst gezeigt werden, dass die Multiquadratische Methode nach HARDY zu Vorhersagen fuehrt, die in ihrer Struktur den Vorhersagen durch Kollokation entsprechen. Unter geostatistischen Modellannahmen werden nach dem Fehlerfortpflanzungsgesetz Formeln fuer die Berechnung der Vorhersagefehler angegeben. Auf der Grundlage dieser Formeln wird die Multiquadratische Methode mit der Kollokation verglichen. Es wird auf die Aequivalenzen zwischen Kollokation und Kriging verwiesen und angegeben, wie sich die Vorhersagen im BAYESschen Sinne verbessern lassen. (orig./MSK)

  19. A novel geotechnical/geostatistical approach for exploration and production of natural gas from multiple geologic strata, Phase 1

    Energy Technology Data Exchange (ETDEWEB)

    Overbey, W.K. Jr.; Reeves, T.K.; Salamy, S.P.; Locke, C.D.; Johnson, H.R.; Brunk, R.; Hawkins, L. (BDM Engineering Services Co., Morgantown, WV (United States))

    1991-05-01

    This research program has been designed to develop and verify a unique geostatistical approach for finding natural gas resources. The project has been conducted by Beckley College, Inc., and BDM Engineering Services Company (BDMESC) under contract to the US Department of Energy (DOE), Morgantown Energy Technology Center (METC). This section, Volume II, contains a detailed discussion of the methodology used and the geological and production information collected and analyzed for this study. A companion document, Volume 1, provides an overview of the program, technique and results of the study. In combination, Volumes I and II cover the completion of the research undertaken under Phase I of this DOE project, which included the identification of five high-potential sites for natural gas production on the Eccles Quadrangle, Raleigh County, West Virginia. Each of these sites was selected for its excellent potential for gas production from both relatively shallow coalbeds and the deeper, conventional reservoir formations.

  20. Monte Carlo full-waveform inversion of crosshole GPR data using multiple-point geostatistical a priori information

    DEFF Research Database (Denmark)

    Cordua, Knud Skou; Hansen, Thomas Mejer; Mosegaard, Klaus

    2012-01-01

    We present a general Monte Carlo full-waveform inversion strategy that integrates a priori information described by geostatistical algorithms with Bayesian inverse problem theory. The extended Metropolis algorithm can be used to sample the a posteriori probability density of highly nonlinear...... into account during the inversion. The suggested inversion strategy is tested on synthetic tomographic crosshole ground-penetrating radar full-waveform data using multiple-point-based a priori information. This is, to our knowledge, the first example of obtaining a posteriori realizations of a full......-waveform inverse problem. Benefits of the proposed methodology compared with deterministic inversion approaches include: (1) The a posteriori model variability reflects the states of information provided by the data uncertainties and a priori information, which provides a means of obtaining resolution analysis. (2...

  1. Geostatistical modeling of facies, bitumen grade and particle size distribution for the Joslyn oil sand open pit mine project

    Energy Technology Data Exchange (ETDEWEB)

    Babak, Olena; Insalaco, Enzo; Mittler, Andreas [Total EandP Canada Ltd. (Canada)

    2011-07-01

    The Joslyn North Mine Project is currently in the pre-development stage; the aim of this study is to use different available data to draw a geological model of facies, bitumen grade, full particle size distribution (PSD) and ore/waste discrimination. The study was conducted with the database of around 800 wells, stochastic, indicator and Gaussian simulations were performed along with a sensitivity study. Results demonstrated the importance of some parameters for evaluating grade cases including variogram uncertainty, sampling limitations and errors in geostatistical workflow. In addition, modeling the full PSD dataset was shown to be useful. This study demonstrated how to use available database through an overall workflow to develop case scenarios for bitumen in place in ore and characterize the ore material.

  2. Geostatistical analysis of groundwater level using Euclidean and non-Euclidean distance metrics and variable variogram fitting criteria

    Science.gov (United States)

    Theodoridou, Panagiota G.; Karatzas, George P.; Varouchakis, Emmanouil A.; Corzo Perez, Gerald A.

    2015-04-01

    Groundwater level is an important information in hydrological modelling. Geostatistical methods are often employed to map the free surface of an aquifer. In geostatistical analysis using Kriging techniques the selection of the optimal variogram model is very important for the optimal method performance. This work compares three different criteria, the least squares sum method, the Akaike Information Criterion and the Cressie's Indicator, to assess the theoretical variogram that fits to the experimental one and investigates the impact on the prediction results. Moreover, five different distance functions (Euclidean, Minkowski, Manhattan, Canberra, and Bray-Curtis) are applied to calculate the distance between observations that affects both the variogram calculation and the Kriging estimator. Cross validation analysis in terms of Ordinary Kriging is applied by using sequentially a different distance metric and the above three variogram fitting criteria. The spatial dependence of the observations in the tested dataset is studied by fitting classical variogram models and the Matérn model. The proposed comparison analysis performed for a data set of two hundred fifty hydraulic head measurements distributed over an alluvial aquifer that covers an area of 210 km2. The study area is located in the Prefecture of Drama, which belongs to the Water District of East Macedonia (Greece). This area was selected in terms of hydro-geological data availability and geological homogeneity. The analysis showed that a combination of the Akaike information Criterion for the variogram fitting assessment and the Brays-Curtis distance metric provided the most accurate cross-validation results. The Power-law variogram model provided the best fit to the experimental data. The aforementioned approach for the specific dataset in terms of the Ordinary Kriging method improves the prediction efficiency in comparison to the classical Euclidean distance metric. Therefore, maps of the spatial

  3. Spatially explicit burden estimates of malaria in Tanzania: bayesian geostatistical modeling of the malaria indicator survey data.

    Directory of Open Access Journals (Sweden)

    Laura Gosoniu

    Full Text Available A national HIV/AIDS and malaria parasitological survey was carried out in Tanzania in 2007-2008. In this study the parasitological data were analyzed: i to identify climatic/environmental, socio-economic and interventions factors associated with child malaria risk and ii to produce a contemporary, high spatial resolution parasitaemia risk map of the country. Bayesian geostatistical models were fitted to assess the association between parasitaemia risk and its determinants. bayesian kriging was employed to predict malaria risk at unsampled locations across Tanzania and to obtain the uncertainty associated with the predictions. Markov chain Monte Carlo (MCMC simulation methods were employed for model fit and prediction. Parasitaemia risk estimates were linked to population data and the number of infected children at province level was calculated. Model validation indicated a high predictive ability of the geostatistical model, with 60.00% of the test locations within the 95% credible interval. The results indicate that older children are significantly more likely to test positive for malaria compared with younger children and living in urban areas and better-off households reduces the risk of infection. However, none of the environmental and climatic proxies or the intervention measures were significantly associated with the risk of parasitaemia. Low levels of malaria prevalence were estimated for Zanzibar island. The population-adjusted prevalence ranges from 0.29% in Kaskazini province (Zanzibar island to 18.65% in Mtwara region. The pattern of predicted malaria risk is similar with the previous maps based on historical data, although the estimates are lower. The predicted maps could be used by decision-makers to allocate resources and target interventions in the regions with highest burden of malaria in order to reduce the disease transmission in the country.

  4. A multiple-point geostatistical method for characterizing uncertainty of subsurface alluvial units and its effects on flow and transport

    Science.gov (United States)

    Cronkite-Ratcliff, C.; Phelps, G.A.; Boucher, A.

    2012-01-01

    This report provides a proof-of-concept to demonstrate the potential application of multiple-point geostatistics for characterizing geologic heterogeneity and its effect on flow and transport simulation. The study presented in this report is the result of collaboration between the U.S. Geological Survey (USGS) and Stanford University. This collaboration focused on improving the characterization of alluvial deposits by incorporating prior knowledge of geologic structure and estimating the uncertainty of the modeled geologic units. In this study, geologic heterogeneity of alluvial units is characterized as a set of stochastic realizations, and uncertainty is indicated by variability in the results of flow and transport simulations for this set of realizations. This approach is tested on a hypothetical geologic scenario developed using data from the alluvial deposits in Yucca Flat, Nevada. Yucca Flat was chosen as a data source for this test case because it includes both complex geologic and hydrologic characteristics and also contains a substantial amount of both surface and subsurface geologic data. Multiple-point geostatistics is used to model geologic heterogeneity in the subsurface. A three-dimensional (3D) model of spatial variability is developed by integrating alluvial units mapped at the surface with vertical drill-hole data. The SNESIM (Single Normal Equation Simulation) algorithm is used to represent geologic heterogeneity stochastically by generating 20 realizations, each of which represents an equally probable geologic scenario. A 3D numerical model is used to simulate groundwater flow and contaminant transport for each realization, producing a distribution of flow and transport responses to the geologic heterogeneity. From this distribution of flow and transport responses, the frequency of exceeding a given contaminant concentration threshold can be used as an indicator of uncertainty about the location of the contaminant plume boundary.

  5. A case study of spatial heterogeneity of soil moisture in the Loess Plateau,western China:A geostatistical approach

    Institute of Scientific and Technical Information of China (English)

    BI Huaxing; LI Xiaoyin; LIU Xin; GUO Mengxia; LI Jun

    2009-01-01

    Soil moisture distribution shows highly variation both spatially and temporally.This study assesses the spatial heterogeneity of soil moisture on a hill-slope scale in the Loess Plateau in West China by using a geostatistical approach.Soil moisture was measured by time-domain reflectometry (TDR) in 313 samples.Two kinds of sampling scales were used (2 × 2 m and 20 × 20m) at two soil layers (0-30 cm and 30-450 cm).The general characteristics of soil moisture were analyzed by a classical statistics method,and the spatial heterogeneity of soil moisture was analyzed using a geostatistical approach.The results showed that the spherical model is the best-fit model to simulate soil moisture on the experimental hill-slope.The parameters of this model indicated that the spatial dependence of soil moisture in the selected hill-slope was moderate.Even the 2 × 2 m sampling scale was too coarse to show the detailed spatial variances of soil moisture in this area.The dependent distance increased from 27.4 m to 494.16 m as the sampling scale became coarse (from 2 ×2 m to 20 × 20 m).A map of soil moisture was generated by using original soil moisture data and interpolated values determined by the Kriging method.The average soil moisture (area weighted) in the different layers of soil was calculated on the basis of this map (10.94% for the 0-30 cm soil layer,11.88% for the 30-60 em soil layer).This average soil moisture is lower than the corresponding average effective soil moisture,which suggests that the soil moisture is not sufficient to support vegetation in this area.

  6. Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural Malawi

    Science.gov (United States)

    Chipeta, Michael G.; McCann, Robert S.; Phiri, Kamija S.; van Vugt, Michèle; Takken, Willem; Diggle, Peter; Terlouw, Anja D.

    2017-01-01

    Introduction In the context of malaria elimination, interventions will need to target high burden areas to further reduce transmission. Current tools to monitor and report disease burden lack the capacity to continuously detect fine-scale spatial and temporal variations of disease distribution exhibited by malaria. These tools use random sampling techniques that are inefficient for capturing underlying heterogeneity while health facility data in resource-limited settings are inaccurate. Continuous community surveys of malaria burden provide real-time results of local spatio-temporal variation. Adaptive geostatistical design (AGD) improves prediction of outcome of interest compared to current random sampling techniques. We present findings of continuous malaria prevalence surveys using an adaptive sampling design. Methods We conducted repeated cross sectional surveys guided by an adaptive sampling design to monitor the prevalence of malaria parasitaemia and anaemia in children below five years old in the communities living around Majete Wildlife Reserve in Chikwawa district, Southern Malawi. AGD sampling uses previously collected data to sample new locations of high prediction variance or, where prediction exceeds a set threshold. We fitted a geostatistical model to predict malaria prevalence in the area. Findings We conducted five rounds of sampling, and tested 876 children aged 6–59 months from 1377 households over a 12-month period. Malaria prevalence prediction maps showed spatial heterogeneity and presence of hotspots—where predicted malaria prevalence was above 30%; predictors of malaria included age, socio-economic status and ownership of insecticide-treated mosquito nets. Conclusions Continuous malaria prevalence surveys using adaptive sampling increased malaria prevalence prediction accuracy. Results from the surveys were readily available after data collection. The tool can assist local managers to target malaria control interventions in areas with the

  7. Interannual Changes in Biomass Affect the Spatial Aggregations of Anchovy and Sardine as Evidenced by Geostatistical and Spatial Indicators.

    Directory of Open Access Journals (Sweden)

    Marco Barra

    Full Text Available Geostatistical techniques were applied and a series of spatial indicators were calculated (occupation, aggregation, location, dispersion, spatial autocorrelation and overlap to characterize the spatial distributions of European anchovy and sardine during summer. Two ecosystems were compared for this purpose, both located in the Mediterranean Sea: the Strait of Sicily (upwelling area and the North Aegean Sea (continental shelf area, influenced by freshwater. Although the biomass of anchovy and sardine presented high interannual variability in both areas, the location of the centres of gravity and the main spatial patches of their populations were very similar between years. The size of the patches representing the dominant part of the abundance (80% was mostly ecosystem- and species-specific. Occupation (area of presence appears to be shaped by the extent of suitable habitats in each ecosystem whereas aggregation patterns (how the populations are distributed within the area of presence were species-specific and related to levels of population biomass. In the upwelling area, both species showed consistently higher occupation values compared to the continental shelf area. Certain characteristics of the spatial distribution of sardine (e.g. spreading area, overlapping with anchovy differed substantially between the two ecosystems. Principal component analysis of geostatistical and spatial indicators revealed that biomass was significantly related to a suite of, rather than single, spatial indicators. At the spatial scale of our study, strong correlations emerged between biomass and the first principal component axis with highly positive loadings for occupation, aggregation and patchiness, independently of species and ecosystem. Overlapping between anchovy and sardine increased with the increase of sardine biomass but decreased with the increase of anchovy. This contrasting pattern was attributed to the location of the respective major patches

  8. The distribution of arsenic in shallow alluvial groundwater under agricultural land in central Portugal: insights from multivariate geostatistical modeling.

    Science.gov (United States)

    Andrade, A I A S S; Stigter, T Y

    2013-04-01

    In this study multivariate and geostatistical methods are jointly applied to model the spatial and temporal distribution of arsenic (As) concentrations in shallow groundwater as a function of physicochemical, hydrogeological and land use parameters, as well as to assess the related uncertainty. The study site is located in the Mondego River alluvial body in Central Portugal, where maize, rice and some vegetable crops dominate. In a first analysis scatter plots are used, followed by the application of principal component analysis to two different data matrices, of 112 and 200 samples, with the aim of detecting associations between As levels and other quantitative parameters. In the following phase explanatory models of As are created through factorial regression based on correspondence analysis, integrating both quantitative and qualitative parameters. Finally, these are combined with indicator-geostatistical techniques to create maps indicating the predicted probability of As concentrations in groundwater exceeding the current global drinking water guideline of 10 μg/l. These maps further allow assessing the uncertainty and representativeness of the monitoring network. A clear effect of the redox state on the presence of As is observed, and together with significant correlations with dissolved oxygen, nitrate, sulfate, iron, manganese and alkalinity, points towards the reductive dissolution of Fe (hydr)oxides as the essential mechanism of As release. The association of high As values with rice crop, known to promote reduced environments due to ponding, further corroborates this hypothesis. An additional source of As from fertilizers cannot be excluded, as the correlation with As is higher where rice is associated with vegetables, normally associated with higher fertilization rates. The best explanatory model of As occurrence integrates the parameters season, crop type, well and water depth, nitrate and Eh, though a model without the last two parameters also gives

  9. Geostatistics and multivariate analysis as a tool to characterize volcaniclastic deposits: Application to Nevado de Toluca volcano, Mexico

    Science.gov (United States)

    Bellotti, F.; Capra, L.; Sarocchi, D.; D'Antonio, M.

    2010-03-01

    Grain size analysis of volcaniclastic deposits is mainly used to study flow transport and depositional processes, in most cases by comparing some statistical parameters and how they change with distance from the source. In this work the geospatial and multivariate analyses are presented as a strong adaptable geostatistical tool applied to volcaniclastic deposits in order to provide an effective and relatively simple methodology for texture description, deposit discrimination and interpretation of depositional processes. We choose the case of Nevado de Toluca volcano (Mexico) due to existing knowledge of its geological evolution, stratigraphic succession and spatial distribution of volcaniclastic units. Grain size analyses and frequency distribution curves have been carried out to characterize and compare the 28-ka block-and-ash flow deposit associated to a dome destruction episode, and the El Morral debris avalanche deposit originated from the collapse of the south-eastern sector of the volcano. The geostatistical interpolation of sedimentological data allows to realize bidimensional maps draped over the volcano topography, showing the granulometric distribution, sorting and fine material concentration into the whole deposit with respect to topographic changes. In this way, it is possible to analyze a continuous surface of the grain size distribution of volcaniclastic deposits and better understand flow transport processes. The application of multivariate statistic analysis (discriminant function) indicates that this methodology could be useful in discriminating deposits with different origin or different depositional lithofacies within the same deposit. The proposed methodology could be an interesting approach to sustain more classical analysis of volcaniclastic deposits, especially where a clear field classification appears problematic because of a homogeneous texture of the deposits or their scarce and discontinuous outcrops. Our study is an example of the

  10. Geostatistical validation and cross-validation of magnetometric measurements of soil pollution with Potentially Toxic Elements in problematic areas

    Science.gov (United States)

    Fabijańczyk, Piotr; Zawadzki, Jarosław

    2016-04-01

    Field magnetometry is fast method that was previously effectively used to assess the potential soil pollution. One of the most popular devices that are used to measure the soil magnetic susceptibility on the soil surface is a MS2D Bartington. Single reading using MS2D device of soil magnetic susceptibility is low time-consuming but often characterized by considerable errors related to the instrument or environmental and lithogenic factors. In this connection, measured values of soil magnetic susceptibility have to be usually validated using more precise, but also much more expensive, chemical measurements. The goal of this study was to analyze validation methods of magnetometric measurements using chemical analyses of a concentration of elements in soil. Additionally, validation of surface measurements of soil magnetic susceptibility was performed using selected parameters of a distribution of magnetic susceptibility in a soil profile. Validation was performed using selected geostatistical measures of cross-correlation. The geostatistical approach was compared with validation performed using the classic statistics. Measurements were performed at selected areas located in the Upper Silesian Industrial Area in Poland, and in the selected parts of Norway. In these areas soil magnetic susceptibility was measured on the soil surface using a MS2D Bartington device and in the soil profile using MS2C Bartington device. Additionally, soil samples were taken in order to perform chemical measurements. Acknowledgment The research leading to these results has received funding from the Polish-Norwegian Research Programme operated by the National Centre for Research and Development under the Norwegian Financial Mechanism 2009-2014 in the frame of Project IMPACT - Contract No Pol-Nor/199338/45/2013.

  11. Spatial Distribution of Soil Organic Matter Using Geostatistics: A Key Indicator to Assess Soil Degradation Status in Central Italy

    Institute of Scientific and Technical Information of China (English)

    A.MARCHETTI; C.PICCINI; R.FRANCAVIGLIA; L.MABIT

    2012-01-01

    Soil organic matter (SOM) content is one of the main factors to be considered in the evaluation of soil health and fertility.As timing,human and monetary resources often limit the amount of available data,geostatistical techniques provide a valid scientific approach to cope with spatial variability,to interpolate existing data and to predict values at unsampled locations for accurate SOM status survey.Using geostatistical and geographic information system (GIS) approaches,the spatial variability of some physical and chemical soil parameters was investigated under Mediterranean climatic condition in the Abruzzo region of central Italy,where soil erosion processes accelerated by human induced factors are the main causes of soil degradation associated with low SOM content.Experimental semivariograms were established to determine the spatial dependence of the soil variables under investigation.The results of 250 soil sampling point data were interpolated by means of ordinary kriging coupled with a GIS to produce contour maps distribution of soil texture,SOM content related to texture,and C/N ratio.The resulting spatial interpolation of the dataset highlighted a low content of SOM in relation with soil texture in most of the surveyed area (87%) and an optimal C/N ratio for only half of the investigated surface area.Spatial location of degraded area and the assessment of its magnitude can provide decision makers with an accurate support to design appropriate soil conservation strategies and then facilitate a regional planning of agri-environmental measures in the framework of the European Common Agricultural Policy.

  12. Geostatistical Analyses of Soil Organic Carbon Concentrations in Aligodarz Watershed, Lorestan Province

    Directory of Open Access Journals (Sweden)

    Masoud Davari

    2017-01-01

    distribution of SOC were carried out with the geostatistical software GS+ (version 5. 1. Maps were generated by using ILWIS (version 3.3 GIS software. Results and Discussion: The results revealed that the raw SOC data have a long tail towards higher concentrations, whereas that squareroot transformed data can be satisfactorily modelled by a normal distribution. The probability distribution of SOC appeared to be positively skewed and have a positive kurtosis. The square root transformed data showed small skewness and kurtosis, and passed the K–S normality test at a significance level of higher than 0.05. Therefore, the square root transformed data of SOC was used for analyses. The SOC concentration varied from 0.08 to 2.39%, with an arithmetic mean of 0.81% and geometric mean of 0.73%. The coefficient of variation (CV, as an index of overall variability of SOC, was 44.49%. According to the classification system presented by Nielson and Bouma (1985, a variable is moderately varying if the CV is between 10% and 100%. Therefore, the content of SOC in the Aligodarz watershed can be considered to be in moderate variability. The experimental variogram of SOC was fitted by an exponential model. The values of the range, nugget, sill, and nugget/sill ratio of the best-fitted model were 6.80 km, 0.058, 0.133, and 43.6%, respectively. The positive nugget value can be explained by sampling error, short range variability, and unexplained and inherent variability. The nugget/sill ratio of 43.6% showed a moderate spatial dependence of SOC in the study area. The parameters of the exponential smivariogram model were used for kriging method to produce a spatial distribution map of SOC in the study area. The interpolated values ranged between 0.30 and 1.40%. Southern and central parts of this study area have the highest SOC concentrations, while the northern parts have the lowest concentrations of SOC. Kriging results also showed that the major parts of the Aligodarz watershed (about 87% have

  13. Study of the permeability up-scaling by direct filtering of geostatistical model; Etude du changement d'echelle des permeabilites par filtrage direct du modele geostatistique

    Energy Technology Data Exchange (ETDEWEB)

    Zargar, G.

    2005-10-15

    In this thesis, we present a new approach, which consists in directly up-scaling the geostatistical permeability distribution rather than the individual realizations. Practically, filtering techniques based on. the FFT (Fast Fourier Transform), allows us to generate geostatistical images, which sample the up-scaled distributions. In the log normal case, an equivalence hydraulic criterion is proposed, allowing to re-estimate the geometric mean of the permeabilities. In the anisotropic case, the effective geometric mean becomes a tensor which depends on the level of filtering used and it can be calculated by a method of renormalisation. Then, the method was generalized for the categorial model. Numerical tests of the method were set up for isotropic, anisotropic and categorial models, which shows good agreement with theory. (author)

  14. [Multivariate geostatistics and GIS-based approach to study the spatial distribution and sources of heavy metals in agricultural soil in the Pearl River Delta, China].

    Science.gov (United States)

    Cai, Li-mei; Ma, Jin; Zhou, Yong-zhang; Huang, Lan-chun; Dou, Lei; Zhang, Cheng-bo; Fu, Shan-ming

    2008-12-01

    One hundred and eighteen surface soil samples were collected from the Dongguan City, and analyzed for concentration of Cu, Zn, Ni, Cr, Pb, Cd, As, Hg, pH and OM. The spatial distribution and sources of soil heavy metals were studied using multivariate geostatistical methods and GIS technique. The results indicated concentrations of Cu, Zn, Ni, Pb, Cd and Hg were beyond the soil background content in Guangdong province, and especially concentrations of Pb, Cd and Hg were greatly beyond the content. The results of factor analysis group Cu, Zn, Ni, Cr and As in Factor 1, Pb and Hg in Factor 2 and Cd in Factor 3. The spatial maps based on geostatistical analysis show definite association of Factor 1 with the soil parent material, Factor 2 was mainly affected by industries. The spatial distribution of Factor 3 was attributed to anthropogenic influence.

  15. Geostatistical analysis of space variation in underground water various quality parameters in Kłodzko water intake area (SW part of Poland)

    Science.gov (United States)

    Namysłowska-Wilczyńska, Barbara

    2016-09-01

    This paper presents selected results of research connected with the development of a (3D) geostatistical hydrogeochemical model of the Kłodzko Drainage Basin, dedicated to the spatial variation in the different quality parameters of underground water in the water intake area (SW part of Poland). The research covers the period 2011-2012. Spatial analyses of the variation in various quality parameters, i.e., contents of: iron, manganese, ammonium ion, nitrate ion, phosphate ion, total organic carbon, pH redox potential and temperature, were carried out on the basis of the chemical determinations of the quality parameters of underground water samples taken from the wells in the water intake area. Spatial variation in the parameters was analyzed on the basis of data obtained (November 2011) from tests of water taken from 14 existing wells with a depth ranging from 9.5 to 38.0 m b.g.l. The latest data (January 2012) were obtained (gained) from 3 new piezometers, made in other locations in the relevant area. A depth of these piezometers amounts to 9-10 m. Data derived from 14 wells (2011) and 14 wells + 3 piezometers (2012) were subjected to spatial analyses using geostatistical methods. The evaluation of basic statistics of the quality parameters, including their histograms of distributions, scatter diagrams and correlation coefficient values r were presented. The directional semivariogram function γ(h) and the ordinary (block) kriging procedure were used to build the 3D geostatistical model. The geostatistical parameters of the theoretical models of directional semivariograms of the water quality parameters under study, calculated along the wells depth (taking into account the terrain elevation), were used in the ordinary (block) kriging estimation. The obtained results of estimation, i.e., block diagrams allowed us to determine the levels of increased values of estimated averages Z* of underground water quality parameters.

  16. 地质统计学反演在海安凹陷中的应用%Application of geostatistical inversion technique in Haian depression

    Institute of Scientific and Technical Information of China (English)

    张勇; 钟薇

    2015-01-01

    地质统计学反演基于地质统计学方法,对储层的空间分布特征进行模拟,预测储层分布规律。相对于常规约束稀疏脉冲反演,地质统计学反演不再受限于地震资料频带宽度,能有效提高反演结果纵向分辨率,识别厚度较小的储层。以海安凹陷曲塘深凹为例,利用地震、测井资料和地质认识,在稀疏脉冲波阻抗反演基础上开展地质统计学反演,进行砂体展布预测。研究结果与钻井资料吻合度较高,符合地质认识,为认识该区砂体分布提供了依据,为寻找有利区奠定了基础。%Geostatistical inversion based on geostatistical method simulate the spatial distribution characteristics of reservoir and predict the reservoir distribution. Compared with conventional constrained sparse spike inversion, the geostatistical inversion no longer limited to the bandwidth of seismic data can effectively improve the vertical resolution of the inversion results and identify thickness smaller reservoir. Taking Qutang deep pit of Haian depression as an example, and by using seismic, logging data and geo⁃logical understanding, geostatistical inversion based on constrained sparse spike inversion was conduct to predict the sandbody res⁃ervoir distribution. The results highly consistent with the drilling data and geological understanding, provide basis for recognizing the sandbody distribution of this area and lay the foundation of finding advantage areas.

  17. Optimisation of groundwater level monitoring networks using geostatistical modelling based on the Spartan family variogram and a genetic algorithm method

    Science.gov (United States)

    Parasyris, Antonios E.; Spanoudaki, Katerina; Kampanis, Nikolaos A.

    2016-04-01

    Groundwater level monitoring networks provide essential information for water resources management, especially in areas with significant groundwater exploitation for agricultural and domestic use. Given the high maintenance costs of these networks, development of tools, which can be used by regulators for efficient network design is essential. In this work, a monitoring network optimisation tool is presented. The network optimisation tool couples geostatistical modelling based on the Spartan family variogram with a genetic algorithm method and is applied to Mires basin in Crete, Greece, an area of high socioeconomic and agricultural interest, which suffers from groundwater overexploitation leading to a dramatic decrease of groundwater levels. The purpose of the optimisation tool is to determine which wells to exclude from the monitoring network because they add little or no beneficial information to groundwater level mapping of the area. Unlike previous relevant investigations, the network optimisation tool presented here uses Ordinary Kriging with the recently-established non-differentiable Spartan variogram for groundwater level mapping, which, based on a previous geostatistical study in the area leads to optimal groundwater level mapping. Seventy boreholes operate in the area for groundwater abstraction and water level monitoring. The Spartan variogram gives overall the most accurate groundwater level estimates followed closely by the power-law model. The geostatistical model is coupled to an integer genetic algorithm method programmed in MATLAB 2015a. The algorithm is used to find the set of wells whose removal leads to the minimum error between the original water level mapping using all the available wells in the network and the groundwater level mapping using the reduced well network (error is defined as the 2-norm of the difference between the original mapping matrix with 70 wells and the mapping matrix of the reduced well network). The solution to the

  18. Geostatistical Analysis of Population Density and the Change of Land Cover and Land Use in the Komadugu-Yobe River Basin in Nigeria

    Science.gov (United States)

    Tobar, I.; Lee, J.; Black, F. W.; Babamaaji, R. A.

    2014-12-01

    The Komadugu-Yobe River Basin in northeastern Nigeria is an important tributary of Lake Chad and has experienced significant changes in population density and land cover in recent decades. The present study focuses on the application of geostatistical methods to examine the land cover and population density dynamics in the river basin. The geostatistical methods include spatial autocorrelation, overlapping neighborhood statistics with Pearson's correlation coefficient, Moran's I index analysis, and indicator variogram analysis with rose diagram. The land cover and land use maps were constructed from USGS Landsat images and Globcover images from the European Space Agency. The target years of the analysis are 1970, 1986, 2000, 2005, and 2009. The calculation of net changes in land cover indicates significant variation in the changes of rainfed cropland, mosaic cropland, and grassland. Spatial autocorrelation analysis and Moran I index analysis showed that the distribution of land cover is highly clustered. A new GIS geostatistical tool was designed to calculate the overlapping neighborhood statistics with Pearson's correlation coefficient between the land use/land cover and population density datasets. The 10x10 neighborhood cell unit showed a clear correlation between the variables in certain zones of the study area. The ranges calculated from the indicator variograms of land use and land cover and population density showed that the cropland and sparse vegetation are most closely related to the spatial change of population density.

  19. Health risks from arsenic-contaminated soil in Flin Flon-Creighton, Canada: Integrating geostatistical simulation and dose-response model

    Energy Technology Data Exchange (ETDEWEB)

    Zhang Hua [Environmental Systems Engineering Program, Faculty of Engineering, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada); Huang Guohe, E-mail: huang@iseis.or [Environmental Systems Engineering Program, Faculty of Engineering, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada); Zeng Guangming [College of Environmental Science and Engineering, Hunan University, Changsha, Hunan 410082 (China)

    2009-08-15

    Elevated concentrations of arsenic were detected in surface soils adjacent to a smelting complex in northern Canada. We evaluated the cancer risks caused by exposure to arsenic in two communities through combining geostatistical simulation with demographic data and dose-response models in a framework. Distribution of arsenic was first estimated using geostatistical circulant-embedding simulation method. We then evaluated the exposures from inadvertent ingestion, inhalation and dermal contact. Risks of skin caner and three internal cancers were estimated at both grid scale and census-unit scale using parametric dose-response models. Results indicated that local residents could face non-negligible cancer risks (skin cancer and liver cancer mainly). Uncertainties of risk estimates were discussed from the aspects of arsenic concentrations, exposed population and dose-response model. Reducing uncertainties would require additional soil sampling, epidemic records as well as complementary studies on land use, demographic variation, outdoor activities and bioavailability of arsenic. - Cancer risks induced by arsenic in soil were evaluated using geostatistical simulation and dose-response model.

  20. Hierarchical probabilistic regionalization of volcanism for Sengan region in Japan using multivariate statistical techniques and geostatistical interpolation techniques

    Energy Technology Data Exchange (ETDEWEB)

    Park, Jinyong [Univ. of Arizona, Tucson, AZ (United States); Balasingham, P [Univ. of Arizona, Tucson, AZ (United States); McKenna, Sean Andrew [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kulatilake, Pinnaduwa H.S.W. [Univ. of Arizona, Tucson, AZ (United States)

    2004-09-01

    Sandia National Laboratories, under contract to Nuclear Waste Management Organization of Japan (NUMO), is performing research on regional classification of given sites in Japan with respect to potential volcanic disruption using multivariate statistics and geo-statistical interpolation techniques. This report provides results obtained for hierarchical probabilistic regionalization of volcanism for the Sengan region in Japan by applying multivariate statistical techniques and geostatistical interpolation techniques on the geologic data provided by NUMO. A workshop report produced in September 2003 by Sandia National Laboratories (Arnold et al., 2003) on volcanism lists a set of most important geologic variables as well as some secondary information related to volcanism. Geologic data extracted for the Sengan region in Japan from the data provided by NUMO revealed that data are not available at the same locations for all the important geologic variables. In other words, the geologic variable vectors were found to be incomplete spatially. However, it is necessary to have complete geologic variable vectors to perform multivariate statistical analyses. As a first step towards constructing complete geologic variable vectors, the Universal Transverse Mercator (UTM) zone 54 projected coordinate system and a 1 km square regular grid system were selected. The data available for each geologic variable on a geographic coordinate system were transferred to the aforementioned grid system. Also the recorded data on volcanic activity for Sengan region were produced on the same grid system. Each geologic variable map was compared with the recorded volcanic activity map to determine the geologic variables that are most important for volcanism. In the regionalized classification procedure, this step is known as the variable selection step. The following variables were determined as most important for volcanism: geothermal gradient, groundwater temperature, heat discharge, groundwater

  1. Spatial heterogeneity and risk factors for stunting among children under age five in Ethiopia: A Bayesian geo-statistical model

    Science.gov (United States)

    Hagos, Seifu; Hailemariam, Damen; WoldeHanna, Tasew; Lindtjørn, Bernt

    2017-01-01

    Background Understanding the spatial distribution of stunting and underlying factors operating at meso-scale is of paramount importance for intervention designing and implementations. Yet, little is known about the spatial distribution of stunting and some discrepancies are documented on the relative importance of reported risk factors. Therefore, the present study aims at exploring the spatial distribution of stunting at meso- (district) scale, and evaluates the effect of spatial dependency on the identification of risk factors and their relative contribution to the occurrence of stunting and severe stunting in a rural area of Ethiopia. Methods A community based cross sectional study was conducted to measure the occurrence of stunting and severe stunting among children aged 0–59 months. Additionally, we collected relevant information on anthropometric measures, dietary habits, parent and child-related demographic and socio-economic status. Latitude and longitude of surveyed households were also recorded. Local Anselin Moran's I was calculated to investigate the spatial variation of stunting prevalence and identify potential local pockets (hotspots) of high prevalence. Finally, we employed a Bayesian geo-statistical model, which accounted for spatial dependency structure in the data, to identify potential risk factors for stunting in the study area. Results Overall, the prevalence of stunting and severe stunting in the district was 43.7% [95%CI: 40.9, 46.4] and 21.3% [95%CI: 19.5, 23.3] respectively. We identified statistically significant clusters of high prevalence of stunting (hotspots) in the eastern part of the district and clusters of low prevalence (cold spots) in the western. We found out that the inclusion of spatial structure of the data into the Bayesian model has shown to improve the fit for stunting model. The Bayesian geo-statistical model indicated that the risk of stunting increased as the child’s age increased (OR 4.74; 95% Bayesian credible

  2. Spatial Prediction of Soil Aggregate Stability and Aggregate-Associated Organic Carbon Content at the Catchment Scale Using Geostatistical Techniques

    Institute of Scientific and Technical Information of China (English)

    J.MOHAMMADI; M.H.MOTAGHIAN

    2011-01-01

    The association of organic carbon with secondary parzicles (aggregates) results in its storage and retention in soil. A study was carried out at a catchment covering about 92 km2 to predict spatial variability of soil water-stable aggregates (WSA), mean weight diameter (MWD) of aggregates and organic carbon (OC) content in macro- (> 2 mm), meso- (1-2 mm), and micro-aggregate (< 1 mm) fractions, using geostatistical methods. One hundred and eleven soil samples were c(o)llected at the 0-10 cm depth and fractionated into macro-, meso-, and micro-aggregates by wet sieving. The OC content was determined for each fraction. A greater percentage of water-stable aggregates was found for micro-aggregates, followed by meso-aggregates. Aggregate OC content was greatest in meso-aggregates (9 g kg-1), followed by micro-aggregates (7 g kg-1), while the least OC content was found in macro-aggregates (3 g kg-1). Although a significart effect (P = 0.000) of aggregate size on aggregate OC content was found, however, our findings did not support the model of aggregate hierarchy.Land use had a significant effect (P = 0.073) on aggregate OC content. The coefficients of variation (CVs) for OC contents associated with each aggregate fraction indicated macro-aggregates as the most variable (CV = 71%). Among the aggregate fractions, the micro-aggregate fraction had a lower CV value of 27%. The mean content of WSA ranged from 15% for macro-aggregates to 84% for micro-aggregates. Geostatistical analysis showed that the measured soil variables exhibited differences in their spatial patterns in both magnitude and space at each aggregate size fraction. The relative nugget variance for most aggregate-associated properties was lower than 45%. The range value for the variogram of water-stable aggregates was almost similar (about 3 km) for the three studied aggregate size classes. The range value for the variogram of aggregate-associated OC contents ranged from about 3 km for macro

  3. Based on Geostatistical Analyst module analysis the nutrient spatial variation characteristics in karst region soil%基于Geostatistical Analyst的喀斯特地区土壤养分空间变异研究

    Institute of Scientific and Technical Information of China (English)

    陆锦; 任晓冬; 刘洪云

    2015-01-01

    基于ARCGIS平台Geostatistical Analyst模块对喀斯特地区土壤养分空间变异特征进行研究,以贵州省黔西县韦寨村为研究区,首先对研究区内耕地土壤进行取样,深度为0~ 20cm,并将研究区划分为两片,采样方式分别为25m* 25m和50m* 50m.通过实验得出土壤中全氮、有效磷、速效钾、有机质含量和pH值.采用GIS地统计分析和常规统计方法对土壤pH值及养分(全氮、有效磷、速效钾、有机质)的空间变异分析和合理采样数目研究.结果表明,研究区内全氮、有效磷、速效钾、pH、有机质均呈对数正态分布;全氮、有效磷、速效钾、pH、有机质均为中等变异强度;研究区内最优采样方式为50m* 50m.

  4. 岩石物理参数高分辨率地质统计学反演%High-resolution geostatistical petrophysical-parameter inversion

    Institute of Scientific and Technical Information of China (English)

    姜文龙; 杨锴

    2012-01-01

    Geostatistical inversion can well characterize thin-layers for its high-resolution. We discussed the relationship between geostatistical inversion and high-resolution, as well as the problem of geostatistical inversion in petrophysical parameter inversion. Moreover, the algorithm for reducing the uncertainty of inversion was studied. Research results show that along with the alternation of variogram, the resolution of geostatistical inversion result will change, but the conventional Krigging algorithm destroy the continuity of original geologic formations when improving resolution through reducing variogram. Based on the above results, we introduced some restraints such as geologic interpretation strata and dip into geostatistical inversion. The method was applied on the inversion of carbonate mineral components at ODP1144 station sea area in South Sea.%地质统计学反演由于其高分辨率的特点,可以很好地用来描述薄层等信息.就地质统计学反演与高分辨率的关系和地质统计学反演在岩石物理参数反演中存在的问题进行了讨论,并从算法上研究了减小反演不确定性的方法.研究结果表明,随着变差函数变程的改变,地质统计学模拟结果的分辨率也会发生改变,但常规的克里金算法在通过减小变程来提高分辨率的同时,破坏了原有地质层位的连续性.在此基础上提出加入地质解释层位和地层倾角等约束信息的地质统计学反演方法,将该方法应用于南海ODP1144站位海区矿物组分的反演,很好地揭示了该区碳酸盐矿物的沉积特征.

  5. Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium

    Directory of Open Access Journals (Sweden)

    S. Ly

    2011-07-01

    Full Text Available Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (kriging are widely applied in spatial interpolation from point measurement to continuous surfaces. The first step in kriging computation is the semi-variogram modelling which usually used only one variogram model for all-moment data. The objective of this paper was to develop different algorithms of spatial interpolation for daily rainfall on 1 km2 regular grids in the catchment area and to compare the results of geostatistical and deterministic approaches. This study leaned on 30-yr daily rainfall data of 70 raingages in the hilly landscape of the Ourthe and Ambleve catchments in Belgium (2908 km2. This area lies between 35 and 693 m in elevation and consists of river networks, which are tributaries of the Meuse River. For geostatistical algorithms, seven semi-variogram models (logarithmic, power, exponential, Gaussian, rational quadratic, spherical and penta-spherical were fitted to daily sample semi-variogram on a daily basis. These seven variogram models were also adopted to avoid negative interpolated rainfall. The elevation, extracted from a digital elevation model, was incorporated into multivariate geostatistics. Seven validation raingages and cross validation were used to compare the interpolation performance of these algorithms applied to different densities of raingages. We found that between the seven variogram models used, the Gaussian model was the most frequently best fit. Using seven variogram models can avoid negative daily rainfall in ordinary kriging. The negative estimates of kriging were observed for convective more than stratiform rain. The performance of the different methods varied slightly according to the density of raingages, particularly between 8 and 70 raingages but it was much different for interpolation using 4 raingages. Spatial interpolation with the geostatistical and

  6. Assessing variability of water quality in a groundwater-fed perennial lake of Kashmir Himalayas using linear geostatistics

    Science.gov (United States)

    Sarah, S.; Jeelani, Gh.; Ahmed, Shakeel

    2011-06-01

    This paper presents a study on Manasbal lake, which is one of the high altitude lakes in the Kashmir Valley, India. Eighteen water samples were analysed for major ions and trace elements to assess the variability of water quality of the lake for various purposes. Geostatistics, the theory of regionalized variables, was then used to enhance the dataset and estimate some missing spatial values. Results indicated that the concentration of major ions in the water samples in winter was higher than in summer. The scatter diagrams suggested the dominance of alkaline earths over the alkali elements. Three types of water were identified in the lake that are referred to as Ca-HCO3, Mg-HCO3 and hybrid types. The lake water was found to be controlled by rock-water interaction with carbonate lithology as a dominant source of the solutes. The major (Ca2 + , Mg2 + , Na + , K + , NO3 and {{HCO}}3-, CO3 and Cl) and trace elements of the lake water were within the World Health Organization standards, therefore the lake water was considered chemically safe for drinking purposes. Although NO3 concentration (ranging from 1.72 to 2 mg/L), is within the permissible limit and not very alarming, the gradually increasing trend is not acceptable. It is however, important to guard its spatio-temporal variability as the water is used for domestic as well as agricultural purposes. This study is significant as hydrogeological information on such high altitude lakes in India is scanty.

  7. Geostatistical study of spatial correlations of lead and zinc concentration in urban reservoir. Study case Czerniakowskie Lake, Warsaw, Poland

    Science.gov (United States)

    Fabijańczyk, Piotr; Zawadzki, Jarosław; Wojtkowska, Małgorzata

    2016-07-01

    The article presents detailed geostatistical analysis of spatial distribution of lead and zinc concentration in water, suspension and bottom sediments of large, urban lake exposed to intensive anthropogenic pressure within a large city. Systematic chemical measurements were performed at eleven cross-sections located along Czerniakowskie Lake, the largest lake in Warsaw, the capital of Poland. During the summer, the lake is used as a public bathing area, therefore, to better evaluate human impacts, field measurements were carried out in high-use seasons. It was found that the spatial distributions of aqueous lead and zinc differ during the summer and autumn. In summer several Pb and Zn hot-spots were observed, while during autumn spatial distributions of Pb and Zn were rather homogenous throughout the entire lake. Large seasonal differences in spatial distributions of Pb and Zn were found in bottom sediments. Autumn concentrations of both heavy metals were ten times higher in comparison with summer values. Clear cross-correlations of Pb and Zn concentrations in water, suspension and bottom sediments suggest that both Pb and Zn came to Czerniakowskie Lake from the same source.

  8. Geostatistical assessment of the impact of World War I on the spatial occurrence of soil heavy metals.

    Science.gov (United States)

    Meerschman, Eef; Cockx, Liesbet; Islam, Mohammad Monirul; Meeuws, Fun; Van Meirvenne, Marc

    2011-06-01

    Previous research showed a regional Cu enrichment of 6 mg kg(-1) in the top soil of the Ypres war zone (Belgium), caused by corrosion of WWI shell fragments. Further research was required since in addition to Cu, also As, Pb, and Zn were used during the manufacturing of ammunition. Therefore, an additional data collection was conducted in which the initial Cu data set was tripled to 731 data points and extended to eight heavy metals (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) which permitted (1) to evaluate the environmental impact of the heavy metals at a regional scale and (2) to assess their regional spatial occurrence by performing an optimized geostatistical modeling. The results showed no pollution at a regional scale, but sometimes locally concentrations exceeded the soil sanitation threshold, especially for Cu, Pb, and Zn. The spatial patterns of Ni and Cr were related to variations in soil texture whereas the occurrences of Cu and Pb were clearly linked to WWI activities. This difference in spatial behavior was confirmed by an analysis of coregionalization.

  9. Modelling infiltration and geostatistical analysis of spatial variability of sorptivity and transmissivity in a flood spreading area

    Energy Technology Data Exchange (ETDEWEB)

    Haghighi-Fashi, F.; Sharifi, F.; Kamali, K.

    2014-06-01

    Knowledge of infiltration characteristics is useful in hydrological studies of agricultural soils. Soil hydraulic parameters such as steady infiltration rate, sorptivity, and transmissivity can exhibit appreciable spatial variability. The main objectives of this study were to examine several mathematical models of infiltration and to analyze the spatial variability of observed final infiltration rate, estimated sorptivity and estimated transmissivity in flood spreading and control areas in Ilam province, Iran. The suitability of geostatistics to describe such spatial variability was assessed using data from 30 infiltration measurements sampled along three lines. The Horton model provided the most accurate simulation of infiltration considering all measurements and the Philips two-term model provided less accurate simulation. A comparison of the measured values and the estimated final infiltration rates showed that the Kostiakov- Lewis, Kostiakov, and SCS models could not estimate the final infiltration rate as well as Horton model. Estimated sorptivity and transmissivity parameters of the Philips two-term model and final infiltration rate had spatial structure, and were considered to be structural variables over the transect pattern. The Gaussian model provided the best-fit theoretical variogram for these three parameters. Variogram values ranged from 99 and 88 m for sorptivity and final infiltration rate to 686 (spherical) and 384 m (Gaussian) for transmissivity. Sorptivity, transmissivity and final infiltration attributes showed a high degree of spatial dependence, being 0.99, 0.81 and 1, respectively. Results showed that kriging could be used to predict the studied parameters in the study area. (Author)

  10. Evaluation of spatial variability of soil arsenic adjacent to a disused cattle-dip site, using model-based geostatistics.

    Science.gov (United States)

    Niazi, Nabeel K; Bishop, Thomas F A; Singh, Balwant

    2011-12-15

    This study investigated the spatial variability of total and phosphate-extractable arsenic (As) concentrations in soil adjacent to a cattle-dip site, employing a linear mixed model-based geostatistical approach. The soil samples in the study area (n = 102 in 8.1 m(2)) were taken at the nodes of a 0.30 × 0.35 m grid. The results showed that total As concentration (0-0.2 m depth) and phosphate-extractable As concentration (at depths of 0-0.2, 0.2-0.4, and 0.4-0.6 m) in soil adjacent to the dip varied greatly. Both total and phosphate-extractable soil As concentrations significantly (p = 0.004-0.048) increased toward the cattle-dip. Using the linear mixed model, we suggest that 5 samples are sufficient to assess a dip site for soil (As) contamination (95% confidence interval of ±475.9 mg kg(-1)), but 15 samples (95% confidence interval of ±212.3 mg kg(-1)) is desirable baseline when the ultimate goal is to evaluate the effects of phytoremediation. Such guidelines on sampling requirements are crucial for the assessment of As contamination levels at other cattle-dip sites, and to determine the effect of phytoremediation on soil As.

  11. Assessment of nitrate pollution in the Grand Morin aquifers (France): combined use of geostatistics and physically based modeling.

    Science.gov (United States)

    Flipo, Nicolas; Jeannée, Nicolas; Poulin, Michel; Even, Stéphanie; Ledoux, Emmanuel

    2007-03-01

    The objective of this work is to combine several approaches to better understand nitrate fate in the Grand Morin aquifers (2700 km(2)), part of the Seine basin. cawaqs results from the coupling of the hydrogeological model newsam with the hydrodynamic and biogeochemical model of river ProSe. cawaqs is coupled with the agronomic model Stics in order to simulate nitrate migration in basins. First, kriging provides a satisfactory representation of aquifer nitrate contamination from local observations, to set initial conditions for the physically based model. Then associated confidence intervals, derived from data using geostatistics, are used to validate cawaqs results. Results and evaluation obtained from the combination of these approaches are given (period 1977-1988). Then cawaqs is used to simulate nitrate fate for a 20-year period (1977-1996). The mean nitrate concentrations increase in aquifers is 0.09 mgN L(-1)yr(-1), resulting from an average infiltration flux of 3500 kgN.km(-2)yr(-1).

  12. Sediment distribution pattern mapped from the combination of objective analysis and geostatistics in the large shallow Taihu Lake, China.

    Science.gov (United States)

    Luo, Lian-Cong; Qin, Bo-Qiang; Zhu, Guang-Wei

    2004-01-01

    Investigation was made into sediment depth at 723 irregularly scattered measurement points which cover all the regions in Taihu Lake, China. The combination of successive correction scheme and geostatistical method was used to get all the values of recent sediment thickness at the 69 x 69 grids in the whole lake. The results showed that there is the significant difference in sediment depth between the eastern area and the western region, and most of the sediments are located in the western shore-line and northern regimes but just a little in the center and eastern parts. The notable exception is the patch between the center and Xishan Island where the maximum sediment depth is more than 4.0 m. This sediment distribution pattern is more than likely related to the current circulation pattern induced by the prevailing wind-forcing in Taihu Lake. The numerical simulation of hydrodynamics can strong support the conclusion. Sediment effects on water quality was also studied and the results showed that the concentrations of TP, TN and SS in the western part are obviously larger than those in the eastern regime, which suggested that more nutrients can be released from thicker sediment areas.

  13. Sediment distribution pattern mapped from the combination of objective analysis and geostatistics in the large shallow Taihu Lake, China

    Institute of Scientific and Technical Information of China (English)

    LUO Lian-cong; QIN Bo-qiang; ZHU Guang-wei

    2004-01-01

    Investigation was made into sediment depth at 723 irregularly scattered measurement points which cover all the regions in Taihu Lake, China. The combination of successive correction scheme and geostatistical method was used to get all the values of recent sediment thickness at the 69×69 grids in the whole lake. The results showed that there is the significant difference in sediment depth between the eastern area and the western region, and most of the sediments are located in the western shore-line and northern regimes but just a little in the center and eastern parts. The notable exception is the patch between the center and Xishan Island where the maximum sediment depth is more than 4.0 m. This sediment distribution pattern is more than likely related to the current circulation pattern induced by the prevailing wind-forcing in Taihu Lake. The numerical simulation of hydrodynamics can strong support the conclusion. Sediment effects on water quality was also studied and the results showed that the concentrations of TP, TN and SS in the western part are obviously larger than those in the eastern regime, which suggested that more nutrients can be released from thicker sediment areas.

  14. Geostatistics and Geographic Information Systems to Study the Spatial Distribution of Grapholita molesta (Busck) (Lepidoptera: Tortricidae) in Peach Fields.

    Science.gov (United States)

    Duarte, F; Calvo, M V; Borges, A; Scatoni, I B

    2015-08-01

    The oriental fruit moth, Grapholita molesta (Busck), is the most serious pest in peach, and several insecticide applications are required to reduce crop damage to acceptable levels. Geostatistics and Geographic Information Systems (GIS) are employed to measure the range of spatial correlation of G. molesta in order to define the optimum sampling distance for performing spatial analysis and to determine the current distribution of the pest in peach orchards of southern Uruguay. From 2007 to 2010, 135 pheromone traps per season were installed and georeferenced in peach orchards distributed over 50,000 ha. Male adult captures were recorded weekly from September to April. Structural analysis of the captures was performed, yielding 14 semivariograms for the accumulated captures analyzed by generation and growing season. Two sets of maps were constructed to describe the pest distribution. Nine significant models were obtained in the 14 evaluated periods. The range estimated for the correlation was from 908 to 6884 m. Three hot spots of high population level and some areas with comparatively low populations were constant over the 3-year period, while there is a greater variation in the size of the population in different generations and years in other areas.

  15. Estimating the volume and age of water stored in global lakes using a geo-statistical approach

    Science.gov (United States)

    Messager, Mathis Loïc; Lehner, Bernhard; Grill, Günther; Nedeva, Irena; Schmitt, Oliver

    2016-12-01

    Lakes are key components of biogeochemical and ecological processes, thus knowledge about their distribution, volume and residence time is crucial in understanding their properties and interactions within the Earth system. However, global information is scarce and inconsistent across spatial scales and regions. Here we develop a geo-statistical model to estimate the volume of global lakes with a surface area of at least 10 ha based on the surrounding terrain information. Our spatially resolved database shows 1.42 million individual polygons of natural lakes with a total surface area of 2.67 × 106 km2 (1.8% of global land area), a total shoreline length of 7.2 × 106 km (about four times longer than the world's ocean coastline) and a total volume of 181.9 × 103 km3 (0.8% of total global non-frozen terrestrial water stocks). We also compute mean and median hydraulic residence times for all lakes to be 1,834 days and 456 days, respectively.

  16. APPLICATION OF BAYESIAN AND GEOSTATISTICAL MODELING TO THE ENVIRONMENTAL MONITORING OF CS-137 AT THE IDAHO NATIONAL LABORATORY

    Energy Technology Data Exchange (ETDEWEB)

    Kara G. Eby

    2010-08-01

    At the Idaho National Laboratory (INL) Cs-137 concentrations above the U.S. Environmental Protection Agency risk-based threshold of 0.23 pCi/g may increase the risk of human mortality due to cancer. As a leader in nuclear research, the INL has been conducting nuclear activities for decades. Elevated anthropogenic radionuclide levels including Cs-137 are a result of atmospheric weapons testing, the Chernobyl accident, and nuclear activities occurring at the INL site. Therefore environmental monitoring and long-term surveillance of Cs-137 is required to evaluate risk. However, due to the large land area involved, frequent and comprehensive monitoring is limited. Developing a spatial model that predicts Cs-137 concentrations at unsampled locations will enhance the spatial characterization of Cs-137 in surface soils, provide guidance for an efficient monitoring program, and pinpoint areas requiring mitigation strategies. The predictive model presented herein is based on applied geostatistics using a Bayesian analysis of environmental characteristics across the INL site, which provides kriging spatial maps of both Cs-137 estimates and prediction errors. Comparisons are presented of two different kriging methods, showing that the use of secondary information (i.e., environmental characteristics) can provide improved prediction performance in some areas of the INL site.

  17. The effect of training image and secondary data integration with multiple-point geostatistics in groundwater modeling

    Directory of Open Access Journals (Sweden)

    X. He

    2013-09-01

    Full Text Available Multiple-point geostatistic simulation (MPS has recently become popular in stochastic hydrogeology, primarily because of its capability to derive multivariate distributions from the training image (TI. However, its application in three dimensional simulations has been constrained by the difficulty of constructing 3-D TI. The object-based TiGenerator may be a useful tool in this regard; yet the sensitivity of model predictions to the training image has not been documented. Another issue in MPS is the integration of multiple geophysical data. The best way to retrieve and incorporate information from high resolution geophysical data is still under discussion. This work shows that TI from TiGenerator delivers acceptable results when used for groundwater modeling, although the TI directly converted from high resolution geophysical data leads to better simulation. The model results also indicate that soft conditioning in MPS is a convenient and efficient way of integrating secondary data such as 3-D airborne electromagnetic data, but over conditioning has to be avoided.

  18. Detection of terrain indices related to soil salinity and mapping salt-affected soils using remote sensing and geostatistical techniques.

    Science.gov (United States)

    Triki Fourati, Hela; Bouaziz, Moncef; Benzina, Mourad; Bouaziz, Samir

    2017-04-01

    Traditional surveying methods of soil properties over landscapes are dramatically cost and time-consuming. Thus, remote sensing is a proper choice for monitoring environmental problem. This research aims to study the effect of environmental factors on soil salinity and to map the spatial distribution of this salinity over the southern east part of Tunisia by means of remote sensing and geostatistical techniques. For this purpose, we used Advanced Spaceborne Thermal Emission and Reflection Radiometer data to depict geomorphological parameters: elevation, slope, plan curvature (PLC), profile curvature (PRC), and aspect. Pearson correlation between these parameters and soil electrical conductivity (ECsoil) showed that mainly slope and elevation affect the concentration of salt in soil. Moreover, spectral analysis illustrated the high potential of short-wave infrared (SWIR) bands to identify saline soils. To map soil salinity in southern Tunisia, ordinary kriging (OK), minimum distance (MD) classification, and simple regression (SR) were used. The findings showed that ordinary kriging technique provides the most reliable performances to identify and classify saline soils over the study area with a root mean square error of 1.83 and mean error of 0.018.

  19. A novel geotechnical/geostatistical approach for exploration and production of natural gas from multiple geologic strata, Phase 1

    Energy Technology Data Exchange (ETDEWEB)

    Overbey, W.K. Jr.; Reeves, T.K.; Salamy, S.P.; Locke, C.D.; Johnson, H.R.; Brunk, R.; Hawkins, L. (BDM Engineering Services Co., Morgantown, WV (United States))

    1991-05-01

    This research program has been designed to develop and verify a unique geostatistical approach for finding natural gas resources. The research has been conducted by Beckley College, Inc. (Beckley) and BDM Engineering Services Company (BDMESC) under contract to the US Department of Energy (DOE), Morgantown Energy Technology Center. Phase 1 of the project consisted of compiling and analyzing relevant geological and gas production information in selected areas of Raleigh County, West Virginia, ultimately narrowed to the Eccles, West Virginia, 7 {1/2} minute Quadrangle. The Phase 1 analysis identified key parameters contributing to the accumulation and production of natural gas in Raleigh County, developed analog models relating geological factors to gas production, and identified specific sites to test and verify the analysis methodologies by drilling. Based on the Phase 1 analysis, five sites have been identified with high potential for economic gas production. Phase 2 will consist of drilling, completing, and producing one or more wells at the sites identified in the Phase 1 analyses. The initial well is schedules to the drilled in April 1991. This report summarizes the results of the Phase 1 investigations. For clarity, the report has been prepared in two volumes. Volume 1 presents the Phase 1 overview; Volume 2 contains the detailed geological and production information collected and analyzed for this study.

  20. GIS and RS integration: application of geostatistical techniques and environmental changes in the coastal zone in Kenya

    Science.gov (United States)

    Jahjah, Munzer; Ulivieri, Carlo

    2004-02-01

    Understanding the dynamics of land cover change has increasingly been recognized as one of the key research imperatives in global environmental change research. Scientists have developed and applied various methods in order to find and propose solutions for many environmental world problems. From 1986-1995 changes in Kenya coastal zone landcover, derived from the post-classification TM images, were significant with arid areas growing from 3% to 10%, woody areas decreased from 4% to 2%, herbaceous areas decreased from 25% to 20%, developed land increased from 2% to 3%. In order to generate the change probability map as a continuous surface using geostatistical method-ArcGIS, we used as an input the Generalized Linear Model (GLM) probability result. The results reveal the efficiency of the Probability-of-Change map (POC), especially if reference data are lacking, in indicating the possibility of having a change and its type in a determined area, taking advantage of the layer transparency of the GIS systems. Thus, the derived information supplies a good tool for the interpretation of the magnitude of the land cover changes and guides the final user directly to the areas of changes to understand and derive the possible interactions of human or natural processes.

  1. Geographical distribution of the annual mean radon concentrations in primary schools of Southern Serbia - application of geostatistical methods.

    Science.gov (United States)

    Bossew, P; Žunić, Z S; Stojanovska, Z; Tollefsen, T; Carpentieri, C; Veselinović, N; Komatina, S; Vaupotič, J; Simović, R D; Antignani, S; Bochicchio, F

    2014-01-01

    Between 2008 and 2011 a survey of radon ((222)Rn) was performed in schools of several districts of Southern Serbia. Some results have been published previously (Žunić et al., 2010; Carpentieri et al., 2011; Žunić et al., 2013). This article concentrates on the geographical distribution of the measured Rn concentrations. Applying geostatistical methods we generate "school radon maps" of expected concentrations and of estimated probabilities that a concentration threshold is exceeded. The resulting maps show a clearly structured spatial pattern which appears related to the geological background. In particular in areas with vulcanite and granitoid rocks, elevated radon (Rn) concentrations can be expected. The "school radon map" can therefore be considered as proxy to a map of the geogenic radon potential, and allows identification of radon-prone areas, i.e. areas in which higher Rn radon concentrations can be expected for natural reasons. It must be stressed that the "radon hazard", or potential risk, estimated this way, has to be distinguished from the actual radon risk, which is a function of exposure. This in turn may require (depending on the target variable which is supposed to measure risk) considering demographic and sociological reality, i.e. population density, distribution of building styles and living habits.

  2. Geostatistical analysis and isoscape of ice core derived water stable isotope records in an Antarctic macro region

    Science.gov (United States)

    Hatvani, István Gábor; Leuenberger, Markus; Kohán, Balázs; Kern, Zoltán

    2017-09-01

    Water stable isotopes preserved in ice cores provide essential information about polar precipitation. In the present study, multivariate regression and variogram analyses were conducted on 22 δ2H and 53 δ18O records from 60 ice cores covering the second half of the 20th century. Taking the multicollinearity of the explanatory variables into account, as also the model's adjusted R2 and its mean absolute error, longitude, elevation and distance from the coast were found to be the main independent geographical driving factors governing the spatial δ18O variability of firn/ice in the chosen Antarctic macro region. After diminishing the effects of these factors, using variography, the weights for interpolation with kriging were obtained and the spatial autocorrelation structure of the dataset was revealed. This indicates an average area of influence with a radius of 350 km. This allows the determination of the areas which are as yet not covered by the spatial variability of the existing network of ice cores. Finally, the regional isoscape was obtained for the study area, and this may be considered the first step towards a geostatistically improved isoscape for Antarctica.

  3. Status and Role of Geostatistics in the Education of Geographic Information System%地统计学在地理信息系统教学中的地位与作用

    Institute of Scientific and Technical Information of China (English)

    魏义长; 赵东保; 李小根; 张富; 杨成杰; 姚志宏

    2011-01-01

    地统计分析是空间分析的重要技术手段之一,当前不少地理信息系统(GIS)专业的学生对地统计分析课程不够了解和重视.为促进GIS专业学生对地统计分析课程的认识,推动地统计学的发展,作者根据多年从事地统计分析教学与科研的经验,并通过查阅大量国内外文献,从地统计分析与传统统计学,地理信息系统的区别与联系出发,详细地分析了地统计分析课程在地理信息系统教学中的地位和作用,深入探讨了学习地统计分析课程的方法,最后对地统计分析课程的发展进行了展望.%Ceostatistical analysis is one of very important technical methods in spatial analysis, but many students majoring in geographic information system (CIS) did not attach importance to learning the theory and technique of geostatistics,even more a few students know nothing of it. Therefore,according to the experience of the authors in teaching and researching by using geostatistics,and by consulting all literatures on geostatistics, the paper first discussed the difference and relationship of geostatistics with classical statistics and GIS,and analyzed the status and role of geostatistics in the education of geographic information system,and then investigated the methods of learning geostatistics, finally, took a glance into the future of geostatistics. So that, the authors expect the paper should increase the recognizing to geostatistics in students majoring in GIS, and advance the developing of geostatistics.

  4. Geostatistical modeling of uncertainty of the spatial distribution of available phosphorus in soil in a sugarcane field

    Science.gov (United States)

    Tadeu Pereira, Gener; Ribeiro de Oliveira, Ismênia; De Bortoli Teixeira, Daniel; Arantes Camargo, Livia; Rodrigo Panosso, Alan; Marques, José, Jr.

    2015-04-01

    Phosphorus is one of the limiting nutrients for sugarcane development in Brazilian soils. The spatial variability of this nutrient is great, defined by the properties that control its adsorption and desorption reactions. Spatial estimates to characterize this variability are based on geostatistical interpolation. Thus, the assessment of the uncertainty of estimates associated with the spatial distribution of available P (Plabile) is decisive to optimize the use of phosphate fertilizers. The purpose of this study was to evaluate the performance of sequential Gaussian simulation (sGs) and ordinary kriging (OK) in the modeling of uncertainty in available P estimates. A sampling grid with 626 points was established in a 200-ha experimental sugarcane field in Tabapuã, São Paulo State, Brazil. The soil was sampled in the crossover points of a regular grid with intervals of 50 m. From the observations, 63 points, approximately 10% of sampled points were randomly selected before the geostatistical modeling of the composition of a data set used in the validation process modeling, while the remaining 563 points were used for the predictions variable in a place not sampled. The sGs generated 200 realizations. From the realizations generated, different measures of estimation and uncertainty were obtained. The standard deviation, calculated point to point, all simulated maps provided the map of deviation, used to assess local uncertainty. The visual analysis of maps of the E-type and KO showed that the spatial patterns produced by both methods were similar, however, it was possible to observe the characteristic smoothing effect of the KO especially in regions with extreme values. The Standardized variograms of selected realizations sGs showed both range and model similar to the variogram of the Observed date of Plabile. The variogram KO showed a distinct structure of the observed data, underestimating the variability over short distances, presenting parabolic behavior near

  5. Chapter J: Issues and challenges in the application of geostatistics and spatial-data analysis to the characterization of sand-and-gravel resources

    Science.gov (United States)

    Hack, Daniel R.

    2005-01-01

    Sand-and-gravel (aggregate) resources are a critical component of the Nation's infrastructure, yet aggregate-mining technologies lag far behind those of metalliferous mining and other sectors. Deposit-evaluation and site-characterization methodologies are antiquated, and few serious studies of the potential applications of spatial-data analysis and geostatistics have been published. However, because of commodity usage and the necessary proximity of a mine to end use, aggregate-resource exploration and evaluation differ fundamentally from comparable activities for metalliferous ores. Acceptable practices, therefore, can reflect this cruder scale. The increasing use of computer technologies is colliding with the need for sand-and-gravel mines to modernize and improve their overall efficiency of exploration, mine planning, scheduling, automation, and other operations. The emergence of megaquarries in the 21st century will also be a contributing factor. Preliminary research into the practical applications of exploratory-data analysis (EDA) have been promising. For example, EDA was used to develop a linear-regression equation to forecast freeze-thaw durability from absorption values for Lower Paleozoic carbonate rocks mined for crushed aggregate from quarries in Oklahoma. Applications of EDA within a spatial context, a method of spatial-data analysis, have also been promising, as with the investigation of undeveloped sand-and-gravel resources in the sedimentary deposits of Pleistocene Lake Bonneville, Utah. Formal geostatistical investigations of sand-and-gravel deposits are quite rare, and the primary focus of those studies that have been completed is on the spatial characterization of deposit thickness and its subsequent effect on ore reserves. A thorough investigation of a gravel deposit in an active aggregate-mining area in central Essex, U.K., emphasized the problems inherent in the geostatistical characterization of particle-size-analysis data. Beyond such factors

  6. Geostatistical Analysis of Tritium, 3H/3He Age and Noble Gas Derived Parameters in California Groundwater

    Science.gov (United States)

    Visser, A.; Singleton, M. J.; Moran, J. E.; Fram, M. S.; Kulongoski, J. T.; Esser, B. K.

    2014-12-01

    Key characteristics of California groundwater systems related to aquifer vulnerability, sustainability, recharge locations and mechanisms, and anthropogenic impact on recharge, are revealed in a spatial geostatistical analysis of the data set of tritium, dissolved noble gas and helium isotope analyses collected for the California State Water Resources Control Board's Groundwater Ambient Monitoring and Assessment (GAMA) and California Aquifer Susceptibility (CAS) programs. Over 4,000 tritium and noble gas analyses are available from wells across California. 25% of the analyzed samples contained less than 1 pCi/L indicating recharge occurred before 1950. The correlation length of tritium concentration is 120 km. Nearly 50% of the wells show a significant component of terrigenic helium. Over 50% of these samples show a terrigenic helium isotope ratio (Rter) that is significantly higher than the radiogenic helium isotope ratio (Rrad = 2×10-8). Rter values of more than three times the atmospheric isotope ratio (Ra = 1.384×10-6) are associated with known faults and volcanic provinces in Northern California. In the Central Valley, Rter varies from radiogenic to 2.25 Ra, complicating 3H/3He dating. The Rter was mapped by kriging, showing a correlation length of less than 50 km. The local predicted Rter was used to separate tritiogenic from atmospheric and terrigenic 3He. Regional groundwater recharge areas, indicated by young groundwater ages, are located in the southern Santa Clara Basin and in the upper LA basin and in the eastern San Joaquin Valley and along unlined canals carrying Colorado River water. Recharge in California is dominated by agricultural return flows, river recharge and managed aquifer recharge rather than precipitation excess. Combined application of noble gases and other groundwater tracers reveal the impact of engineered groundwater recharge and prove invaluable for the study of complex groundwater systems. This work was performed under the

  7. Using the Direct Sampling Multiple-Point Geostatistical Method for Filling Gaps in Landsat 7 ETM+ SLC-off Imagery

    KAUST Repository

    Yin, Gaohong

    2016-05-01

    Since the failure of the Scan Line Corrector (SLC) instrument on Landsat 7, observable gaps occur in the acquired Landsat 7 imagery, impacting the spatial continuity of observed imagery. Due to the highly geometric and radiometric accuracy provided by Landsat 7, a number of approaches have been proposed to fill the gaps. However, all proposed approaches have evident constraints for universal application. The main issues in gap-filling are an inability to describe the continuity features such as meandering streams or roads, or maintaining the shape of small objects when filling gaps in heterogeneous areas. The aim of the study is to validate the feasibility of using the Direct Sampling multiple-point geostatistical method, which has been shown to reconstruct complicated geological structures satisfactorily, to fill Landsat 7 gaps. The Direct Sampling method uses a conditional stochastic resampling of known locations within a target image to fill gaps and can generate multiple reconstructions for one simulation case. The Direct Sampling method was examined across a range of land cover types including deserts, sparse rural areas, dense farmlands, urban areas, braided rivers and coastal areas to demonstrate its capacity to recover gaps accurately for various land cover types. The prediction accuracy of the Direct Sampling method was also compared with other gap-filling approaches, which have been previously demonstrated to offer satisfactory results, under both homogeneous area and heterogeneous area situations. Studies have shown that the Direct Sampling method provides sufficiently accurate prediction results for a variety of land cover types from homogeneous areas to heterogeneous land cover types. Likewise, it exhibits superior performances when used to fill gaps in heterogeneous land cover types without input image or with an input image that is temporally far from the target image in comparison with other gap-filling approaches.

  8. A training image evaluation and selection method based on minimum data event distance for multiple-point geostatistics

    Science.gov (United States)

    Feng, Wenjie; Wu, Shenghe; Yin, Yanshu; Zhang, Jiajia; Zhang, Ke

    2017-07-01

    A training image (TI) can be regarded as a database of spatial structures and their low to higher order statistics used in multiple-point geostatistics (MPS) simulation. Presently, there are a number of methods to construct a series of candidate TIs (CTIs) for MPS simulation based on a modeler's subjective criteria. The spatial structures of TIs are often various, meaning that the compatibilities of different CTIs with the conditioning data are different. Therefore, evaluation and optimal selection of CTIs before MPS simulation is essential. This paper proposes a CTI evaluation and optimal selection method based on minimum data event distance (MDevD). In the proposed method, a set of MDevD properties are established through calculation of the MDevD of conditioning data events in each CTI. Then, CTIs are evaluated and ranked according to the mean value and variance of the MDevD properties. The smaller the mean value and variance of an MDevD property are, the more compatible the corresponding CTI is with the conditioning data. In addition, data events with low compatibility in the conditioning data grid can be located to help modelers select a set of complementary CTIs for MPS simulation. The MDevD property can also help to narrow the range of the distance threshold for MPS simulation. The proposed method was evaluated using three examples: a 2D categorical example, a 2D continuous example, and an actual 3D oil reservoir case study. To illustrate the method, a C++ implementation of the method is attached to the paper.

  9. Integrating indicator-based geostatistical estimation and aquifer vulnerability of nitrate-N for establishing groundwater protection zones

    Science.gov (United States)

    Jang, Cheng-Shin; Chen, Shih-Kai

    2015-04-01

    Groundwater nitrate-N contamination occurs frequently in agricultural regions, primarily resulting from surface agricultural activities. The focus of this study is to establish groundwater protection zones based on indicator-based geostatistical estimation and aquifer vulnerability of nitrate-N in the Choushui River alluvial fan in Taiwan. The groundwater protection zones are determined by univariate indicator kriging (IK) estimation, aquifer vulnerability assessment using logistic regression (LR), and integration of the IK estimation and aquifer vulnerability using simple IK with local prior means (sIKlpm). First, according to the statistical significance of source, transport, and attenuation factors dominating the occurrence of nitrate-N pollution, a LR model was adopted to evaluate aquifer vulnerability and to characterize occurrence probability of nitrate-N exceeding 0.5 mg/L. Moreover, the probabilities estimated using LR were regarded as local prior means. IK was then used to estimate the actual extent of nitrate-N pollution. The integration of the IK estimation and aquifer vulnerability was obtained using sIKlpm. Finally, groundwater protection zones were probabilistically determined using the three aforementioned methods, and the estimated accuracy of the delineated groundwater protection zones was gauged using a cross-validation procedure based on observed nitrate-N data. The results reveal that the integration of the IK estimation and aquifer vulnerability using sIKlpm is more robust than univariate IK estimation and aquifer vulnerability assessment using LR for establishing groundwater protection zones. Rigorous management practices for fertilizer use should be implemented in orchards situated in the determined groundwater protection zones.

  10. Improved hydrological model parametrization for climate change impact assessment under data scarcity - The potential of field monitoring techniques and geostatistics.

    Science.gov (United States)

    Meyer, Swen; Blaschek, Michael; Duttmann, Rainer; Ludwig, Ralf

    2016-02-01

    According to current climate projections, Mediterranean countries are at high risk for an even pronounced susceptibility to changes in the hydrological budget and extremes. These changes are expected to have severe direct impacts on the management of water resources, agricultural productivity and drinking water supply. Current projections of future hydrological change, based on regional climate model results and subsequent hydrological modeling schemes, are very uncertain and poorly validated. The Rio Mannu di San Sperate Basin, located in Sardinia, Italy, is one test site of the CLIMB project. The Water Simulation Model (WaSiM) was set up to model current and future hydrological conditions. The availability of measured meteorological and hydrological data is poor as it is common for many Mediterranean catchments. In this study we conducted a soil sampling campaign in the Rio Mannu catchment. We tested different deterministic and hybrid geostatistical interpolation methods on soil textures and tested the performance of the applied models. We calculated a new soil texture map based on the best prediction method. The soil model in WaSiM was set up with the improved new soil information. The simulation results were compared to standard soil parametrization. WaSiMs was validated with spatial evapotranspiration rates using the triangle method (Jiang and Islam, 1999). WaSiM was driven with the meteorological forcing taken from 4 different ENSEMBLES climate projections for a reference (1971-2000) and a future (2041-2070) times series. The climate change impact was assessed based on differences between reference and future time series. The simulated results show a reduction of all hydrological quantities in the future in the spring season. Furthermore simulation results reveal an earlier onset of dry conditions in the catchment. We show that a solid soil model setup based on short-term field measurements can improve long-term modeling results, which is especially important

  11. Geostatistics-based groundwater-level monitoring network design and its application to the Upper Floridan aquifer, USA.

    Science.gov (United States)

    Bhat, Shirish; Motz, Louis H; Pathak, Chandra; Kuebler, Laura

    2015-01-01

    A geostatistical method was applied to optimize an existing groundwater-level monitoring network in the Upper Floridan aquifer for the South Florida Water Management District in the southeastern United States. Analyses were performed to determine suitable numbers and locations of monitoring wells that will provide equivalent or better quality groundwater-level data compared to an existing monitoring network. Ambient, unadjusted groundwater heads were expressed as salinity-adjusted heads based on the density of freshwater, well screen elevations, and temperature-dependent saline groundwater density. The optimization of the numbers and locations of monitoring wells is based on a pre-defined groundwater-level prediction error. The newly developed network combines an existing network with the addition of new wells that will result in a spatial distribution of groundwater monitoring wells that better defines the regional potentiometric surface of the Upper Floridan aquifer in the study area. The network yields groundwater-level predictions that differ significantly from those produced using the existing network. The newly designed network will reduce the mean prediction standard error by 43% compared to the existing network. The adoption of a hexagonal grid network for the South Florida Water Management District is recommended to achieve both a uniform level of information about groundwater levels and the minimum required accuracy. It is customary to install more monitoring wells for observing groundwater levels and groundwater quality as groundwater development progresses. However, budget constraints often force water managers to implement cost-effective monitoring networks. In this regard, this study provides guidelines to water managers concerned with groundwater planning and monitoring.

  12. An approach for land suitability evaluation using geostatistics, remote sensing, and geographic information system in arid and semiarid ecosystems.

    Science.gov (United States)

    Emadi, Mostafa; Baghernejad, Majid; Pakparvar, Mojtaba; Kowsar, Sayyed Ahang

    2010-05-01

    This study was undertaken to incorporate geostatistics, remote sensing, and geographic information system (GIS) technologies to improve the qualitative land suitability assessment in arid and semiarid ecosystems of Arsanjan plain, southern Iran. The primary data were obtained from 85 soil samples collected from tree depths (0-30, 30-60, and 60-90 cm); the secondary information was acquired from the remotely sensed data from the linear imaging self-scanner (LISS-III) receiver of the IRS-P6 satellite. Ordinary kriging and simple kriging with varying local means (SKVLM) methods were used to identify the spatial dependency of soil important parameters. It was observed that using the data collected from the spectral values of band 1 of the LISS-III receiver as the secondary variable applying the SKVLM method resulted in the lowest mean square error for mapping the pH and electrical conductivity (ECe) in the 0-30-cm depth. On the other hand, the ordinary kriging method resulted in a reliable accuracy for the other soil properties with moderate to strong spatial dependency in the study area for interpolation in the unstamped points. The parametric land suitability evaluation method was applied on the density points (150 x 150 m(2)) instead of applying on the limited representative profiles conventionally, which were obtained by the kriging or SKVLM methods. Overlaying the information layers of the data was used with the GIS for preparing the final land suitability evaluation. Therefore, changes in land characteristics could be identified in the same soil uniform mapping units over a very short distance. In general, this new method can easily present the squares and limitation factors of the different land suitability classes with considerable accuracy in arbitrary land indices.

  13. Application of geostatistics with Indicator Kriging for analyzing spatial variability of groundwater arsenic concentrations in Southwest Bangladesh.

    Science.gov (United States)

    Hassan, M Manzurul; Atkins, Peter J

    2011-01-01

    This article seeks to explore the spatial variability of groundwater arsenic (As) concentrations in Southwestern Bangladesh. Facts about spatial pattern of As are important to understand the complex processes of As concentrations and its spatial predictions in the unsampled areas of the study site. The relevant As data for this study were collected from Southwest Bangladesh and were analyzed with Flow Injection Hydride Generation Atomic Absorption Spectrometry (FI-HG-AAS). A geostatistical analysis with Indicator Kriging (IK) was employed to investigate the regionalized variation of As concentration. The IK prediction map shows a highly uneven spatial pattern of arsenic concentrations. The safe zones are mainly concentrated in the north, central and south part of the study area in a scattered manner, while the contamination zones are found to be concentrated in the west and northeast parts of the study area. The southwest part of the study area is contaminated with a highly irregular pattern. A Generalized Linear Model (GLM) was also used to investigate the relationship between As concentrations and aquifer depths. A negligible negative correlation between aquifer depth and arsenic concentrations was found in the study area. The fitted value with 95 % confidence interval shows a decreasing tendency of arsenic concentrations with the increase of aquifer depth. The adjusted mean smoothed lowess curve with a bandwidth of 0.8 shows an increasing trend of arsenic concentration up to a depth of 75 m, with some erratic fluctuations and regional variations at the depth between 30 m and 60 m. The borehole lithology was considered to analyze and map the pattern of As variability with aquifer depths. The study has performed an investigation of spatial pattern and variation of As concentrations.

  14. Geostatistical modelling of arsenic in drinking water wells and related toenail arsenic concentrations across Nova Scotia, Canada.

    Science.gov (United States)

    Dummer, T J B; Yu, Z M; Nauta, L; Murimboh, J D; Parker, L

    2015-02-01

    Arsenic is a naturally occurring class 1 human carcinogen that is widespread in private drinking water wells throughout the province of Nova Scotia in Canada. In this paper we explore the spatial variation in toenail arsenic concentrations (arsenic body burden) in Nova Scotia. We describe the regional distribution of arsenic concentrations in private well water supplies in the province, and evaluate the geological and environmental features associated with higher levels of arsenic in well water. We develop geostatistical process models to predict high toenail arsenic concentrations and high well water arsenic concentrations, which have utility for studies where no direct measurements of arsenic body burden or arsenic exposure are available. 892 men and women who participated in the Atlantic Partnership for Tomorrow's Health Project provided both drinking water and toenail clipping samples. Information on socio-demographic, lifestyle and health factors was obtained with a set of standardized questionnaires. Anthropometric indices and arsenic concentrations in drinking water and toenails were measured. In addition, data on arsenic concentrations in 10,498 private wells were provided by the Nova Scotia Department of Environment. We utilised stepwise multivariable logistic regression modelling to develop separate statistical models to: a) predict high toenail arsenic concentrations (defined as toenail arsenic levels ≥0.12 μg g(-1)) and b) predict high well water arsenic concentrations (defined as well water arsenic levels ≥5.0 μg L(-1)). We found that the geological and environmental information that predicted well water arsenic concentrations can also be used to accurately predict toenail arsenic concentrations. We conclude that geological and environmental factors contributing to arsenic contamination in well water are the major contributing influences on arsenic body burden among Nova Scotia residents. Further studies are warranted to assess appropriate

  15. Evaluation of factors controlling the distribution of organic matter and phosphorus in the Eastern Arabian Shelf: A geostatistical reappraisal

    Science.gov (United States)

    Acharya, Shiba Shankar; Panigrahi, Mruganka K.

    2016-09-01

    The Eastern Arabian Shelf (EAS) is a region of high primary production and a part of an intense oxygen minimum zone as well. The EAS is a zone of significant accumulation of organic matter that is ascribable to either the prevalent anoxic condition or high primary productivity, There has been a considerable amount of debate on the dominant factor responsible for the enrichment of organic matter in the sediments in EAS. The present study is an attempt to resolve the issue through robust geostatistical analysis of published and unpublished data. Results of Empirical Bayesian kriging (EBK) and geographically weighted regression (GWR) of available data help to get a refined distribution of organic carbon and phosphorus in the Eastern Arabian Shelf as compared to the earlier known distribution patterns. The primary productivity, evaluated through the latest satellite dataset using Vertically Generalized Production Model, does not show any similarity with the distribution pattern of either organic carbon (Corg) or phosphorus, that was determined based on the in situ data. The negative correlations of primary production with Corg (r=-0.14) and P (r=-0.4) indicate that primary productivity is the most unlikely modulator of organic matter accumulation in the EAS. The negative correlation of bottom water oxygen concentration with Corg (r=-0.39) and Ti-normalized fraction of organic carbon (r=-0.56) indicates that anoxia plays a major role in the preservation of organic matter in the EAS. The mass accumulation rates of Corg and phosphorus show a strong dependency on sedimentation rate (r>0.88), which indicates that the accumulation rate of sediments outweighs the other depositional parameters in controlling the accumulation of organic matter in the EAS.

  16. Representation of animal distributions in space: how geostatistical estimates impact simulation modeling of foot-and-mouth disease spread.

    Science.gov (United States)

    Highfield, Linda; Ward, Michael P; Laffan, Shawn W

    2008-01-01

    Modeling potential disease spread in wildlife populations is important for predicting, responding to and recovering from a foreign animal disease incursion. To make spatial epidemic predictions, the target animal species of interest must first be represented in space. We conducted a series of simulation experiments to determine how estimates of the spatial distribution of white-tailed deer impact the predicted magnitude and distribution of foot-and-mouth disease (FMD) outbreaks. Outbreaks were simulated using a susceptible-infected-recovered geographic automata model. The study region was a 9-county area (24 000 km(2)) of southern Texas. Methods used for creating deer distributions included dasymetric mapping, kriging and remotely sensed image analysis. The magnitudes and distributions of the predicted outbreaks were evaluated by comparing the median number of deer infected and median area affected (km(2)), respectively. The methods were further evaluated for similar predictive power by comparing the model predicted outputs with unweighted pair group method with arithmetic mean (UPGMA) clustering. There were significant differences in the estimated number of deer in the study region, based on the geostatistical estimation procedure used (range: 385 939-768 493). There were also substantial differences in the predicted magnitude of the FMD outbreaks (range: 1 563-8 896) and land area affected (range: 56-447 km(2)) for the different estimated animal distributions. UPGMA clustering indicated there were two main groups of distributions, and one outlier. We recommend that one distribution from each of these two groups be used to model the range of possible outbreaks. Methods included in cluster 1 (such as county-level disaggregation) could be used in conjunction with any of the methods in cluster 2, which included kriging, NDVI split by ecoregion, or disaggregation at the regional level, to represent the variability in the model predicted outbreak distributions. How

  17. Comparison of ArcGIS and SAS Geostatistical Analyst to Estimate Population-Weighted Monthly Temperature for US Counties.

    Science.gov (United States)

    Xiaopeng, Q I; Liang, Wei; Barker, Laurie; Lekiachvili, Akaki; Xingyou, Zhang

    Temperature changes are known to have significant impacts on human health. Accurate estimates of population-weighted average monthly air temperature for US counties are needed to evaluate temperature's association with health behaviours and disease, which are sampled or reported at the county level and measured on a monthly-or 30-day-basis. Most reported temperature estimates were calculated using ArcGIS, relatively few used SAS. We compared the performance of geostatistical models to estimate population-weighted average temperature in each month for counties in 48 states using ArcGIS v9.3 and SAS v 9.2 on a CITGO platform. Monthly average temperature for Jan-Dec 2007 and elevation from 5435 weather stations were used to estimate the temperature at county population centroids. County estimates were produced with elevation as a covariate. Performance of models was assessed by comparing adjusted R(2), mean squared error, root mean squared error, and processing time. Prediction accuracy for split validation was above 90% for 11 months in ArcGIS and all 12 months in SAS. Cokriging in SAS achieved higher prediction accuracy and lower estimation bias as compared to cokriging in ArcGIS. County-level estimates produced by both packages were positively correlated (adjusted R(2) range=0.95 to 0.99); accuracy and precision improved with elevation as a covariate. Both methods from ArcGIS and SAS are reliable for U.S. county-level temperature estimates; However, ArcGIS's merits in spatial data pre-processing and processing time may be important considerations for software selection, especially for multi-year or multi-state projects.

  18. The sensitivity of training image and integration of airborne 3D electromagnetic data in multiple-point geostatistical simulation and the impact on groundwater modeling

    Science.gov (United States)

    Jensen, K. H.; He, X.; Sonnenborg, T. O.; Jørgensen, F.

    2016-12-01

    Multiple-point geostatistical simulation (MPS) of the geological structure has become popular in recent years in groundwater modeling. The method derives multi-point based structural information from a training image (TI) and as such is superior to the traditional two-point based geostatistical approach. Its application in 3D simulations has been constrained by the difficulty of constructing 3D TI. High resolution 3D electromagnetic data can be used for defining a TI but the data can also be used as secondary data for soft conditioning. An alternative approach for derived a TI is to use the object-based unconditional simulation program TiGenerator. In this study we present different MPS simulations of the geological structure for a site in Denmark based on different scenarios regarding TI and soft conditioning. The generated geostatistical realizations are used for developing groundwater models based on MODFLOW and each of these models is calibrated against hydraulic head measurements using the inversion code PEST. Based on the calibrated flow models the particle tracking code MODPATH is used to simulate probabilistic capture zones for abstraction wells. By comparing simulations of groundwater flow and probabilistic capture zone, comparable results are obtained based on TI directly derived from high resolution geophysical data and generated by theTiGenerator even for the probabilistic capture zones, which are highly sensitive to the geological structure. The study further suggests that soft conditioning in MPS is an effective way of integrating secondary data such as 3D airborne electromagnetic data (SkyTEM) leading to improved estimations of the geological structure as evidenced by the resulting hydraulic parameter values. However, care should be taken when the same data source is used for defining the TI and for soft conditioning as this may lead reduction in the uncertainty estimation.

  19. 地质统计学在固体矿山中的应用%Application of Geostatistics in Solid Mine

    Institute of Scientific and Technical Information of China (English)

    刘焕荣; 燕永锋; 杨海涛

    2013-01-01

    As a rising and crossed subject,the geostatistics has obtained the development greatly during nearly 50 years of research and practice,which was also known as spatial information statistics in recent years.Producing practice,both at home and abroad,showed that geostatistics study has obvious advantages in geoscience,and has been greatly used in the study of solid mine.This paper mainly introduced the geostatistics applied in many ways such as the calculation of the re-serves of the mineral resources,the distributional characteristics of the minerals,the classification of the reserves,the opti-mization of the determine exploratory grid and the investigation of the minerals.%地质统计学作为一门新兴的交叉学科,在近50年的研究和实践中得到了很大的发展,近年来又被称为空间信息统计学。国内外的生产实践表明,地质统计学除了在地学科研方面具有明显的优越性,在固体矿山中的应用也越来越广泛。本文主要介绍了地质统计学在矿产资源储量计算、矿产分布特征、储量分类、勘探网度优化及矿产勘查等方面的应用。

  20. Spatial distribution of soil organic carbon and total nitrogen based on GIS and geostatistics in a small watershed in a hilly area of northern China.

    Science.gov (United States)

    Peng, Gao; Bing, Wang; Guangpo, Geng; Guangcan, Zhang

    2013-01-01

    The spatial variability of soil organic carbon (SOC) and total nitrogen (STN) levels is important in both global carbon-nitrogen cycle and climate change research. There has been little research on the spatial distribution of SOC and STN at the watershed scale based on geographic information systems (GIS) and geostatistics. Ninety-seven soil samples taken at depths of 0-20 cm were collected during October 2010 and 2011 from the Matiyu small watershed (4.2 km(2)) of a hilly area in Shandong Province, northern China. The impacts of different land use types, elevation, vegetation coverage and other factors on SOC and STN spatial distributions were examined using GIS and a geostatistical method, regression-kriging. The results show that the concentration variations of SOC and STN in the Matiyu small watershed were moderate variation based on the mean, median, minimum and maximum, and the coefficients of variation (CV). Residual values of SOC and STN had moderate spatial autocorrelations, and the Nugget/Sill were 0.2% and 0.1%, respectively. Distribution maps of regression-kriging revealed that both SOC and STN concentrations in the Matiyu watershed decreased from southeast to northwest. This result was similar to the watershed DEM trend and significantly correlated with land use type, elevation and aspect. SOC and STN predictions with the regression-kriging method were more accurate than those obtained using ordinary kriging. This research indicates that geostatistical characteristics of SOC and STN concentrations in the watershed were closely related to both land-use type and spatial topographic structure and that regression-kriging is suitable for investigating the spatial distributions of SOC and STN in the complex topography of the watershed.

  1. Migration, distribution and population (stock) structure of shallow-water hake (Merluccius capensis) in the Benguela Current Large Marine Ecosystem inferred using a geostatistical population model

    DEFF Research Database (Denmark)

    Jansen, Teunis; Kristensen, Kasper; Kainge, Paulus Inekela

    2016-01-01

    /nursery areas, through the juvenile phase and the adults' migration to the spawning areas outside/upstream of the nursery areas. This revealed some previously unknown migration patterns and indicated natal homing and the existence of three primary population components in the region, namely the Walvis (central...... (stock) structure. We combined data from multiple demersal trawl surveys from the entire distribution area to estimate growth rate, mortality and spatial and temporal patterns of M. capensis. Analyses were conducted using the geostatistical model GeoPop. The complexity of the model and the amount of data...

  2. 多点地质统计学研究进展与展望%Progress and prospect of multiple-point geostatistics

    Institute of Scientific and Technical Information of China (English)

    尹艳树; 张昌民; 李玖勇; 石书缘

    2011-01-01

    在简要回顾多点地质统计学起源后,介绍了多点地质统计学的3种方法,并总结了多点地质统计学的研究进展.在应用领域,已经从河流相建模发展到扇环境建模,从储集层结构建模发展到储集层物性分布模拟,从宏观地质体预测发展到微观孔喉分布建模,从地质研究发展到地质统计反演.在整合信息建模方面,给出了3种综合地震属性的方法;在算法方面,提出了PRTT实时处理方法,完善了多点地质统计学建模,并开发了新的多点统计生长算法(Growthsim).对多点地质统计学未来发展进行了展望,指出在训练图像、数据综合以及建模方法耦合方面还需要进一步深入研究.%The paper summarizes the progress of multiple point geostatistics. First the origin of multiple point geostatistics is introduced and the theories of three main multiple-point geostatistic methods are analyzed. Then, the development status is concluded in three aspects. Firstly, in real reservoir modeling domain, the modeling environment is from fluvial to fan facies; the modeling content is from reservoir architecture to reservoir petrophysical property; the modeling scale is from large geologic deposits to micro pores and throats; the modeling region is from geological modeling to geological statistic inversion. Secondly, in integrating multi-disciplines modeling domain, there are three methods for integrating seismic data. Thirdly, in modeling methods domain, there are some improvements and new methods such as PRTT and the Growthsim. Based on the analysis of the development of multiple point geostatistics, the paper points out that the training image, data integration and modeling coupration are the main aims in further studies.

  3. Geo-Statistics and its Application for Creating Iso-Maps in Hydro-Geology Electric Conductivity contours of Atrak sub basin, Iran

    Science.gov (United States)

    Allahdadi, M.; Partani, S.; Ahmadi, M.

    2012-12-01

    Taking discrete sampling from the water resource and measuring the parameters in quantity and quality and using the procedures of turning discrete points to integrated surface can lead the research process to surface variations consideration. There are different procedures for turning broken discrete points to integrated surface like procedure of Geostatistics which conclude Kriging procedure, Inverse Distance Weighting (IDW), Radial Basis Functions (RBF), Local Polynomial Interpolation, Global Polynomial Interpolation and Co-kriging. This research is going to explain any applications of Geostatistics for creating iso-maps such as underground water table contours, Iso-quality (for example EC and pH) contours, and place changes of too many other hydro-geological parameters. In this way, statistic signs such as Mean Absolute Error (MAE), Mean Absolute Relative Error (MARE), Root Mean Square Error (RMSE), MBE and Coefficient of Correlation, could be employed to select the best Geo-Statistic model in a GIS framework. Eventually, the power in IDW method has been optimized and also best Geo-Statistic method has been introduced for predicting the Electric Conductivity (EC) of underground water in Atrak plain. Finally any conclusions have been extracted. Table No. 1: Comparison of Statistical Indices for Best Estimator Selection G.S Method r RMSE MBE MAE Kriging 0.81 2200 485 1740 RBF 0.79 2263 427 1662 GPI 0.63 2981 347 2031 LPI 0.64 2942 349 2002 Power Optimized IDW 0.459 4816 -183 2845 The use of MAE shows the amount of absolute error, With regard to table No.1 kriging and RBF models have the best estimation which has the least absolute estimation error. As one of results figure No. 2 shows the Iso-Electric Conductivity Map which has been created by Kriging Method in a GIS Framework.; Figure No. 1: Power optimization in IDW method and the minimum of RMSE ; Figure No.2: Iso-Electric Conductivity Map which has been created by Kriging Method in a GIS Framework

  4. A geostatistical methodology for the optimal design of space-time hydraulic head monitoring networks and its application to the Valle de Querétaro aquifer.

    Science.gov (United States)

    Júnez-Ferreira, H E; Herrera, G S

    2013-04-01

    This paper presents a new methodology for the optimal design of space-time hydraulic head monitoring networks and its application to the Valle de Querétaro aquifer in Mexico. The selection of the space-time monitoring points is done using a static Kalman filter combined with a sequential optimization method. The Kalman filter requires as input a space-time covariance matrix, which is derived from a geostatistical analysis. A sequential optimization method that selects the space-time point that minimizes a function of the variance, in each step, is used. We demonstrate the methodology applying it to the redesign of the hydraulic head monitoring network of the Valle de Querétaro aquifer with the objective of selecting from a set of monitoring positions and times, those that minimize the spatiotemporal redundancy. The database for the geostatistical space-time analysis corresponds to information of 273 wells located within the aquifer for the period 1970-2007. A total of 1,435 hydraulic head data were used to construct the experimental space-time variogram. The results show that from the existing monitoring program that consists of 418 space-time monitoring points, only 178 are not redundant. The implied reduction of monitoring costs was possible because the proposed method is successful in propagating information in space and time.

  5. Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China.

    Science.gov (United States)

    Xiao, Yong; Gu, Xiaomin; Yin, Shiyang; Shao, Jingli; Cui, Yali; Zhang, Qiulan; Niu, Yong

    2016-01-01

    Based on the geo-statistical theory and ArcGIS geo-statistical module, datas of 30 groundwater level observation wells were used to estimate the decline of groundwater level in Beijing piedmont. Seven different interpolation methods (inverse distance weighted interpolation, global polynomial interpolation, local polynomial interpolation, tension spline interpolation, ordinary Kriging interpolation, simple Kriging interpolation and universal Kriging interpolation) were used for interpolating groundwater level between 2001 and 2013. Cross-validation, absolute error and coefficient of determination (R(2)) was applied to evaluate the accuracy of different methods. The result shows that simple Kriging method gave the best fit. The analysis of spatial and temporal variability suggest that the nugget effects from 2001 to 2013 were increasing, which means the spatial correlation weakened gradually under the influence of human activities. The spatial variability in the middle areas of the alluvial-proluvial fan is relatively higher than area in top and bottom. Since the changes of the land use, groundwater level also has a temporal variation, the average decline rate of groundwater level between 2007 and 2013 increases compared with 2001-2006. Urban development and population growth cause over-exploitation of residential and industrial areas. The decline rate of the groundwater level in residential, industrial and river areas is relatively high, while the decreasing of farmland area and development of water-saving irrigation reduce the quantity of water using by agriculture and decline rate of groundwater level in agricultural area is not significant.

  6. Spatial analysis of the distribution of Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) and losses in maize crop productivity using geostatistics.

    Science.gov (United States)

    Farias, Paulo R S; Barbosa, José C; Busoli, Antonio C; Overal, William L; Miranda, Vicente S; Ribeiro, Susane M

    2008-01-01

    The fall armyworm, Spodoptera frugiperda (J.E. Smith), is one of the chief pests of maize in the Americas. The study of its spatial distribution is fundamental for designing correct control strategies, improving sampling methods, determining actual and potential crop losses, and adopting precise agricultural techniques. In São Paulo state, Brazil, a maize field was sampled at weekly intervals, from germination through harvest, for caterpillar densities, using quadrates. In each of 200 quadrates, 10 plants were sampled per week. Harvest weights were obtained in the field for each quadrate, and ear diameters and lengths were also sampled (15 ears per quadrate) and used to estimate potential productivity of the quadrate. Geostatistical analyses of caterpillar densities showed greatest ranges for small caterpillars when semivariograms were adjusted for a spherical model that showed greatest fit. As the caterpillars developed in the field, their spatial distribution became increasingly random, as shown by a model adjusted to a straight line, indicating a lack of spatial dependence among samples. Harvest weight and ear length followed the spherical model, indicating the existence of spatial variability of the production parameters in the maize field. Geostatistics shows promise for the application of precise methods in the integrated control of pests.

  7. Use of geostatistics to determine the spatial distribution and infestation rate of leaf-cutting ant nests (Hymenoptera: Formicidae) in eucalyptus plantations.

    Science.gov (United States)

    Lasmar, O; Zanetti, R; dos Santos, A; Fernandes, B V

    2012-08-01

    One of the fundamental steps in pest sampling is the assessment of the population distribution in the field. Several studies have investigated the distribution and appropriate sampling methods for leaf-cutting ants; however, more reliable methods are still required, such as those that use geostatistics. The objective of this study was to determine the spatial distribution and infestation rate of leaf-cutting ant nests in eucalyptus plantations by using geostatistics. The study was carried out in 2008 in two eucalyptus stands in Paraopeba, Minas Gerais, Brazil. All of the nests in the studied area were located and used for the generation of GIS maps, and the spatial pattern of distribution was determined considering the number and size of nests. Each analysis and map was made using the R statistics program and the geoR package. The nest spatial distribution in a savanna area of Minas Gerais was clustered to a certain extent. The models generated allowed the production of kriging maps of areas infested with leaf-cutting ants, where chemical intervention would be necessary, reducing the control costs, impact on humans, and the environment.

  8. Spatial Analysis of Phytophthora infestans Genotypes and Late Blight Severity on Tomato and Potato in the Del Fuerte Valley Using Geostatistics and Geographic Information Systems.

    Science.gov (United States)

    Jaime-Garcia, R; Orum, T V; Felix-Gastelum, R; Trinidad-Correa, R; Vanetten, H D; Nelson, M R

    2001-12-01

    ABSTRACT Genetic structure of Phytophthora infestans, the causal agent of potato and tomato late blight, was analyzed spatially in a mixed potato and tomato production area in the Del Fuerte Valley, Sinaloa, Mexico. Isolates of P. infestans were characterized by mating type, allozyme analysis at the glucose-6-phosphate isomerase and peptidase loci, restriction fragment length polymorphism with probe RG57, metalaxyl sensitivity, and aggressiveness to tomato and potato. Spatial patterns of P. infestans genotypes were analyzed by geographical information systems and geo-statistics during the seasons of 1994-95, 1995-96, and 1996-97. Spatial analysis of the genetic structure of P. infestans indicates that geographic substructuring of this pathogen occurs in this area. Maps displaying the probabilities of occurrence of mating types and genotypes of P. infestans, and of disease severity at a regional scale, were presented. Some genotypes that exhibited differences in epidemiologically important features such as metalaxyl sensitivity and aggressiveness to tomato and potato had a restricted spread and were localized in isolated areas. Analysis of late blight severity showed recurring patterns, such as the earliest onset of the disease in the area where both potato and tomato were growing, strengthening the hypothesis that infected potato tubers are the main source of primary inoculum. The information that geostatistical analysis provides might help improve management programs for late blight in the Del Fuerte Valley.

  9. Geostatistical Analysis of Winter Rainfall for 2013 in Eastern Black Sea Basin, Turkey (comparison of the past status and future projections)

    Science.gov (United States)

    Ustaoglu, Beyza

    2014-05-01

    Rainfall is one of the most important climatic factor for environmental studies. Several methods (Thiessen polygon, Inverse Distance Weighting (IDW) and Kriging etc.) have been used by researchers for spatial interpolation of rainfall data. Kriging is a geostatistical method which is based on spatial correlation between neighbouring observations to predict attribute values at unsampled locations. The study area, Eastern Black Sea Basin is one of the highest rainfall accumulations in Turkey according to the measured station data (1942 - 2011). Eastern Black Sea Basin is the only basin in Turkey with an increase amount of winter (October, November, December) rainfall for 2013 in comparison to the long term mean and previous year winter rainfall. Regarding to the future projections (Ustaoglu, 2011), this basin has one of the strongest increasing trend according to the A2 scenario analysis obtained from RegCM3 regional climate model during the ten years periods (2011 - 2100). In this study, 2013 winter rainfall in the basin is highlighted and compared with the past and future rainfall conditions of the basin. Keywords: Geostatistical Analysis, Winter Rainfall, Eastern Black Sea Basin

  10. Geostatistical model-based estimates of Schistosomiasis prevalence among individuals aged ≤ 20 years in West Africa.

    Directory of Open Access Journals (Sweden)

    Nadine Schur

    2011-06-01

    Full Text Available BACKGROUND: Schistosomiasis is a water-based disease that is believed to affect over 200 million people with an estimated 97% of the infections concentrated in Africa. However, these statistics are largely based on population re-adjusted data originally published by Utroska and colleagues more than 20 years ago. Hence, these estimates are outdated due to large-scale preventive chemotherapy programs, improved sanitation, water resources development and management, among other reasons. For planning, coordination, and evaluation of control activities, it is essential to possess reliable schistosomiasis prevalence maps. METHODOLOGY: We analyzed survey data compiled on a newly established open-access global neglected tropical diseases database (i to create smooth empirical prevalence maps for Schistosoma mansoni and S. haematobium for individuals aged ≤ 20 years in West Africa, including Cameroon, and (ii to derive country-specific prevalence estimates. We used Bayesian geostatistical models based on environmental predictors to take into account potential clustering due to common spatially structured exposures. Prediction at unobserved locations was facilitated by joint kriging. PRINCIPAL FINDINGS: Our models revealed that 50.8 million individuals aged ≤ 20 years in West Africa are infected with either S. mansoni, or S. haematobium, or both species concurrently. The country prevalence estimates ranged between 0.5% (The Gambia and 37.1% (Liberia for S. mansoni, and between 17.6% (The Gambia and 51.6% (Sierra Leone for S. haematobium. We observed that the combined prevalence for both schistosome species is two-fold lower in Gambia than previously reported, while we found an almost two-fold higher estimate for Liberia (58.3% than reported before (30.0%. Our predictions are likely to overestimate overall country prevalence, since modeling was based on children and adolescents up to the age of 20 years who are at highest risk of infection. CONCLUSION

  11. Soil Moisture Mapping in an Arid Area Using a Land Unit Area (LUA Sampling Approach and Geostatistical Interpolation Techniques

    Directory of Open Access Journals (Sweden)

    Saeid Gharechelou

    2016-03-01

    Full Text Available Soil moisture (SM plays a key role in many environmental processes and has a high spatial and temporal variability. Collecting sample SM data through field surveys (e.g., for validation of remote sensing-derived products can be very expensive and time consuming if a study area is large, and producing accurate SM maps from the sample point data is a difficult task as well. In this study, geospatial processing techniques are used to combine several geo-environmental layers relevant to SM (soil, geology, rainfall, land cover, etc. into a land unit area (LUA map, which delineates regions with relatively homogeneous geological/geomorphological, land use/land cover, and climate characteristics. This LUA map is used to guide the collection of sample SM data in the field, and the field data is finally spatially interpolated to create a wall-to-wall map of SM in the study area (Garmsar, Iran. The main goal of this research is to create a SM map in an arid area, using a land unit area (LUA approach to obtain the most appropriate sample locations for collecting SM field data. Several environmental GIS layers, which have an impact on SM, were combined to generate a LUA map, and then field surveying was done in each class of the LUA map. A SM map was produced based on LUA, remote sensing data indexes, and spatial interpolation of the field survey sample data. The several interpolation methods (inverse distance weighting, kriging, and co-kriging were evaluated for generating SM maps from the sample data. The produced maps were compared to each other and validated using ground truth data. The results show that the LUA approach is a reasonable method to create the homogenous field to introduce a representative sample for field soil surveying. The geostatistical SM map achieved adequate accuracy; however, trend analysis and distribution of the soil sample point locations within the LUA types should be further investigated to achieve even better results. Co

  12. Information for decision making from imperfect national data: tracking major changes in health care use in Kenya using geostatistics

    Directory of Open Access Journals (Sweden)

    Hay Simon I

    2007-12-01

    Full Text Available Abstract Background Most Ministries of Health across Africa invest substantial resources in some form of health management information system (HMIS to coordinate the routine acquisition and compilation of monthly treatment and attendance records from health facilities nationwide. Despite the expense of these systems, poor data coverage means they are rarely, if ever, used to generate reliable evidence for decision makers. One critical weakness across Africa is the current lack of capacity to effectively monitor patterns of service use through time so that the impacts of changes in policy or service delivery can be evaluated. Here, we present a new approach that, for the first time, allows national changes in health service use during a time of major health policy change to be tracked reliably using imperfect data from a national HMIS. Methods Monthly attendance records were obtained from the Kenyan HMIS for 1 271 government-run and 402 faith-based outpatient facilities nationwide between 1996 and 2004. A space-time geostatistical model was used to compensate for the large proportion of missing records caused by non-reporting health facilities, allowing robust estimation of monthly and annual use of services by outpatients during this period. Results We were able to reconstruct robust time series of mean levels of outpatient utilisation of health facilities at the national level and for all six major provinces in Kenya. These plots revealed reliably for the first time a period of steady nationwide decline in the use of health facilities in Kenya between 1996 and 2002, followed by a dramatic increase from 2003. This pattern was consistent across different causes of attendance and was observed independently in each province. Conclusion The methodological approach presented can compensate for missing records in health information systems to provide robust estimates of national patterns of outpatient service use. This represents the first such use of

  13. Geostatistical Characteristic of Space -Time Variation in Underground Water Selected Quality Parameters in Klodzko Water Intake Area (SW Part of Poland)

    Science.gov (United States)

    Namysłowska-Wilczyńska, Barbara

    2016-04-01

    This paper presents selected results of research connected with the development of a (3D) geostatistical hydrogeochemical model of the Klodzko Drainage Basin, dedicated to the spatial and time variation in the selected quality parameters of underground water in the Klodzko water intake area (SW part of Poland). The research covers the period 2011÷2012. Spatial analyses of the variation in various quality parameters, i.e, contents of: ammonium ion [gNH4+/m3], NO3- (nitrate ion) [gNO3/m3], PO4-3 (phosphate ion) [gPO4-3/m3], total organic carbon C (TOC) [gC/m3], pH redox potential and temperature C [degrees], were carried out on the basis of the chemical determinations of the quality parameters of underground water samples taken from the wells in the water intake area. Spatial and time variation in the quality parameters was analyzed on the basis of archival data (period 1977÷1999) for 22 (pump and siphon) wells with a depth ranging from 9.5 to 38.0 m b.g.l., later data obtained (November 2011) from tests of water taken from 14 existing wells. The wells were built in the years 1954÷1998. The water abstraction depth (difference between the terrain elevation and the dynamic water table level) is ranged from 276÷286 m a.s.l., with an average of 282.05 m a.s.l. Dynamic water table level is contained between 6.22 m÷16.44 m b.g.l., with a mean value of 9.64 m b.g.l. The latest data (January 2012) acquired from 3 new piezometers, with a depth of 9÷10m, which were made in other locations in the relevant area. Thematic databases, containing original data on coordinates X, Y (latitude, longitude) and Z (terrain elevation and time - years) and on regionalized variables, i.e. the underground water quality parameters in the Klodzko water intake area determined for different analytical configurations (22 wells, 14 wells, 14 wells + 3 piezometers), were created. Both archival data (acquired in the years 1977÷1999) and the latest data (collected in 2011÷2012) were analyzed

  14. Geostatistics: application of kriging and conditional simulation techniques in the reservoirs CPS-2 of the Carmopolis Field, Sergipe State, Brazil; Geoestatistica: Aplicacao das tecnicas de krigagem e simulacao condicional nos reservatorios CPS-2 do Campo de Carmopolis, Sergipe, Brasil

    Energy Technology Data Exchange (ETDEWEB)

    Souza, M.J. de [PETROBRAS, Rio de Janeiro, RJ (Brazil); Marcotte, D. [Montreal Univ., PQ (Canada). Dept. de Genie Mineral

    1992-07-01

    Description of the parameters used to do an evaluation of wells oil production are discussed. The global estimate production of the CPS-2 (PETROBRAS oil well) on the Carmopolis Field, using geostatistical techniques and a comparative study of the oil production simulation using the kriging techniques are also presented. 16 figs., 2 tabs., 30 refs.

  15. Geostatistics for Large Datasets

    KAUST Repository

    Sun, Ying

    2011-10-31

    Each chapter should be preceded by an abstract (10–15 lines long) that summarizes the content. The abstract will appear onlineat www.SpringerLink.com and be available with unrestricted access. This allows unregistered users to read the abstract as a teaser for the complete chapter. As a general rule the abstracts will not appear in the printed version of your book unless it is the style of your particular book or that of the series to which your book belongs. Please use the ’starred’ version of the new Springer abstractcommand for typesetting the text of the online abstracts (cf. source file of this chapter template abstract) and include them with the source files of your manuscript. Use the plain abstractcommand if the abstract is also to appear in the printed version of the book.

  16. Hungarian contribution to the Global Soil Organic Carbon Map (GSOC17) using advanced machine learning algorithms and geostatistics

    Science.gov (United States)

    Szatmári, Gábor; Laborczi, Annamária; Takács, Katalin; Pásztor, László

    2017-04-01

    The knowledge about soil organic carbon (SOC) baselines and changes, and the detection of vulnerable hot spots for SOC losses and gains under climate change and changed land management is still fairly limited. Thus Global Soil Partnership (GSP) has been requested to develop a global SOC mapping campaign by 2017. GSPs concept builds on official national data sets, therefore, a bottom-up (country-driven) approach is pursued. The elaborated Hungarian methodology suits the general specifications of GSOC17 provided by GSP. The input data for GSOC17@HU mapping approach has involved legacy soil data bases, as well as proper environmental covariates related to the main soil forming factors, such as climate, organisms, relief and parent material. Nowadays, digital soil mapping (DSM) highly relies on the assumption that soil properties of interest can be modelled as a sum of a deterministic and stochastic component, which can be treated and modelled separately. We also adopted this assumption in our methodology. In practice, multiple regression techniques are commonly used to model the deterministic part. However, this global (and usually linear) models commonly oversimplify the often complex and non-linear relationship, which has a crucial effect on the resulted soil maps. Thus, we integrated machine learning algorithms (namely random forest and quantile regression forest) in the elaborated methodology, supposing then to be more suitable for the problem in hand. This approach has enable us to model the GSOC17 soil properties in that complex and non-linear forms as the soil itself. Furthermore, it has enable us to model and assess the uncertainty of the results, which is highly relevant in decision making. The applied methodology has used geostatistical approach to model the stochastic part of the spatial variability of the soil properties of interest. We created GSOC17@HU map with 1 km grid resolution according to the GSPs specifications. The map contributes to the GSPs

  17. Potential of deterministic and geostatistical rainfall interpolation under high rainfall variability and dry spells: case of Kenya's Central Highlands

    Science.gov (United States)

    Kisaka, M. Oscar; Mucheru-Muna, M.; Ngetich, F. K.; Mugwe, J.; Mugendi, D.; Mairura, F.; Shisanya, C.; Makokha, G. L.

    2016-04-01

    digital elevation model in ArcGIS environment. Validation of the selected interpolation methods were based on goodness of fit between gauged (observed) and generated rainfall derived from residual errors statistics, coefficient of determination (R 2), mean absolute errors (MAE) and root mean square error (RMSE) statistics. Analyses showed 90 % chance of below cropping-threshold rainfall (500 mm) exceeding 258.1 mm during short rains in Embu for 1 year return period. Rainfall variability was found to be high in seasonal amounts (e.g. coefficient of variation (CV) = 0.56, 0.47, 0.59) and in number of rainy days (e.g. CV = 0.88, 0.53) in Machang'a and Kiritiri, respectively. Monthly rainfall variability was found to be equally high during April and November (e.g. CV = 0.48, 0.49 and 0.76) with high probabilities (0.67) of droughts exceeding 15 days in Machang'a. Dry spell probabilities within growing months were high, e.g. 81 and 60 % in Machang'a and Embu, respectively. Kriging interpolation method emerged as the most appropriate geostatistical interpolation technique suitable for spatial rainfall maps generation for the study region.

  18. Indicator and Multivariate Geostatistics for Spatial Prediction%基于指示和多元地质统计学的空间预测方法

    Institute of Scientific and Technical Information of China (English)

    张景雄; 姚娜

    2008-01-01

    There are various occasions where simple,ordinary,and universal kriging techniques may find themselves incapa-ble of performing spatial prediction directly or efficiently.One type of application concerns quantification of cumulative distri-bution function (CDF) or probability of occurrences of categorical variables over space.The other is related to optimal use of co-variation inherent to multiple regionalized variables as well as spatial correlation in spatial prediction.This paper extends geostatistics from the realm of kriging with uni-variate and continuous regionalized variables to the territory of indicator and multivariate kriging,where it is of ultimate importance to perform non-parametric estimation of probability distributions and spatial prediction based on co-regionalization and multiple data sources,respectively.

  19. A novel geotechnical/geostatistical approach for exploration and production of natural gas from multiple geologic strata, Phase 1. Volume 2, Geology and engineering

    Energy Technology Data Exchange (ETDEWEB)

    Overbey, W.K. Jr.; Reeves, T.K.; Salamy, S.P.; Locke, C.D.; Johnson, H.R.; Brunk, R.; Hawkins, L. [BDM Engineering Services Co., Morgantown, WV (United States)

    1991-05-01

    This research program has been designed to develop and verify a unique geostatistical approach for finding natural gas resources. The project has been conducted by Beckley College, Inc., and BDM Engineering Services Company (BDMESC) under contract to the US Department of Energy (DOE), Morgantown Energy Technology Center (METC). This section, Volume II, contains a detailed discussion of the methodology used and the geological and production information collected and analyzed for this study. A companion document, Volume 1, provides an overview of the program, technique and results of the study. In combination, Volumes I and II cover the completion of the research undertaken under Phase I of this DOE project, which included the identification of five high-potential sites for natural gas production on the Eccles Quadrangle, Raleigh County, West Virginia. Each of these sites was selected for its excellent potential for gas production from both relatively shallow coalbeds and the deeper, conventional reservoir formations.

  20. A novel geotechnical/geostatistical approach for exploration and production of natural gas from multiple geologic strata, Phase 1. Volume 2, Geology and engineering

    Energy Technology Data Exchange (ETDEWEB)

    Overbey, W.K. Jr.; Reeves, T.K.; Salamy, S.P.; Locke, C.D.; Johnson, H.R.; Brunk, R.; Hawkins, L. [BDM Engineering Services Co., Morgantown, WV (United States)

    1991-05-01

    This research program has been designed to develop and verify a unique geostatistical approach for finding natural gas resources. The project has been conducted by Beckley College, Inc., and BDM Engineering Services Company (BDMESC) under contract to the US Department of Energy (DOE), Morgantown Energy Technology Center (METC). This section, Volume II, contains a detailed discussion of the methodology used and the geological and production information collected and analyzed for this study. A companion document, Volume 1, provides an overview of the program, technique and results of the study. In combination, Volumes I and II cover the completion of the research undertaken under Phase I of this DOE project, which included the identification of five high-potential sites for natural gas production on the Eccles Quadrangle, Raleigh County, West Virginia. Each of these sites was selected for its excellent potential for gas production from both relatively shallow coalbeds and the deeper, conventional reservoir formations.

  1. Applications of stochastic models and geostatistical analyses to study sources and spatial patterns of soil heavy metals in a metalliferous industrial district of China

    Energy Technology Data Exchange (ETDEWEB)

    Zhong, Buqing; Liang, Tao, E-mail: liangt@igsnrr.ac.cn; Wang, Lingqing; Li, Kexin

    2014-08-15

    An extensive soil survey was conducted to study pollution sources and delineate contamination of heavy metals in one of the metalliferous industrial bases, in the karst areas of southwest China. A total of 597 topsoil samples were collected and the concentrations of five heavy metals, namely Cd, As (metalloid), Pb, Hg and Cr were analyzed. Stochastic models including a conditional inference tree (CIT) and a finite mixture distribution model (FMDM) were applied to identify the sources and partition the contribution from natural and anthropogenic sources for heavy metal in topsoils of the study area. Regression trees for Cd, As, Pb and Hg were proved to depend mostly on indicators of anthropogenic activities such as industrial type and distance from urban area, while the regression tree for Cr was found to be mainly influenced by the geogenic characteristics. The FMDM analysis showed that the geometric means of modeled background values for Cd, As, Pb, Hg and Cr were close to their background values previously reported in the study area, while the contamination of Cd and Hg were widespread in the study area, imposing potentially detrimental effects on organisms through the food chain. Finally, the probabilities of single and multiple heavy metals exceeding the threshold values derived from the FMDM were estimated using indicator kriging (IK) and multivariate indicator kriging (MVIK). The high probabilities exceeding the thresholds of heavy metals were associated with metalliferous production and atmospheric deposition of heavy metals transported from the urban and industrial areas. Geostatistics coupled with stochastic models provide an effective way to delineate multiple heavy metal pollution to facilitate improved environmental management. - Highlights: • Conditional inference tree can identify variables controlling metal distribution. • Finite mixture distribution model can partition natural and anthropogenic sources. • Geostatistics with stochastic models

  2. [Spatial variability of soil nutrients based on geostatistics combined with GIS--a case study in Zunghua City of Hebei Province].

    Science.gov (United States)

    Guo, X; Fu, B; Ma, K; Chen, L

    2000-08-01

    Geostatistics combined with GIS was applied to analyze the spatial variability of soil nutrients in topsoil (0-20 cm) in Zunghua City of Hebei Province. GIS can integrate attribute data with geographical data of system variables, which makes the application of geostatistics technique for large spatial scale more convenient. Soil nutrient data in this study included available N (alkaline hydrolyzing nitrogen), total N, available K, available P and organic matter. The results showed that the semivariograms of soil nutrients were best described by spherical model, except for that of available K, which was best fitted by complex structure of exponential model and linear with sill model. The spatial variability of available K was mainly produced by structural factor, while that of available N, total N, available P and organic matter was primarily caused by random factor. However, their spatial heterogeneity degree was different: the degree of total N and organic matter was higher, and that of available P and available N was lower. The results also indicated that the spatial correlation of the five tested soil nutrients at this large scale was moderately dependent. The ranges of available N and available P were almost same, which were 5 km and 5.5 km, respectively. The range of total N was up to 18 km, and that of organic matter was 8.5 km. For available K, the spatial variability scale primarily expressed exponential model between 0-3.5 km, but linear with sill model between 3.5-25.5 km. In addition, five soil nutrients exhibited different isotropic ranges. Available N and available P were isotropic through the whole research range (0-28 km). The isotropic range of available K was 0-8 km, and that of total N and organic matter was 0-10 km.

  3. Using Multivariate Statistical and Geostatistical Methods to Identify Spatial Variability of Trace Elements in Agricultural Soils in Dongguan City,Guangdong,China

    Institute of Scientific and Technical Information of China (English)

    Dou Lei; Zhou Yongzhang; Ma Jin; Li Yong; Cheng Qiuming; Xie Shuyun; Du Haiyan; You Yuanhang; Wan Hongfu

    2008-01-01

    Dongguan (东莞) City, located in the Pearl River Delta, South China, is famous for its rapid industrialization in the past 30 years. A total of 90 topsoil samples have been collected from agricultural fields, including vegetable and orchard soils in the city, and eight heavy metals (As, Cu, Cd,Cr, Hg, Ni, Pb, and Zn) and other items (pH values and organic matter) have been analyzed, to evaluate the influence of anthropie activities on the environmental quality of agricultural soils and to identify the spatial distribution of trace elements and possible sources of trace elements. The elements Hg, Pb, and Cd have accumulated remarkably here, incomparison with the soil background content of elements in Guangdong (广东) Province. Pollution is more serious in the western plain and the central region, which are heavily distributed with industries and rivers. Multivariate and geostatistical methods have been applied to differentiate the influences of natural processes and human activities on the pollution of heavy metals in topsoils in the study area. The results of cluster analysis (CA) and factor analysis (FA) show that Ni, Cr, Cu, Zn, and As are grouped in factor F1,Pb in F2, and Cd and Hg in F3, respectively. The spatial pattern of the three factors may be well demonstrated by geostatistical analysis. It is shown that the first factor could be considered as a natural source controlled by parent rocks. The second factor could be referred to as "industrial and traffic pollution sources". The source of the third factor is mainly controlled by long-term anthropic activities ,ad a consequence of agricultural fossil fuel consumption and atmospheric deposition.

  4. Identifying and closing gaps in environmental monitoring by means of metadata, ecological regionalization and geostatistics using the UNESCO biosphere reserve Rhoen (Germany) as an example.

    Science.gov (United States)

    Schröder, Winfried; Pesch, Roland; Schmidt, Gunther

    2006-03-01

    In Germany, environmental monitoring is intended to provide a holistic view of the environmental condition. To this end the monitoring operated by the federal states must use harmonized, resp., standardized methods. In addition, the monitoring sites should cover the ecoregions without any geographical gaps, the monitoring design should have no gaps in terms of ecologically relevant measurement parameters, and the sample data should be spatially without any gaps. This article outlines the extent to which the Rhoen Biosphere Reserve, occupying a part of the German federal states of Bavaria, Hesse and Thuringia, fulfills the listed requirements. The investigation considered collection, data banking and analysis of monitoring data and metadata, ecological regionalization and geostatistics. Metadata on the monitoring networks were collected by questionnaires and provided a complete inventory and description of the monitoring activities in the reserve and its surroundings. The analysis of these metadata reveals that most of the monitoring methods are harmonized across the boundaries of the three federal states the Rhoen is part of. The monitoring networks that measure precipitation, surface water levels, and groundwater quality are particularly overrepresented in the central ecoregions of the biosphere reserve. Soil monitoring sites are more equally distributed within the ecoregions of the Rhoen. The number of sites for the monitoring of air pollutants is not sufficient to draw spatially valid conclusions. To fill these spatial gaps, additional data on the annual average values of the concentrations of air pollutants from monitoring sites outside of the biosphere reserve had therefore been subject to geostatistical analysis and estimation. This yields valid information on the spatial patterns and temporal trends of air quality. The approach illustrated is applicable to similar cases, as, for example, the harmonization of international monitoring networks.

  5. Analysis of the spatio-temporal distribution of Eurygaster integriceps (Hemiptera: Scutelleridae) by using spatial analysis by distance indices and geostatistics.

    Science.gov (United States)

    Karimzadeh, R; Hejazi, M J; Helali, H; Iranipour, S; Mohammadi, S A

    2011-10-01

    Eurygaster integriceps Puton (Hemiptera: Scutelleridae) is the most serious insect pest of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) in Iran. In this study, spatio-temporal distribution of this pest was determined in wheat by using spatial analysis by distance indices (SADIE) and geostatistics. Global positioning and geographic information systems were used for spatial sampling and mapping the distribution of this insect. The study was conducted for three growing seasons in Gharamalek, an agricultural region to the west of Tabriz, Iran. Weekly sampling began when E. integriceps adults migrated to wheat fields from overwintering sites and ended when the new generation adults appeared at the end of season. The adults were sampled using 1- by 1-m quadrat and distance-walk methods. A sweep net was used for sampling the nymphs, and five 180° sweeps were considered as the sampling unit. The results of spatial analyses by using geostatistics and SADIE indicated that E. integriceps adults were clumped after migration to fields and had significant spatial dependency. The second- and third-instar nymphs showed aggregated spatial structure in the middle of growing season. At the end of the season, population distribution changed toward random or regular patterns; and fourth and fifth instars had weaker spatial structure compared with younger nymphs. In Iran, management measures for E. integriceps in wheat fields are mainly applied against overwintering adults, as well as second and third instars. Because of the aggregated distribution of these life stages, site-specific spraying of chemicals is feasible in managing E. integriceps.

  6. The use of a genetic algorithm-based search strategy in geostatistics: application to a set of anisotropic piezometric head data

    Science.gov (United States)

    Abedini, M. J.; Nasseri, M.; Burn, D. H.

    2012-04-01

    In any geostatistical study, an important consideration is the choice of an appropriate, repeatable, and objective search strategy that controls the nearby samples to be included in the location-specific estimation procedure. Almost all geostatistical software available in the market puts the onus on the user to supply search strategy parameters in a heuristic manner. These parameters are solely controlled by geographical coordinates that are defined for the entire area under study, and the user has no guidance as to how to choose these parameters. The main thesis of the current study is that the selection of search strategy parameters has to be driven by data—both the spatial coordinates and the sample values—and cannot be chosen beforehand. For this purpose, a genetic-algorithm-based ordinary kriging with moving neighborhood technique is proposed. The search capability of a genetic algorithm is exploited to search the feature space for appropriate, either local or global, search strategy parameters. Radius of circle/sphere and/or radii of standard or rotated ellipse/ellipsoid are considered as the decision variables to be optimized by GA. The superiority of GA-based ordinary kriging is demonstrated through application to the Wolfcamp Aquifer piezometric head data. Assessment of numerical results showed that definition of search strategy parameters based on both geographical coordinates and sample values improves cross-validation statistics when compared with that based on geographical coordinates alone. In the case of a variable search neighborhood for each estimation point, optimization of local search strategy parameters for an elliptical support domain—the orientation of which is dictated by anisotropic axes—via GA was able to capture the dynamics of piezometric head in west Texas/New Mexico in an efficient way.

  7. Optimal design of sampling and mapping schemes in the radiometric exploration of Chipilapa, El Salvador (Geo-statistics); Diseno optimo de esquemas de muestreo y mapeo en la exploracion radiometrica de Chipilapa, El Salvador (Geo-estadistica)

    Energy Technology Data Exchange (ETDEWEB)

    Balcazar G, M.; Flores R, J.H

    1992-01-15

    As part of the knowledge about the radiometric surface exploration, carried out in the geothermal field of Chipilapa, El Salvador, its were considered the geo-statistical parameters starting from the calculated variogram of the field data, being that the maxim distance of correlation of the samples in 'radon' in the different observation addresses (N-S, E-W, N W-S E, N E-S W), it was of 121 mts for the monitoring grill in future prospectus in the same area. Being derived of it an optimization (minimum cost) in the spacing of the field samples by means of geo-statistical techniques, without losing the detection of the anomaly. (Author)

  8. Geographically weighted regression and geostatistical techniques to construct the geogenic radon potential map of the Lazio region: A methodological proposal for the European Atlas of Natural Radiation.

    Science.gov (United States)

    Ciotoli, G; Voltaggio, M; Tuccimei, P; Soligo, M; Pasculli, A; Beaubien, S E; Bigi, S

    2017-01-01

    In many countries, assessment programmes are carried out to identify areas where people may be exposed to high radon levels. These programmes often involve detailed mapping, followed by spatial interpolation and extrapolation of the results based on the correlation of indoor radon values with other parameters (e.g., lithology, permeability and airborne total gamma radiation) to optimise the radon hazard maps at the municipal and/or regional scale. In the present work, Geographical Weighted Regression and geostatistics are used to estimate the Geogenic Radon Potential (GRP) of the Lazio Region, assuming that the radon risk only depends on the geological and environmental characteristics of the study area. A wide geodatabase has been organised including about 8000 samples of soil-gas radon, as well as other proxy variables, such as radium and uranium content of homogeneous geological units, rock permeability, and faults and topography often associated with radon production/migration in the shallow environment. All these data have been processed in a Geographic Information System (GIS) using geospatial analysis and geostatistics to produce base thematic maps in a 1000 m × 1000 m grid format. Global Ordinary Least Squared (OLS) regression and local Geographical Weighted Regression (GWR) have been applied and compared assuming that the relationships between radon activities and the environmental variables are not spatially stationary, but vary locally according to the GRP. The spatial regression model has been elaborated considering soil-gas radon concentrations as the response variable and developing proxy variables as predictors through the use of a training dataset. Then a validation procedure was used to predict soil-gas radon values using a test dataset. Finally, the predicted values were interpolated using the kriging algorithm to obtain the GRP map of the Lazio region. The map shows some high GRP areas corresponding to the volcanic terrains (central

  9. Geostatistical modeling of the gas emission zone and its in-place gas content for Pittsburgh-seam mines using sequential Gaussian simulation

    Science.gov (United States)

    Karacan, C.O.; Olea, R.A.; Goodman, G.

    2012-01-01

    Determination of the size of the gas emission zone, the locations of gas sources within, and especially the amount of gas retained in those zones is one of the most important steps for designing a successful methane control strategy and an efficient ventilation system in longwall coal mining. The formation of the gas emission zone and the potential amount of gas-in-place (GIP) that might be available for migration into a mine are factors of local geology and rock properties that usually show spatial variability in continuity and may also show geometric anisotropy. Geostatistical methods are used here for modeling and prediction of gas amounts and for assessing their associated uncertainty in gas emission zones of longwall mines for methane control.This study used core data obtained from 276 vertical exploration boreholes drilled from the surface to the bottom of the Pittsburgh coal seam in a mining district in the Northern Appalachian basin. After identifying important coal and non-coal layers for the gas emission zone, univariate statistical and semivariogram analyses were conducted for data from different formations to define the distribution and continuity of various attributes. Sequential simulations performed stochastic assessment of these attributes, such as gas content, strata thickness, and strata displacement. These analyses were followed by calculations of gas-in-place and their uncertainties in the Pittsburgh seam caved zone and fractured zone of longwall mines in this mining district. Grid blanking was used to isolate the volume over the actual panels from the entire modeled district and to calculate gas amounts that were directly related to the emissions in longwall mines.Results indicated that gas-in-place in the Pittsburgh seam, in the caved zone and in the fractured zone, as well as displacements in major rock units, showed spatial correlations that could be modeled and estimated using geostatistical methods. This study showed that GIP volumes may

  10. Application of geostatistical inversion to thin reservoir prediction%地质统计学反演技术在薄储层预测中的应用

    Institute of Scientific and Technical Information of China (English)

    王香文; 刘红; 滕彬彬; 王连雨

    2012-01-01

    Taking Ml thin reservoir in H-N oilfield,Southern Ecuador,as an example,this paper documents the challenges and problems of thin reservoir prediction and presents relevant techniques and methods to tackle these problems. Based on analysis of geophysical characteristics of reservoirs and surrounding rocks,a geostatistical inversion technique is applied in this case to identify the thin(l -25ft) reservoirs with rapid lateral changes and strong concealment. Sand distribution is refined through correlation between different data volume including seismic interpretation, CSSI( Constrained Sparse Spike Inversion) and geostatistical inversion,and is further checked by non-well, random-wells and newly drilled wells. The accuracy of thin reservoir prediction is greatly enhanced to a vertical resolution up to 5ft. This technique is successfully applied in H-N oilfield and the new drilling data show that all the prediceted thin sand layers are encountered and the drilling coincidence rate is 82%.%以厄瓜多尔南部H-N油田M1层薄储层为例,阐述了研究区M1层储层预测难点和存在问题,提出针对性的储层预测方法技术.经过储层和围岩地球物理特征分析,论证了储层预测条件,确定了运用以地质统计学反演为核心的储层预测技术对该区进行储层预测研究,来解决该区储层薄(1 ~25 ft)、横向变化大、隐蔽性强的薄储层的识别;通过以地震、稀疏脉冲反演、地质统计学反演不同数据体间砂体进行对比分析,精细解释出该区砂体分布;经过无井、盲井和新钻井校验,实现了薄层的高精度预测,提高了预测精度(垂向分辨率达到5ft).该预测结果经过在H-N油田的实际应用和新钻井钻探证实,砂层钻遇率为100%,钻探符合率达82%,实现了该区新井产能的突破.

  11. Spatial assessment of soil organic carbon and physicochemical properties in a horticultural orchard at arid zone of India using geostatistical approaches.

    Science.gov (United States)

    Singh, Akath; Santra, Priyabrata; Kumar, Mahesh; Panwar, Navraten; Meghwal, P R

    2016-09-01

    Soil organic carbon (SOC) is a major indicator of long-term sustenance of agricultural production system. Apart from sustaining productivity, SOC plays a crucial role in context of climate change. Keeping in mind these potentials, spatial variation of SOC contents of a fruit orchard comprising several arid fruit plantations located at arid region of India is assessed in this study through geostatistical approaches. For this purpose, surface and subsurface soil samples from 175 locations from a fruit orchard spreading over 14.33 ha area were collected along with geographical coordinates. SOC content and soil physicochemical properties of collected soil samples were determined followed by geostatistical analysis for mapping purposes. Average SOC stock density of the orchard was 14.48 Mg ha(-1) for 0- to 30-cm soil layer ranging from 9.01 Mg ha(-1) in Carissa carandas to 19.52 Mg ha(-1) in Prosopis cineraria block. Range of spatial variation of SOC content was found about 100 m, while two other soil physicochemical properties, e.g., pH and electrical conductivity (EC) also showed similar spatial trend. This indicated that minimum sampling distance for future SOC mapping programme may be kept lower than 100 m for better accuracy. Ordinary kriging technique satisfactorily predicted SOC contents (in percent) at unsampled locations with root-mean-squared residual (RMSR) of 0.35-0.37. Co-kriging approach was found slightly superior (RMSR = 0.26-0.28) than ordinary kriging for spatial prediction of SOC contents because of significant correlations of SOC contents with pH and EC. Uncertainty of SOC estimation was also presented in terms of 90 % confidence interval. Spatial estimates of SOC stock through ordinary kriging or co-kriging approach were also found with low uncertainty of estimation than non-spatial estimates, e.g., arithmetic averaging approach. Among different fruit block plantations of the orchard, the block with Prosopis cineraria ('khejri') has

  12. Geostatistical analysis of disease data: visualization and propagation of spatial uncertainty in cancer mortality risk using Poisson kriging and p-field simulation

    Directory of Open Access Journals (Sweden)

    Goovaerts Pierre

    2006-02-01

    Full Text Available Abstract Background Smoothing methods have been developed to improve the reliability of risk cancer estimates from sparsely populated geographical entities. Filtering local details of the spatial variation of the risk leads however to the detection of larger clusters of low or high cancer risk while most spatial outliers are filtered out. Static maps of risk estimates and the associated prediction variance also fail to depict the uncertainty attached to the spatial distribution of risk values and does not allow its propagation through local cluster analysis. This paper presents a geostatistical methodology to generate multiple realizations of the spatial distribution of risk values. These maps are then fed into spatial operators, such as in local cluster analysis, allowing one to assess how risk spatial uncertainty translates into uncertainty about the location of spatial clusters and outliers. This novel approach is applied to age-adjusted breast and pancreatic cancer mortality rates recorded for white females in 295 US counties of the Northeast (1970–1994. A public-domain executable with example datasets is provided. Results Geostatistical simulation generates risk maps that are more variable than the smooth risk map estimated by Poisson kriging and reproduce better the spatial pattern captured by the risk semivariogram model. Local cluster analysis of the set of simulated risk maps leads to a clear visualization of the lower reliability of the classification obtained for pancreatic cancer versus breast cancer: only a few counties in the large cluster of low risk detected in West Virginia and Southern Pennsylvania are significant over 90% of all simulations. On the other hand, the cluster of high breast cancer mortality in Niagara county, detected after application of Poisson kriging, appears on 60% of simulated risk maps. Sensitivity analysis shows that 500 realizations are needed to achieve a stable classification for pancreatic cancer

  13. Distribuição espacial de Huanglongbing (Greening em citros utilizando a geoestatística Spatial distribuition of Huanglongbing (Greening on citrus using geostatistic

    Directory of Open Access Journals (Sweden)

    Renata Moreira Leal

    2010-09-01

    Full Text Available Os objetivos do trabalho foram avaliar a distribuição espacial e a expansão da Huanglongbing (greening em talhões de citros de uma propriedade agrícola localizada no município de Araraquara-SP, utilizando a geoestatística. Para determinar o número de plantas com greening, foram realizadas inspeções periódicas em intervalos de três meses, no período de março de 2005 a julho de 2007, contando-se, em cada talhão, o número de plantas com os sintomas característicos da doença. Realizou-se a análise descritiva dos dados e, para verificar a distribuição espacial do greening, utilizou-se a geoestatística através do ajuste de semivariogramas e da interpolação dos dados por krigagem. A dependência espacial de plantas com greening apresentou raio de agregação de 300 a 560 m, indicando distribuição agregada da doença. Por meio dos mapas de krigagem, observou-se que o foco inicial de plantas doentes ocorreu nos limites da fazenda, com expansão do greening por toda a área. O intervalo de inspeção de três meses não foi adequado para a redução do greening na fazenda.The aim of this study was to use geostatistics to verify the spatial distribution of Huanglongbing (greening in oranges orchards on agricultural property located in the city of Araraquara, São Paulo. To determine the number of plants with greening, periodic inspections the three months were made from March 2005 until July 2007, counting the number of plants in each stand with the characteristic symptoms of the disease. A descriptive analysis of the data was undertaken, and geostatistics were used to verify the spatial distribution of greening through the adjustment of semivariograms and interpolation of data by kriging. The spatial dependence of plants with greening formed a beam of aggregation of 300 to 560 m, indicated an aggregated distribution of disease. Diagrams of kriging showed that initial focus of plants with greening started at the border in the

  14. Modern space/time geostatistics using river distances: data integration of turbidity and E. coli measurements to assess fecal contamination along the Raritan River in New Jersey.

    Science.gov (United States)

    Money, Eric S; Carter, Gail P; Serre, Marc L

    2009-05-15

    Escherichia coli (E. coli) is a widely used indicator of fecal contamination in water bodies. External contact and subsequent ingestion of bacteria coming from fecal contamination can lead to harmful health effects. Since E. coli data are sometimes limited, the objective of this study is to use secondary information in the form of turbidity to improve the assessment of E. coli at unmonitored locations. We obtained all E. coli and turbidity monitoring data available from existing monitoring networks for the 2000-2006 time period for the Raritan River Basin, New Jersey. Using collocated measurements, we developed a predictive model of E. coli from turbidity data. Using this model, soft data are constructed for E. coli given turbidity measurements at 739 space/time locations where only turbidity was measured. Finally, the Bayesian Maximum Entropy (BME) method of modern space/time geostatistics was used for the data integration of monitored and predicted E. coli data to produce maps showing E. coli concentration estimated daily across the river basin. The addition of soft data in conjunction with the use of river distances reduced estimation error by about 30%. Furthermore, based on these maps, up to 35% of river miles in the Raritan Basin had a probability of E coli impairment greater than 90% on the most polluted day of the study period.

  15. Spatial variability of nutrients (N, P) in a deep, temperate lake with a low trophic level supported by global navigation satellite systems, geographic information system and geostatistics.

    Science.gov (United States)

    Łopata, Michał; Popielarczyk, Dariusz; Templin, Tomasz; Dunalska, Julita; Wiśniewski, Grzegorz; Bigaj, Izabela; Szymański, Daniel

    2014-01-01

    We investigated changes in the spatial distribution of phosphorus (P) and nitrogen (N) in the deep, mesotrophic Lake Hańcza. The raw data collection, supported by global navigation satellite system (GNSS) positioning, was conducted on 79 sampling points. A geostatistical method (kriging) was applied in spatial interpolation. Despite the relatively small area of the lake (3.04 km(2)), compact shape (shore development index of 2.04) and low horizontal exchange of water (retention time 11.4 years), chemical gradients in the surface waters were found. The largest variation concerns the main biogenic element - phosphorus. The average value was 0.032 at the extreme values of 0.019 to 0.265 mg L(-1) (coefficient of variation 87%). Smaller differences are related to nitrogen compounds (0.452-1.424 mg L(-1) with an average value of 0.583 mg L(-1), the coefficient of variation 20%). The parts of the lake which are fed with tributaries are the richest in phosphorus. The water quality of the oligo-mesotrophic Lake Hańcza has been deteriorating in recent years. Our results indicate that inferences about trends in the evolution of examined lake trophic status should be based on an analysis of the data, taking into account the local variation in water chemistry.

  16. Near-Infrared Spectroscopy and Geostatistical Analysis for Modeling Spatial Distribution of Analytical Constituents in Bulk Animal By-Product Protein Meals.

    Science.gov (United States)

    Adame-Siles, José A; Fearn, Tom; Guerrero-Ginel, José E; Garrido-Varo, Ana; Maroto-Molina, Francisco; Pérez-Marín, Dolores

    2017-03-01

    Control and inspection operations within the context of safety and quality assessment of bulk foods and feeds are not only of particular importance, they are also demanding challenges, given the complexity of food/feed production systems and the variability of product properties. Existing methodologies have a variety of limitations, such as high costs of implementation per sample or shortcomings in early detection of potential threats for human/animal health or quality deviations. Therefore, new proposals are required for the analysis of raw materials in situ in a more efficient and cost-effective manner. For this purpose, a pilot laboratory study was performed on a set of bulk lots of animal by-product protein meals to introduce and test an approach based on near-infrared (NIR) spectroscopy and geostatistical analysis. Spectral data, provided by a fiber optic probe connected to a Fourier transform (FT) NIR spectrometer, were used to predict moisture and crude protein content at each sampling point. Variographic analysis was carried out for spatial structure characterization, while ordinary Kriging achieved continuous maps for those parameters. The results indicated that the methodology could be a first approximation to an approach that, properly complemented with the Theory of Sampling and supported by experimental validation in real-life conditions, would enhance efficiency and the decision-making process regarding safety and adulteration issues.

  17. A novel geotechnical/geostatistical approach for exploration and production of natural gas from multiple geologic strata, Phase 1. Volume 1, Overview

    Energy Technology Data Exchange (ETDEWEB)

    Overbey, W.K. Jr.; Reeves, T.K.; Salamy, S.P.; Locke, C.D.; Johnson, H.R.; Brunk, R.; Hawkins, L. [BDM Engineering Services Co., Morgantown, WV (United States)

    1991-05-01

    This research program has been designed to develop and verify a unique geostatistical approach for finding natural gas resources. The research has been conducted by Beckley College, Inc. (Beckley) and BDM Engineering Services Company (BDMESC) under contract to the US Department of Energy (DOE), Morgantown Energy Technology Center. Phase 1 of the project consisted of compiling and analyzing relevant geological and gas production information in selected areas of Raleigh County, West Virginia, ultimately narrowed to the Eccles, West Virginia, 7 {1/2} minute Quadrangle. The Phase 1 analysis identified key parameters contributing to the accumulation and production of natural gas in Raleigh County, developed analog models relating geological factors to gas production, and identified specific sites to test and verify the analysis methodologies by drilling. Based on the Phase 1 analysis, five sites have been identified with high potential for economic gas production. Phase 2 will consist of drilling, completing, and producing one or more wells at the sites identified in the Phase 1 analyses. The initial well is schedules to the drilled in April 1991. This report summarizes the results of the Phase 1 investigations. For clarity, the report has been prepared in two volumes. Volume 1 presents the Phase 1 overview; Volume 2 contains the detailed geological and production information collected and analyzed for this study.

  18. Application of the ESRI Geostatistical Analyst for Determining the Adequacy and Sample Size Requirements of Ozone Distribution Models in the Carpathian and Sierra Nevada Mountains

    Directory of Open Access Journals (Sweden)

    Witold Fraczek

    2001-01-01

    Full Text Available Models of O3 distribution in two mountain ranges, the Carpathians in Central Europe and the Sierra Nevada in California were constructed using ArcGIS Geostatistical Analyst extension (ESRI, Redlands, CA using kriging and cokriging methods. The adequacy of the spatially interpolated ozone (O3 concentrations and sample size requirements for ozone passive samplers was also examined. In case of the Carpathian Mountains, only a general surface of O3 distribution could be obtained, partially due to a weak correlation between O3 concentration and elevation, and partially due to small numbers of unevenly distributed sample sites. In the Sierra Nevada Mountains, the O3 monitoring network was much denser and more evenly distributed, and additional climatologic information was available. As a result the estimated surfaces were more precise and reliable than those created for the Carpathians. The final maps of O3 concentrations for Sierra Nevada were derived from cokriging algorithm based on two secondary variables — elevation and maximum temperature as well as the determined geographic trend. Evenly distributed and sufficient numbers of sample points are a key factor for model accuracy and reliability.

  19. Application of the ESRI Geostatistical Analyst for determining the adequacy and sample size requirements of ozone distribution models in the Carpathian and Sierra Nevada Mountains.

    Science.gov (United States)

    Fraczek, W; Bytnerowicz, A; Arbaugh, M J

    2001-12-07

    Models of O3 distribution in two mountain ranges, the Carpathians in Central Europe and the Sierra Nevada in California were constructed using ArcGIS Geostatistical Analyst extension (ESRI, Redlands, CA) using kriging and cokriging methods. The adequacy of the spatially interpolated ozone (O3) concentrations and sample size requirements for ozone passive samplers was also examined. In case of the Carpathian Mountains, only a general surface of O3 distribution could be obtained, partially due to a weak correlation between O3 concentration and elevation, and partially due to small numbers of unevenly distributed sample sites. In the Sierra Nevada Mountains, the O3 monitoring network was much denser and more evenly distributed, and additional climatologic information was available. As a result the estimated surfaces were more precise and reliable than those created for the Carpathians. The final maps of O3 concentrations for Sierra Nevada were derived from cokriging algorithm based on two secondary variables--elevation and maximum temperature as well as the determined geographic trend. Evenly distributed and sufficient numbers of sample points are a key factor for model accuracy and reliability.

  20. Description and validation of a new set of object-based temporal geostatistical features for land-use/land-cover change detection

    Science.gov (United States)

    Gil-Yepes, Jose L.; Ruiz, Luis A.; Recio, Jorge A.; Balaguer-Beser, Ángel; Hermosilla, Txomin

    2016-11-01

    A new set of temporal features derived from codispersion and cross-semivariogram geostatistical functions is proposed, described, extracted, and evaluated for object-based land-use/land-cover change detection using high resolution images. Five features were extracted from the codispersion function and another six from the cross-semivariogram. The set of features describes the temporal behaviour of the internal structure of the image objects defined in a cadastral database. The set of extracted features was combined with spectral information and a feature selection study was performed using forward stepwise discriminant analysis, principal component analysis, as well as correlation and feature interpretation analysis. The temporal feature set was validated using high resolution aerial images from an agricultural area located in south-east Spain, in order to solve a tree crop change detection problem. Direct classification using decision tree classifier was used as change detection method. Different classifications were performed comparing various feature group combinations in order to obtain the most suitable features for this study. Results showed that the new sets of cross-semivariogram and codispersion features provided high global accuracy classification results (83.55% and 85.71% respectively), showing high potential for detecting changes related to the internal structure of agricultural tree crop parcels. A significant increase in accuracy value was obtained when combining features from both groups with spectral information (94.59%).

  1. MIDDLE MIOCENE DEPOSITIONAL MODEL IN THE DRAVA DEPRESSION DESCRIBED BY GEOSTATISTICAL POROSITY AND THICKNESS MAPS (CASE STUDY: STARI GRADAC-BARCS NYUGAT FIELD

    Directory of Open Access Journals (Sweden)

    Tomislav Malvić

    2006-12-01

    Full Text Available Neogene depositional environments in the Drava depression can be classified in two groups. One group is of local alluvial fans, which were active during the period of Middle Miocene (Badenian extension through the entire Pannonian Basin. The second group is represented by continuous Pannonian and Pontian sedimentation starting with lacustrine environment of partly deep water and partly prodelta (turbidity fans and terminating at the delta plain sedimentation. The coarse-grained sediments of alluvial fans have the great hydrocarbon potential, because they often comprise reservoir rocks. Reservoir deposits are mostly overlain (as result of fan migration by pelitic seal deposits and sometimes including organic rich source facies. That Badenian sequences are often characterised by complete petroleum systems, what is confirmed by large number of oil and gas discoveries in such sediments in the Drava and other Croatian depressions. Alluvial environments are characterised by frequent changes of petrophysical properties, due to local character of depositional mechanism and material sources. In the presented paper, Stari Gradac-Barcs Nyugat field is selected as a case study for demonstrating the above mentioned heterogenic features of the Badenian sequences. Structural solutions are compared by maps of parameters related to depositional environment, i.e. porosity and thickness maps. Geostatistics were used for spatial extension of input dataset. The spatial variability of porosity values, i.e. reservoir quality, is interpreted by transition among different sub-environments (facies in the alluvial fan system.

  2. Time-lapse analysis of methane quantity in Mary Lee group of coal seams using filter-based multiple-point geostatistical simulation

    Science.gov (United States)

    Karacan, C. Özgen; Olea, Ricardo A.

    2013-01-01

    Coal seam degasification and its success are important for controlling methane, and thus for the health and safety of coal miners. During the course of degasification, properties of coal seams change. Thus, the changes in coal reservoir conditions and in-place gas content as well as methane emission potential into mines should be evaluated by examining time-dependent changes and the presence of major heterogeneities and geological discontinuities in the field. In this work, time-lapsed reservoir and fluid storage properties of the New Castle coal seam, Mary Lee/Blue Creek seam, and Jagger seam of Black Warrior Basin, Alabama, were determined from gas and water production history matching and production forecasting of vertical degasification wellbores. These properties were combined with isotherm and other important data to compute gas-in-place (GIP) and its change with time at borehole locations. Time-lapsed training images (TIs) of GIP and GIP difference corresponding to each coal and date were generated by using these point-wise data and Voronoi decomposition on the TI grid, which included faults as discontinuities for expansion of Voronoi regions. Filter-based multiple-point geostatistical simulations, which were preferred in this study due to anisotropies and discontinuities in the area, were used to predict time-lapsed GIP distributions within the study area. Performed simulations were used for mapping spatial time-lapsed methane quantities as well as their uncertainties within the study area.

  3. Geostatistics for Spatial Uncertainty Characterization%地统计学用于空间不确定性的描述

    Institute of Scientific and Technical Information of China (English)

    张景雄; 张金平; 姚娜

    2009-01-01

    Most geospatial phenomena can be interpreted probabilistically because we are ignorant of the biophysical proc-esses and mechanisms that have jointly created ancl observed events. This philosophy is important because we are certain about the phenomenon under study at sampled locations, except for measurement errors, but, in between the sampled, we become uncertain about how the phenomenon behaves. Geostatistical uncertainty characterization is to generate random numbers in such a way that they simulate the outcomes of the random processes that created the existing sample data. This set of existing sample is viewed as a partially sampled realization of that random function model. The random function's spa-tial variability is described by a variogram or covariance model. The realized surfaces need to honour sample data at their lo-cations, and reflect the spatial structure quantified by the variogram models. They should each reproduce the sample histo-gram representative of the whole sampling area. This paper will review the fundamentals in stochastic simulation by covering univariate and indicator techniques in the hope that their applications in geospatial information science will be wide-spread and fruitful.

  4. Spatial distribution of electrical conductivity and stable isotopes in groundwater in large catchments: a geostatistical approach in the Quequén Grande River catchment, Argentina.

    Science.gov (United States)

    Quiroz Londoño, Orlando Mauricio; Martínez, Daniel Emilio; Massone, Hector Enrique; Londoño Ciro, Libardo Antonio; Dapeña, Cristina

    2015-01-01

    Stable isotopes and electrical conductivity in groundwater were used as natural tracers to adjust the hydrogeological conceptual model in one of the largest catchments within the inter-mountainous Pampa plain, Argentina. Geostatistical tools were used to define the model that best fitted the spatial distribution of each tracer, and information was obtained in areas where there was a lack of data. The conventional isotopic analysis allowed the identification of three groundwater groups with different isotopic fingerprints. One group containing 56% of the total groundwater samples suggested a well-mixed system and soil infiltration precipitation as the main recharge source to the aquifer. The other two groups included samples with depleted (25.5%) and enriched (18.5%) isotopic compositions, respectively. The combination of δ(18)O, δ(2)H and electrical conductivities maps suggested ascending regional flows and water transfer from the Quequén Grande River catchment to the Moro creek. The spatial interpretation of these tracers modified the conceptual hydrogeological model of the Quequén Grande River.

  5. Use of geostatistics for assessing the concentration of heavy metals in a stretch of the River Apodi-Mossoro (Rio Grande do Norte State, Brazil).

    Science.gov (United States)

    Bezerra, J. M.; Siqueira, G. M.; Montenegro, A. A. A.; Silva, P. C. M.; Batista, R. O.

    2012-04-01

    The objective of this study was to assess the environmental changes with respect to the concentration of heavy metals in the sediment contained a stretch of the River Apodi-Mossoró (Rio Grande do Norte State, Brazil), considering changes in land use and soil. The sediment samples were collected at 30 points in the bed Apodi- Mossoró River in a section with features urban-rural town of Mossoró. The concentration of heavy metals in the sediment was determined using composite samples of surface sediments from the bottom with a depth of 20 cm, according to the methodology of APHAAWWA-WPCF (1998), where he subsequently held to determine the presence and quantity of metal concentration total by the technique of atomic absorption spectrometry, and analyzed the following heavy metals: aluminum(Al), cádmium (Cd), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb) and zinc (Zn). Data were analyzed using statistical and geostatistical. The geostatistical analysiswas performed by the construction of experimental semivariogramas self-assessment and adjustment by using the technique of Jack-kinifing. The elemento Cd was absent in the samples, which reduces the possibility of environmental contamination events. The average concentrations of the elements under study are within the limits proposed by the environmental legislation (National Environmental Council). However, for the elements Fe, Al and Mn no threshold values, because these are associated with the rocky material of geochemical origin. The elemento Fe had the highest range of values than the other, and all elements except for Zn and Cd showed the presence of outliers, suggesting the possibility that these points are listed as points liable to contribution by human activities. It was verified the presence of human influence, because the elements undergo an increase of concentration values from the point 11, which is located downstream of the urban bus consolidated. The experimental

  6. Geostatistical and stratigraphic analysis of deltaic reservoirs from the Reconcavo Basin, Brazil; Analise estratigrafica e geoestatistica de reservatorios deltaicos da Bacia do Reconcavo (BA)

    Energy Technology Data Exchange (ETDEWEB)

    Soares, Carlos Moreira

    1997-07-01

    This study presents the characterization of the external geometry of deltaic oil reservoirs, including the description of their areal distribution using geo statistic tools, such as variography and kriging. A high-resolution stratigraphic study was developed over a 25 km{sup 2} area, by using data from 276 closely-spaced wells of an oil-producer field from the Reconcavo Basin, northeastern Brazil. The studied succession records the progressive lacustrine transgression of a deltaic environment. Core data and stratigraphic cross sections suggest that the oil reservoirs are mostly amalgamated, delta-front lobes, and subordinately, crevasse deposits. Some important geometrical elements were recognized by the detailed variographic analysis developed for each stratigraphic unit (zone). The average width for the groups of deltaic lobes of one zone was measured from the variographic feature informally named as hole effect. This procedure was not possible for the other zones due to the intense lateral amalgamation of sandstones, indicated by many variographic nested structures. Net sand krigged maps for the main zones suggest a NNW-SSE orientation for the deltaic lobes, as also their common amalgamation and compensation arrangements. High-resolution stratigraphic analyses should include a more regional characterization of the depositional system that comprises the studied succession. On the other hand, geostatistical studies should be developed only after the recognition of the depositional processes acting in the study area and the geological meaning of the variable to be treated, including its spatial variability scales as a function of sand body thickness, orientation and amalgamation. (author)

  7. Groundwater-Quality Impacts from Natural-Gas Wellbore Leakage: Numerical Sensitivity Analysis of Hydrogeologic, Geostatistical, and Source-Term Parameterization at Varying Depths

    Science.gov (United States)

    Rice, A. K.; McCray, J. E.; Singha, K.

    2016-12-01

    The development of directional drilling and stimulation of reservoirs by hydraulic fracturing has transformed the energy landscape in the U.S. by making recovery of hydrocarbons from shale formations not only possible but economically viable. Activities associated with hydraulic fracturing present a set of water-quality challenges, including the potential for impaired groundwater quality. In this project, we use a three-dimensional, multiphase, multicomponent numerical model to investigate hydrogeologic conditions that could lead to groundwater contamination from natural gas wellbore leakage. This work explores the fate of methane that enters a well annulus, possibly from an intermediate formation or from the production zone via a flawed cement seal, and leaves the annulus at one of two depths: at the elevation of groundwater or below a freshwater aquifer. The latter leakage scenario is largely ignored in the current scientific literature, where focus has been on leakage directly into freshwater aquifers, despite modern regulations requiring steel casings and cement sheaths at these depths. We perform a three-stage sensitivity analysis, examining (1) hydrogeologic parameters of media surrounding a methane leakage source zone, (2) geostatistical variations in intrinsic permeability, and (3) methane source zone pressurization. Results indicate that in all cases methane reaches groundwater within the first year of leakage. To our knowledge, this is the first study to consider natural gas wellbore leakage in the context of multiphase flow through heterogeneous permeable media; advantages of multiphase modeling include more realistic analysis of methane vapor-phase relative permeability as compared to single-phase models. These results can be used to inform assessment of aquifer vulnerability to hydrocarbon wellbore leakage at varying depths.

  8. Agricultura de precisão: mapeamento da produtividade em pomares cítricos usando geoestatística Precision agriculture: mapping yield in citrus groves using geostatistics

    Directory of Open Access Journals (Sweden)

    Paulo Roberto Silva Farias

    2003-08-01

    Full Text Available A variabilidade espacial de produtividade e tamanho de frutos foi avaliada em pomares de laranja irrigados e não irrigados, localizados no município de Luiz Antônio - SP, utilizando-se de geoestatística. Através dos mapas de krigagem, podem-se determinar as áreas de alta e baixa produtividade dos talhões. Verificou-se maior variabilidade para produtividade e tamanho de frutos nas quadras irrigadas e não irrigadas. Portanto, a geoestatística mostrou-se uma ferramenta extremamente útil para auxiliar em Programas de Agricultura de Precisão.The yield variability of sweet orange groves located in Luiz Antonio country, Sao Paulo State, Brazil, was evaluated using geostatistics. Through the kriging maps, areas with higher and lower yields in the groves were determinated. A great variability of yield and fruit size on irrigated and non-irrigated groves was verified. Thus, the geostatistic showed to be an extremely useful tool to enhance Precision Agriculture Programs.

  9. Geostatistical characterization of soil pollution at industrial sites Case of polycyclic aromatic hydrocarbons at former coking plants; Caracterisation geostatistique de pollutions industrielles de sols cas des hydrocarbures aromatiques polycycliques sur d'anciens sites de cokeries

    Energy Technology Data Exchange (ETDEWEB)

    Jeannee, N.

    2001-05-15

    Estimating polycyclic aromatic hydrocarbons concentrations in soil at former industrial sites poses several practical problems on account of the properties of the contaminants and the history of site: 1)collection and preparation of samples from highly heterogeneous material, 2) high short scale variability, particularly in presence of backfill, 3) highly contrasted grades making the vario-gram inference complicated. The sampling strategy generally adopted for contaminated sites is based on the historical information. Systematic sampling recommended for geostatistical estimation is often considered to be excessive and unnecessary. Two former coking plants are used as test cases for comparing several geostatistical methods for estimating (i) in situ concentrations and (ii) the probability that they are above a pollution threshold. Several practical and methodological questions are considered: 1) the properties of various estimators of the experimental vario-gram and the validity of the results; 2) the use of soft data, such as historical information, organoleptic observations and semi-quantitative methods, with a view to improve the precision of the estimates; 3) the comparison of standard sampling strategies, taking into account vertical repartition of grades and the history of the site. Multiple analyses of the same sample give an approximation of the sampling error. Short scale sampling shows the difficulty of selecting soils in the absence of a spatial structure. Sensitivity studies are carried out to assess how densely sampled soft data can improve estimates. By using mainly existing models, this work aims at giving practical recommendations for the characterization of soil pollution. (author)

  10. Geostatistical approach for management of soil nutrients with special emphasis on different forms of potassium considering their spatial variation in intensive cropping system of West Bengal, India.

    Science.gov (United States)

    Chatterjee, Sourov; Santra, Priyabrata; Majumdar, Kaushik; Ghosh, Debjani; Das, Indranil; Sanyal, S K

    2015-04-01

    A large part of precision agriculture research in the developing countries is devoted towards precision nutrient management aspects. This has led to better economics and efficiency of nutrient use with off-farm advantages of environmental security. The keystone of precision nutrient management is analysis and interpretation of spatial variability of soils by establishing management zones. In this study, spatial variability of major soil nutrient contents was evaluated in the Ghoragacha village of North 24 Parganas district of West Bengal, India. Surface soil samples from 100 locations, covering different cropping systems of the village, was collected from 0 to 15 cm depth using 100×100 m grid system and analyzed in the laboratory to determine organic carbon (OC), available nitrogen (N), phosphorus (P), and potassium (K) contents of the soil as well as its water-soluble K (KWS), exchangeable K (KEX), and non-exchangeable forms of K (KNEX). Geostatistical analyses were performed to determine the spatial variation structure of each nutrient content within the village, followed by the generation of surface maps through kriging. Four commonly used semivariogram models, i.e., spherical, exponential, Gaussian, and linear models were fitted to each soil property, and the best one was used to prepare surface maps through krigging. Spherical model was found the best for available N and P contents, while linear and exponential model was the best for OC and available K, and for KWS and KNEK, Gausian model was the best. Surface maps of nutrient contents showed that N content (129-195 kg ha(-1)) was the most limiting factor throughout the village, while P status was generally very high ( 10-678 kg ha(-1)) in the soils of the present village. Among the different soil K fractions, KWS registered the maximum variability (CV 75%), while the remaining soil K fractions showed moderate to high variation. Interestingly, KNEX content also showed high variability, which essentially

  11. A geostatistical approach to recover the release history of groundwater pollution events; L'approccio geostatistico per la ricostruzione della storia di rilascio di inquinanti in falda

    Energy Technology Data Exchange (ETDEWEB)

    Butera, I.; Tanda, M. G. [Milan Politecnico, Milan (Italy). Dipt. di Ingegneria Idraulica, Ambientale e del Rilevamento

    2001-08-01

    In this work, on the basis of the spatial concentration data available at a given time, the temporal release history of a pollutant is recovered by a geostatistical methodology. The problem in hand belongs to the inverse problems category: in literature different approaches are proposed for their solution. The methodology adopted in this study has been developed by Snodgras and Kitanidis (1997) for one dimensional flow and transport case. In this work the methodology is developed to the case of two dimensional transport (one dimensional transport assumption implies not negligible approximations, even if transversal dispersivity is small compared to the longitudinal one). The study, applied to a literature case, considers the quality of the results and the performance of the algorithm used to implement the procedure with regard of: plume sampling scheme (location and number of the measurement points); the impact of concentration measurement errors; the impact of errors in the aquifer parameters estimate (velocity, longitudinal and transversal dispersion coefficients); erroneous identification of the hydraulic gradient direction. The results of the numerical analysis show that the method provides a likely description of the release history jointed to the estimate error variance. [Italian] Nel presente lavoro si propone un'applicazione di un metodo sviluppato nell'ambito della geostatistica, per la ricostruzione della storia temporale dei rilasci di un agente presente in falda, sulla base dei dati di concentrazione d'inquinante rilevati in diversi punti dell'acquifero. Il problema in esame rientra nella categoria dei problemi inversi, per la cui soluzione in letteratura sono prooposti metodi di impostazione diversa. La metodologia adottata in questo studio e' stata sviluppata ed applicata da Snodgrass e Kitanidis (1997) per condizioni di flusso e trasporto monodimensionali; nella presente memoria, essa e' estesa a non trascurabili, anche

  12. What have we learned from deterministic geostatistics at highly resolved field sites, as relevant to mass transport processes in sedimentary aquifers?

    Science.gov (United States)

    Ritzi, Robert W.; Soltanian, Mohamad Reza

    2015-12-01

    In the method of deterministic geostatistics (sensu Isaaks and Srivastava, 1988), highly-resolved data sets are used to compute sample spatial-bivariate statistics within a deterministic framework. The general goal is to observe what real, highly resolved, sample spatial-bivariate correlation looks like when it is well-quantified in naturally-occurring sedimentary aquifers. Furthermore, it is to understand how this correlation structure, (i.e. shape and correlation range) is related to independent and physically quantifiable attributes of the sedimentary architecture. The approach has evolved among work by Rubin (1995, 2003), Barrash and Clemo (2002), Ritzi et al. (2004, 2007, 2013), Dai et al. (2005), and Ramanathan et al. (2010). In this evolution, equations for sample statistics have been developed which allow tracking the facies types at the heads and tails of lag vectors. The goal is to observe and thereby understand how aspects of the sedimentary architecture affect the well-supported sample statistics. The approach has been used to study heterogeneity at a number of sites, representing a variety of depositional environments, with highly resolved data sets. What have we learned? We offer and support an opinion that the single most important insight derived from these studies is that the structure of spatial-bivariate correlation is essentially the cross-transition probability structure, determined by the sedimentary architecture. More than one scale of hierarchical sedimentary architecture has been represented in these studies, and a hierarchy of cross-transition probability structures was found to define the correlation structure in all cases. This insight allows decomposing contributions from different scales of the sedimentary architecture, and has led to a more fundamental understanding of mass transport processes including mechanical dispersion of solutes within aquifers, and the time-dependent retardation of reactive solutes. These processes can now be

  13. Inference of strata separation and gas emission paths in longwall overburden using continuous wavelet transform of well logs and geostatistical simulation

    Science.gov (United States)

    Karacan, C. Özgen; Olea, Ricardo A.

    2014-06-01

    Prediction of potential methane emission pathways from various sources into active mine workings or sealed gobs from longwall overburden is important for controlling methane and for improving mining safety. The aim of this paper is to infer strata separation intervals and thus gas emission pathways from standard well log data. The proposed technique was applied to well logs acquired through the Mary Lee/Blue Creek coal seam of the Upper Pottsville Formation in the Black Warrior Basin, Alabama, using well logs from a series of boreholes aligned along a nearly linear profile. For this purpose, continuous wavelet transform (CWT) of digitized gamma well logs was performed by using Mexican hat and Morlet, as the mother wavelets, to identify potential discontinuities in the signal. Pointwise Hölder exponents (PHE) of gamma logs were also computed using the generalized quadratic variations (GQV) method to identify the location and strength of singularities of well log signals as a complementary analysis. PHEs and wavelet coefficients were analyzed to find the locations of singularities along the logs. Using the well logs in this study, locations of predicted singularities were used as indicators in single normal equation simulation (SNESIM) to generate equi-probable realizations of potential strata separation intervals. Horizontal and vertical variograms of realizations were then analyzed and compared with those of indicator data and training image (TI) data using the Kruskal-Wallis test. A sum of squared differences was employed to select the most probable realization representing the locations of potential strata separations and methane flow paths. Results indicated that singularities located in well log signals reliably correlated with strata transitions or discontinuities within the strata. Geostatistical simulation of these discontinuities provided information about the location and extents of the continuous channels that may form during mining. If there is a gas

  14. Geostatistical analysis of disease data: accounting for spatial support and population density in the isopleth mapping of cancer mortality risk using area-to-point Poisson kriging

    Directory of Open Access Journals (Sweden)

    Goovaerts Pierre

    2006-11-01

    Full Text Available Abstract Background Geostatistical techniques that account for spatially varying population sizes and spatial patterns in the filtering of choropleth maps of cancer mortality were recently developed. Their implementation was facilitated by the initial assumption that all geographical units are the same size and shape, which allowed the use of geographic centroids in semivariogram estimation and kriging. Another implicit assumption was that the population at risk is uniformly distributed within each unit. This paper presents a generalization of Poisson kriging whereby the size and shape of administrative units, as well as the population density, is incorporated into the filtering of noisy mortality rates and the creation of isopleth risk maps. An innovative procedure to infer the point-support semivariogram of the risk from aggregated rates (i.e. areal data is also proposed. Results The novel methodology is applied to age-adjusted lung and cervix cancer mortality rates recorded for white females in two contrasted county geographies: 1 state of Indiana that consists of 92 counties of fairly similar size and shape, and 2 four states in the Western US (Arizona, California, Nevada and Utah forming a set of 118 counties that are vastly different geographical units. Area-to-point (ATP Poisson kriging produces risk surfaces that are less smooth than the maps created by a naïve point kriging of empirical Bayesian smoothed rates. The coherence constraint of ATP kriging also ensures that the population-weighted average of risk estimates within each geographical unit equals the areal data for this unit. Simulation studies showed that the new approach yields more accurate predictions and confidence intervals than point kriging of areal data where all counties are simply collapsed into their respective polygon centroids. Its benefit over point kriging increases as the county geography becomes more heterogeneous. Conclusion A major limitation of choropleth

  15. Accounting for regional background and population size in the detection of spatial clusters and outliers using geostatistical filtering and spatial neutral models: the case of lung cancer in Long Island, New York

    Directory of Open Access Journals (Sweden)

    Goovaerts Pierre

    2004-07-01

    Full Text Available Abstract Background Complete Spatial Randomness (CSR is the null hypothesis employed by many statistical tests for spatial pattern, such as local cluster or boundary analysis. CSR is however not a relevant null hypothesis for highly complex and organized systems such as those encountered in the environmental and health sciences in which underlying spatial pattern is present. This paper presents a geostatistical approach to filter the noise caused by spatially varying population size and to generate spatially correlated neutral models that account for regional background obtained by geostatistical smoothing of observed mortality rates. These neutral models were used in conjunction with the local Moran statistics to identify spatial clusters and outliers in the geographical distribution of male and female lung cancer in Nassau, Queens, and Suffolk counties, New York, USA. Results We developed a typology of neutral models that progressively relaxes the assumptions of null hypotheses, allowing for the presence of spatial autocorrelation, non-uniform risk, and incorporation of spatially heterogeneous population sizes. Incorporation of spatial autocorrelation led to fewer significant ZIP codes than found in previous studies, confirming earlier claims that CSR can lead to over-identification of the number of significant spatial clusters or outliers. Accounting for population size through geostatistical filtering increased the size of clusters while removing most of the spatial outliers. Integration of regional background into the neutral models yielded substantially different spatial clusters and outliers, leading to the identification of ZIP codes where SMR values significantly depart from their regional background. Conclusion The approach presented in this paper enables researchers to assess geographic relationships using appropriate null hypotheses that account for the background variation extant in real-world systems. In particular, this new

  16. A Streamlined Approach by a Combination of Bioindication and Geostatistical Methods for Assessing Air Contaminants and Their Effects on Human Health in Industrialized Areas: A Case Study in Southern Brazil

    Directory of Open Access Journals (Sweden)

    Angélica B. Ferreira

    2017-09-01

    Full Text Available Industrialization in developing countries associated with urban growth results in a number of economic benefits, especially in small or medium-sized cities, but leads to a number of environmental and public health consequences. This problem is further aggravated when adequate infrastructure is lacking to monitor the environmental impacts left by industries and refineries. In this study, a new protocol was designed combining biomonitoring and geostatistics to evaluate the possible effects of shale industry emissions on human health and wellbeing. Futhermore, the traditional and expensive air quality method based on PM2.5 measuring was also used to validate the low-cost geostatistical approach. Chemical analysis was performed using Energy Dispersive X-ray Fluorescence Spectrometer (EDXRF to measure inorganic elements in tree bark and shale retorted samples in São Mateus do Sul city, Southern Brazil. Fe, S, and Si were considered potential pollutants in the study area. Distribution maps of element concentrations were generated from the dataset and used to estimate the spatial behavior of Fe, S, and Si and the range from their hot spot(s, highlighting the regions sorrounding the shale refinery. This evidence was also demonstrated in the measurements of PM2.5 concentrations, which are in agreement with the information obtained from the biomonitoring and geostatistical model. Factor and descriptive analyses performed on the concentrations of tree bark contaminants suggest that Fe, S, and Si might be used as indicators of industrial emissions. The number of cases of respiratory diseases obtained from local basic health unit were used to assess a possible correlation between shale refinery emissions and cases of repiratory disease. These data are public and may be accessed on the website of the the Brazilian Ministry of Health. Significant associations were found between the health data and refinery activities. The combination of the spatial

  17. 3D geostatistic modelling of the acoustic impedance for the characterization of Namorado oil field, Brazil; Modelagem geoestatistica 3D da impedancia acustica para a caracterizacao do Campo de Namorado

    Energy Technology Data Exchange (ETDEWEB)

    Vidal, Alexandre Campane; Sancevero, Sergio Sacani; Remacre, Armando Zaupa; Costanzo, Caetano Pontes [UNICAMP, Instituto de Geociencias, Dept. de Geologia e Recursos Naturais, Campinas, SP (Brazil)], E-mails: vidal@ige.unicamp.br, sacani@ige.unicamp.br, geden@ige.unicamp.br, caetano.costanzo@gmail.com

    2007-07-15

    The aim of this work is analyze the vertical seismic resolution of the turbidity reservoir of Namorado Field. In this work the seismic modeling was accomplished using the convolution method. The wavelet used was the Ricker type with dominant frequency of 20 hz, 35 hz and 50 hz. The results show that wavelet with frequencies of 35 hz and 50 hz have better seismic resolution than wavelets of 20 hz, however all frequencies delimit top and base of the reservoir. From the acoustic impedance model, obtained from the synthetic seismogram, was possible, knowing the correlation of this variable with reservoir rocks, determine the distribution of reservoir facies. For that was used the geostatistical analysis that still enabled the studies regarding to the scenarios analysis by means of the application of stochastic methods. (author)

  18. 我国小麦条锈病菌既越冬又越夏地区的气候区划%Climate-based regional classification for oversummering and overwintering of Puccinia striiformis in China with GIS and geostatistics

    Institute of Scientific and Technical Information of China (English)

    马占鸿; 石守定; 王海光; 张美荣

    2005-01-01

    以具有大区流行特点的小麦条锈病为研究对象,在建立小麦条锈病相关因子地理信息数据库的基础上,首次采用地理信息系统(GIS)和地统计学(Geostatistics)方法,对我国小麦条锈病菌越夏区、越冬区进行了气候区划.明确了我国适合小麦条锈病菌越夏、越冬的范围,可为制定小麦条锈病综合治理方案提供依据,也可为条锈病菌越夏、越冬情况的进一步调查研究提供指导.

  19. Multi-Criteria GIS Methodology Focused on the Location of Optimal Places for Small Hydro Power Via Hydrological and Geostatistic Aplications; Metodologia SIG Multicriterio Enfocada a la Localizacion de Enclaves Optimos para Centrales de Minihidroelectricas mediante Aplicaciones Hidrologicas y Geoestadisticas

    Energy Technology Data Exchange (ETDEWEB)

    Paz, C. de la

    2013-02-01

    The main objective of this research is the development of a location methodology for sitting optimization of small hydro power (SHP) centrals. In order of achieve this goal, a Multi-Criteria Evaluation (MCE) methodology implemented through the use of tools in a GIS environment: Spatial Analysis, Geostatistic Analysis, and Hydrology have been developed. This methodology includes two different models based on the same MCE process. The substantial difference of both models is in the input data and the tools applied to estimate the energy resource and the principal factor of the methodology (caudal or accumulated flow). The first model is generated from caudal data obtained in the study area (El Bierzo), and the second one from pluviometric data and Digital Terrain Model (DTM). Both models include viability maps with greater ability areas to locate SHP facilities. As an additional objective, the study allows contrasting the results of the two developed models to evaluate their similarity. (Author)

  20. 地统计关联特征与多子集匹配的缺失指纹识别算法%Missing fingerprint identification based on linked features of geostatistics and subset matches

    Institute of Scientific and Technical Information of China (English)

    陈云志

    2014-01-01

    针对主流指纹识别算法对缺失指纹图像识别率非常低的问题,提出了一种地统计关联特征与多子集匹配的算法(GS-MS)。首先对指纹图像进行Gabor滤波增强以及二值化、细化预处理,然后将图像均匀划分为N个子集,分别提取各子集的地统计学关联特征与分叉点、端点等细节特征点,最后以待识别指纹图像子集为基准,与指纹库子集进行匹配识别。采用完整与缺失两种指纹数据集进行测试,GS-MS算法均取得了较优的识别精度,而且没有大幅度增加运行时间。%Most fingerprint identification algorithm has low accuracy for incomplete image, a novel miss fingerprint iden-tification based on linked features of geostatistics and subset matching is proposed in this paper. Image preprocessing is performed by Gabor filter, binary conversion and thinning. The image is partitioned into several sub-images without over-lapped image. The features of each sub-image are extracted such as the linked features of geostatistics, fingerprint minutiaes of fork point and endpoint. The performance of algorithm is test by simulation experiments. The result shows that the pro-posed method has achieved higher identification accuracy in missing images and has not increased consuming time.

  1. 因子分析与地统计学在化探数据分析中的应用%Application of factor analysis and geostatistics to geochemical data analysis

    Institute of Scientific and Technical Information of China (English)

    祁轶宏; 李晓晖; 霍立新

    2013-01-01

    Based on soil geochemical data of the Tongling mining camp,this paper obtained the information of a number of major factors from the geochemical data using factor analysis,and made spatial variation analysis for each major fac-tor and interpolation study using geostatistic method,indicating that each major factor acquired from factor analysis cor-responds to different ore-forming information,and combined use of factor analysis,geostatistic analysis and interpola-tion method can better display the spatial distribution trend of each major factor score and its correlation with known mineralization information,further serving metallogenic prognosis and mineral exploration.%  以铜陵矿集区土壤勘查地球化学数据为实例,应用因子分析方法获取了地球化学数据中的多个主因子信息,并利用地统计学方法开展了各主因子的空间变异分析和插值研究。研究结果显示,因子分析得到的各主因子对应于不同的成矿信息,将因子分析与地统计学分析和插值方法相结合,可以更好的展现各主因子得分的空间分布趋势以及与已知成矿信息的关联程度,进而服务于成矿预测和找矿勘探工作。

  2. 测井、井间地震与地面地震数据联合约束下的地质统计学随机建模方法研究%A new geostatistical inversion and reservoir modeling technique constrained by well-log, crosshole and surface seismic data

    Institute of Scientific and Technical Information of China (English)

    杨锴; 艾迪飞; 耿建华

    2012-01-01

    Based on a geostatistical simulation method conditioned to well-log and crosshole seismic rays, a novel geostatistical reservoir inversion (modeling) technique constrained by well-log, crosshole and surface seismic data is presented. Compared with existing geostatistical inversion methods only the well-log and surface seismic data being honored, all related prior information include well-log, crosshole seismic data and surface seimismic data are honored in presented method. Thus the accuracy of geostatistical inversion/modeling is greatly improved and the uncertainty of inversion/modeling is reduced. The test on synthetic data proved the above points.%利用能够整合测井信息与井间地震信息的地质统计学随机模拟方法,结合传统的地质统计学反演思路,得到了一种能够同时整合测井、井间地震与地面地震三种先验信息的地质统计学反演与储层建模方法.由于井间射线信息、测井信息与地面地震数据在随机反演与建模过程当中都得到了尊重,因此与传统地质统计学反演仅利用了测井与地面地震数据相比,本文的地质统计学反演与建模方法更充分地利用了先验信息,有效提高了反演的精度,降低了随机建模中的多解性.基于理论数据的测试证实了上述观点.

  3. Simulação geoestatística na caracterização espacial de óxidos de ferro em diferentes pedoformas Geostatistical simulation for the spatial characterization of iron oxides in different landforms

    Directory of Open Access Journals (Sweden)

    João Fernandes da Silva Junior

    2012-12-01

    of cause and effect with soil properties. In this sense, the quality of the spatial estimates can affect the results and consequently the interpretation of the spatial patterns. This study aimed to evaluate the performance of the geostatistical estimation methods by ordinary kriging (OK and conditional stochastic simulations (SGS in the characterization of spatial concentration of the iron oxides goethite (Gt and hematite (Hm in a concave and in a convex landform. From each landform, 121 soil samples of an Alfisol were collected at points with a regular spacing of 10 m. The iron oxide content was obtained by X-ray diffraction. The data were subjected to geostatistical analysis by modeling the variogram and later interpolation by OK and SGS. The OK did not reflect the true variability of the iron oxides hematite and goethite and is therefore inappropriate for the spatial characterization of the iron oxide concentrations. Thus, the use of SGS is preferable to kriging when the maintenance of high and low values in the spatial estimates is required. The performance of the geostatistical methods was influenced by the landform. For iron oxides, E-type maps should be recommended instead of OK maps, for being rich in detail and practical to define homogenous zones for localized managing for OK, particularly in concave landforms.

  4. Diagnostic techniques applied in geostatistics for agricultural data analysis Técnicas de diagnóstico utilizadas em geoestatística para análise de dados agrícolas

    Directory of Open Access Journals (Sweden)

    Joelmir André Borssoi

    2009-12-01

    Full Text Available The structural modeling of spatial dependence, using a geostatistical approach, is an indispensable tool to determine parameters that define this structure, applied on interpolation of values at unsampled points by kriging techniques. However, the estimation of parameters can be greatly affected by the presence of atypical observations in sampled data. The purpose of this study was to use diagnostic techniques in Gaussian spatial linear models in geostatistics to evaluate the sensitivity of maximum likelihood and restrict maximum likelihood estimators to small perturbations in these data. For this purpose, studies with simulated and experimental data were conducted. Results with simulated data showed that the diagnostic techniques were efficient to identify the perturbation in data. The results with real data indicated that atypical values among the sampled data may have a strong influence on thematic maps, thus changing the spatial dependence structure. The application of diagnostic techniques should be part of any geostatistical analysis, to ensure a better quality of the information from thematic maps.A modelagem da estrutura de dependência espacial pela abordagem da geoestatística é de fundamental importância para a definição de parâmetros que definem essa estrutura e que são utilizados na interpolação de valores em locais não amostrados, pela técnica de krigagem. Entretanto, a estimação de parâmetros pode ser muito alterada pela presença de observações atípicas nos dados amostrados. O desenvolvimento deste trabalho teve por objetivo utilizar técnicas de diagnóstico em modelos espaciais lineares gaussianos, empregados em geoestatística, para avaliar a sensibilidade dos estimadores de máxima verossimilhança e máxima verossimilhança restrita a pequenas perturbações nos dados. Foram realizados estudos de dados simulados e experimentais. O estudo com dados simulados mostrou que as técnicas de diagnóstico foram

  5. Research on the Spatial Interpolation of Concentrations of Carbon Dioxide Based on the Geostatistics%基于地统计方法的二氧化碳浓度空间插值研究

    Institute of Scientific and Technical Information of China (English)

    陈锦赋

    2012-01-01

    克里格法(Kriging)是地质统计学的主要内容之一,从统计意义上说,是从变量关系和变异性出发,在有限区域内对区域化变量的取值进行无偏、最优估计的一种方法:从插值角度讲是对空间分布的数据求线性最优、无偏内插估计的一种方法。克里格法的适用条件是区域化变量存在空间相关性。将临安市内的二氧化碳作为区域化变量.根据不同的半变异函数理论模型,采用普通Kriging法,通过对比分析得到:基于地统计的插值方法。根据半变异函数云图和试验方差最小的原理.选择合适的半变异函数理论模型进行变量的空间插值.能够较好地模拟区域化变量的空间连续分布格局.并取得较好效果。%Kriging is the main content of geostatistics, from the statistical sense, Kriging start from the variables and variability, a method of unbiased and the optimal estimation in a limited area about the value of regionalized variables. From the interpolation sense, Kriging is a method of linear optimal and Unbiased interpolation estimate to the space distribution of data. The appli- cable condition of Kriging is that regionalized variables have spatial correlation. With the carbon dioxide of Linan as regionalized variables, according to different half variation functions theory model, adopts the common method of Kriging, through the comparative analysis we can get : the interpolation method based on the geostatistics, according to the theory of Half a variation functions cloud chart and the minimum test variance, chooses the right half of the variograms model to spatial interpolation for variable, can simulate the space continuous distribution pattern of regionalized variables, and gets a good effect.

  6. 柑橘全爪螨种群空间格局的地学统计学分析%Geostatistic analysis of spatial pattern of the citrus red mite, Panonychus cirri (McGregor) ( Acarina: Tetranychidae) in citrus orchard

    Institute of Scientific and Technical Information of China (English)

    李志强; 梁广文; 岑伊静

    2008-01-01

    The citrus red mite, Panonychus citri (McGregor), is a key pest of citrus. Geostatistic method was applied to study the spatial pattern of citrus red mite population, in citrus orchard by the spatial analysis software Variowin 2.1, The results indicated that the spatial pattern of citrus red mite population can be described by geostatistic method, and the semivariogram of citrus red mite mainly fitted the gauss models with the ranges of 1.1-21.0 m. Citrus red mite population showed an aggregative distribution, and the aggregating intensities were relatively strong in March, August and September. The spatial pattern dynamics showed that two occurrence peaks of citrus red mite population occurred in April and October, specially in October, citrus red mite popula-tion rapidly diffused. March and September were two crucial stages of monitoring and treatment for citrus red mite.%应用地学统计学方法分析了柑橘园主要害螨柑橘全爪螨Panonychus citri(McGregor)种群的空间格局及其动态.结果表明,柑橘全爪螨种群具有空间相关性,变程介于1.10~21.0 m,其半变异函数主要符合高斯模型,表现为聚集分布,其中3月、8月和9月的聚集强度较大;种群空间格局动态显示,4月、10月为该种群的两个发生高峰期,柑橘全爪螨种群数量快速上升扩散.地学统计学方法能够应用于柑橘全爪螨种群的空间格局分析,并有助于对该害螨进行发生预测与控制处理.

  7. Use of geostatistic techniques to describe a reservoir to be submitted into a secondary recovery process field case: {open_quotes}Eocene B-Inferior/VLG-3659, Ceuta, Venezuela{close_quotes}

    Energy Technology Data Exchange (ETDEWEB)

    Hernandez, T.; Poquioma, W. [Maraven, S.A., Caracas (Venezuela)

    1997-08-01

    This study presents the results of an integrated reservoir study of the Eocene B-Inferior/VLG-3659, Area 7, Ceuta filed. This field located in the Maracaibo Lake in the western side of Venezuela. The objective was to evaluating the feasibility to implement a secondary recovery project by means of water flooding. Core information was used for this study (194 ft), PVT analysis, RFI, build-up and statistic`s pressure analysis, modem logs and production history data. Using geostatistical techniques (Kriging) it was defined a low uncertainty geological model that was validated by means of a black oil simulator (Eclipse). The results showed a good comparison of historical pressure of the reservoir against those obtained from the model, without the need of {open_quotes}history matching{close_quotes}. It means without modifying neither the initial rock properties nor reservoir fluids. The results of this study recommended drilling in two new locations, also the reactivation of four producing wells and water flooding under peripherical array by means of four injection wells, with the recovery of an additional 30.2 MMSTB. The economical evaluation shows an internal return rate of 31.4%.

  8. Geostatistics and remote sensing as predictive tools of tick distribution: a cokriging system to estimate Ixodes scapularis (Acari: Ixodidae) habitat suitability in the United States and Canada from advanced very high resolution radiometer satellite imagery.

    Science.gov (United States)

    Estrada-Peña, A

    1998-11-01

    Geostatistics (cokriging) was used to model the cross-correlated information between satellite-derived vegetation and climate variables and the distribution of the tick Ixodes scapularis (Say) in the Nearctic. Output was used to map the habitat suitability for I. scapularis on a continental scale. A data base of the localities where I. scapularis was collected in the United States and Canada was developed from a total of 346 published and geocoded records. This data base was cross-correlated with satellite pictures from the advanced very high resolution radiometer sensor obtained from 1984 to 1994 on the Nearctic at 10-d intervals, with a resolution of 8 km per pixel. Eight climate and vegetation variables were tabulated from this imagery. A cokriging system was generated to exploit satellite-derived data and to estimate the distribution of I. scapularis. Results obtained using 2 vegetation (standard NDVI) and 4 temperature variables closely agreed with actual records of the tick, with a sensitivity of 0.97 and a specificity of 0.89, with 6 and 4% of false-positive and false-negative sites, respectively. Such statistical analysis can be used to guide field work toward the correct interpretation of the distribution limits of I. scapularis and can also be used to make predictions about the impact of global change on tick range.

  9. Geostatistics and remote sensing using NOAA-AVHRR satellite imagery as predictive tools in tick distribution and habitat suitability estimations for Boophilus microplus (Acari: Ixodidae) in South America. National Oceanographic and Atmosphere Administration-Advanced Very High Resolution Radiometer.

    Science.gov (United States)

    Estrada-Peña, A

    1999-02-01

    Remote sensing based on NOAA (National Oceanographic and Atmosphere Administration) satellite imagery was used, together with geostatistics (cokriging) to model the correlation between the temperature and vegetation variables and the distribution of the cattle tick, Boophilus microplus (Canestrini), in the Neotropical region. The results were used to map the B. microplus habitat suitability on a continental scale. A database of B. microplus capture localities was used, which was tabulated with the AVHRR (Advanced Very High Resolution Radiometer) images from the NOAA satellite series. They were obtained at 10 days intervals between 1983 and 1994, with an 8 km resolution. A cokriging system was generated to extrapolate the results. The data for habitat suitability obtained through two vegetation and four temperature variables were strongly correlated with the known distribution of B. microplus (sensitivity 0.91; specificity 0.88) and provide a good estimation of the tick habitat suitability. This model could be used as a guide to the correct interpretation of the distribution limits of B. microplus. It can be also used to prepare eradication campaigns or to make predictions about the effects of global change on the distribution of the parasite.

  10. Apport d'une approche géostatique dans l'interprétation des mesures de diagraphies différées Enhancing Well Log Interpretation by Using a Geostatistical Approach

    Directory of Open Access Journals (Sweden)

    Denis A.

    2006-12-01

    , spherical or ellipsoidal. Three main groups of measured entities have been identified : jointed entities, overlapping entities and disjoined entities. When the length of the measured entities exceeds the sampling interval, there is an overlapping of the entities (macro logging tools. In that case, each measurement on an entity is partially correlated to the measurement on a neighbour entity, addition of data is unmeaningful and vertical definition is low. Simple statistical treatments, geostatistics or multivariate analysis are then biased, and the bias increase with the ratio between the length of the measured entity and the sampling interval increases. Starting from the assumption that, for a given logging tool, the measured entity remains stable throughout a well, it is possible to make the data additive by transforming the measured entities. A geostatistical approach allows to study the integrator effect of the tool on logging data and three methods to homogenize logging techniques data are proposed. The interest of these techniques for the homogenization of data is shown in several cases (synthetics and actuals logs data. The homogenization technique can give more rigorous log data, which can then be treated without bias. These methods also give the opportunity to increase the vertical resolution of logging tools : the improvement really depending of the sampling interval.

  11. Geostatistical analysis for soil moisture content under the no tillage cropping system Análise geoestatística do teor de água do solo sob sistema de cultivo em plantio direto

    Directory of Open Access Journals (Sweden)

    Célia Regina Grego

    2006-08-01

    Full Text Available Experiments in agriculture usually consider the topsoil properties to be uniform in space and, for this reason, often make inadequate use of the results. The objective of this study was to assess the variability for soil moisture content using geostatistical techniques. The experiment was carried out on a Rhodic Ferralsol (typic Haplorthox in Campinas, SP, Brazil, in an area of 3.42 ha cultivated under the no tillage system, and the sampling was made in a grid of 102 points spaced 10 m x 20 m. Access tubes were inserted down to one meter at each evaluation point in order to measure soil moisture contents (cm³ cm-3 at depths of 30, 60 and 90 cm with a neutron moisture gauge. Samplings were made between the months of August and September of 2003 and in January 2004. The soil moisture content for each sampling date was analyzed using classical statistics in order to appropriately describe the central tendency and dispersion on the data and then using geostatistics to describe the spatial variability. The comparison between the spatial variability for different samplings was made examining scaled semivariograms. Water content was mapped using interpolated values with punctual kriging. The semivariograms showed that, at the 60 cm depth, soil water content had moderate spatial dependence with ranges between 90 and 110 m. However, no spatial dependence was found for 30 and 90 cm depths in 2003. Sampling density was insufficient for an adequate characterization of the spatial variability of soil moisture contents at the 30 and 90 cm depths.Experimentos em agricultura geralmente consideram as propriedades do solo como sendo uniformes no espaço e, por esta razão, os resultados são freqüentemente mal interpretados. O objetivo deste estudo foi avaliar a variabilidade do teor de água do solo usando técnicas de geoestatística. O experimento foi desenvolvido em um Latossolo Vermelho eutroférrico, Campinas, SP, Brasil, numa área de 3,42 ha sob plantio

  12. Reserves Estimation of an Iron Deposit Based on Geostatistics Method%基于地质统计学方法的某铁矿资源量估算

    Institute of Scientific and Technical Information of China (English)

    周旋; 王选问; 金瑜

    2015-01-01

    Ore-body reserves are the important basis of mine construction and production. At present,the traditional re-serves estimation methods cannot meet the requirements of modern mine management. Based on the three-dimensional geolog-ical model of ore-body,geostatistics method is an optimal estimation scientific method of block models by analyzing the varia-tion characteristics of ore grade via the theory of regionalized variable. Taking a large iron deposit in Kyrgyzstan as the research object,firstly,the geological data-based and 3D model of ore-body are established;then,the grade-space variogram is obtained by analyzing the grade distribution characteristics of samples; finally,the Kriging interpolation method is adopted to estimate the grade of ore-body,and also the reserves. The research results show that compared with the traditional reserves estimation method,geostatistics method based on the three-dimensional geological model of ore-body and variogram can estimate reserves more scientifically,efficiently and accurately,which is conduced to realize the three-dimensional visualization of mine informa-tion and dynamic management of mine resources reserves.%矿体资源量是矿山生产建设的重要依据,目前,传统的资源量估算方法已经难以满足现代矿山管理的需求。建立在矿体三维地质模型基础上的地质统计学方法是一种运用区域化变量理论研究矿体品位的变化特征对块体模型进行最优估值的科学方法。以吉尔吉斯斯坦某大型铁矿为研究对象,首先建立地质数据库及矿体三维地质模型;然后通过分析样品品位分布特征,建立品位-空间变异函数;最后对矿体采用克里格插值法进行品位估值并计算资源量。结果表明,相对于传统资源量估算方法而言,地质统计学方法以矿山三维模型及变异函数为基础,能够更科学、高效、准确地估算资源量,有助于实现矿山信息的三维可视化

  13. A bottom-up geostatistical approach for quantifying landcover in Asian desert ecosystems and implications for global and climate models: a case study in Afghanistan utilizing a unique hyperspectral dataset

    Science.gov (United States)

    Shreve, C. M.; Okin, G. S.; Bowles, J.; Gardner, J.

    2007-12-01

    Political tensions, rough terrain, and remoteness have lead to a gap in the ecological understanding cold, mountainous deserts of Asia. Remote sensing is a time- and cost-efficient way to understand the spatial distribution and temporal dynamics of plant and snow cover in these regions. Here, a unique high-resolution hyperspectral dataset from Afghanistan is employed to classify ground cover at high resolution. The hyperspectral data was taken using a CASI-1500 Visible Near InfraRed (VNIR) spectrometer. The instrument was run in a mode with 1518 crosstrack pixels and 72 spectral bands between 380 and 1050 nm. The GSD was controlled by the altitude above ground level and aircraft speed, which varied resulting in GSD between 4 and 6 meters. Geolocation was provided by a CMIGITS II and the resulting accuracy will be better than 40 m. Atmospheric conditions were challenging and proper atmospheric compensation of the data remains a challenge. A bottom- up geostatistical approach for quantifying the coverage of vegetation and snow will be applied to establish the practical limits of coarse resolution MODIS data for classifying vegetation and snow cover, a scale suitable for monitoring large regions and for modeling. A Multiple Endmember Linear Spectral Mixture Algorithm (MESMA) will be applied to classify land cover. Semivariograms at the multispectral (30 m) and coarse resolution scale (1 km) will be compared with simulated variograms using hysperspectral data. Patches of vegetation and snow cover used for spatial comparison will be identified in the image and characterized using object-oriented image analysis software. The relative amount of cover will be determined using block-kriging and compared between scenes with statistical tests. Insight gained from this analysis can be applied to improve existing data products and can be applied for carbon budget and climate change models.

  14. Improved Accuracy of Chlorophyll-a Concentration Estimates from MODIS Imagery Using a Two-Band Ratio Algorithm and Geostatistics: As Applied to the Monitoring of Eutrophication Processes over Tien Yen Bay (Northern Vietnam

    Directory of Open Access Journals (Sweden)

    Nguyen Thi Thu Ha

    2013-12-01

    Full Text Available Sea eutrophication is a natural process of water enrichment caused by increased nutrient loading that severely affects coastal ecosystems by decreasing water quality. The degree of eutrophication can be assessed by chlorophyll-a concentration. This study aims to develop a remote sensing method suitable for estimating chlorophyll-a concentrations in tropical coastal waters with abundant phytoplankton using Moderate Resolution Imaging Spectroradiometer (MODIS/Terra imagery and to improve the spatial resolution of MODIS/Terra-based estimation from 1 km to 100 m by geostatistics. A model based on the ratio of green and blue band reflectance (rGBr is proposed considering the bio-optical property of chlorophyll-a. Tien Yen Bay in northern Vietnam, a typical phytoplankton-rich coastal area, was selected as a case study site. The superiority of rGBr over two existing representative models, based on the blue-green band ratio and the red-near infrared band ratio, was demonstrated by a high correlation of the estimated chlorophyll-a concentrations at 40 sites with values measured in situ. Ordinary kriging was then shown to be highly capable of predicting the concentration for regions of the image covered by clouds and, thus, without sea surface data. Resultant space-time maps of concentrations over a year clarified that Tien Yen Bay is characterized by natural eutrophic waters, because the average of chlorophyll-a concentrations exceeded 10 mg/m3 in the summer. The temporal changes of chlorophyll-a concentrations were consistent with average monthly air temperatures and precipitation. Consequently, a combination of rGBr and ordinary kriging can effectively monitor water quality in tropical shallow waters.

  15. 利用 GIS地理统计模块预测海南岛植被指数季节性变化趋势%Prediction of the Seasonal Change Trend of NDVI in Hainan Island by GIS Geostatistical Analysis

    Institute of Scientific and Technical Information of China (English)

    刘少军; 黄彦彬; 张京红; 李天富; 陈汇林; 陈德明

    2006-01-01

    虽然采用遥感图像提取的植被指数在空间上能较好的反映作物的状况,但其不能预测植被指数在空间上的变化范围,如果能从整体上了解不同市县在不同季节的平均植被指数值,就可以对该区域整体植被状态进行量化分析,也就可以从大范围内进行植被指数的预测分析.利用地理信息系统(GIS)和地统计学相结合的地理统计分析模块(ArcGIS Geostatistical Analyst),根据MODIS遥感数据提取的每季度不同市县平均NDVI植被指数,采用Kriging插值的方法分析了海南岛归一化植被指数(NDVI)季节性变化趋势,并与实际采样值进行对比分析,结果表明,利用ArcGIS Geostatistical Analyst中的Kriging插值方法能较好地预测植被指数的空间分布范围.

  16. Geo-statistical analysis of spatial patterns of Batocera davidis larvae%橙斑白条天牛幼虫空间分布的地理统计学分析

    Institute of Scientific and Technical Information of China (English)

    张思禄

    2012-01-01

    橙斑白条天牛是杨树的一种新害虫.应用地理统计学分析方法,研究了该虫的空间分布特点.结果表明,橙斑白条天牛幼虫在杨树林间呈聚集分布,种群在样地内具有明显空间依赖性,空间聚集范围在10-13 m之间.根据不同样方天牛幼虫种群的变程,南→北方向的相关距离大于东→西方向,说明天牛幼虫在林间的聚集斑块不是圆形的,而是南→北方向比东→西方向长,南→北方向是种群聚集的主方向,也是种群扩散的主要方向.这为该虫的监测和预测预报提供了科学依据.%Batocera davidis Deyrolle is a new poplar pest. Geostatistical methods were applied to measure and analyzed the spatial pattern of B. Davidis larvae. The results showed that the population spatial pattern displayed an aggregation model with significantly spatial dependence. Spatial range of aggregation fluctuated from 10 to 13 m in the all sampling sites. Based on the variogram model parameters, it was found that the relative distance was more in the orientation from south to north than from east to west. It was suggested that the aggregative spot of this pest population was not rounded, however, it was longer in south-north orientation than in east-west orientation. It could be so concluded that the pest population dispersals and aggregates mainly in south-north orientation. It has offered a scientific basis for the monitoring and prediction of B. Davidis.

  17. Mapeamento de áreas de risco à saúde pública por meio de métodos geoestatísticos Public health risk maps using geostatistical methods

    Directory of Open Access Journals (Sweden)

    Roberto Wagner Lourenço

    2005-02-01

    Full Text Available Este trabalho tem por objetivo apresentar uma aplicação de métodos geoestatísticos na elaboração de mapas de risco à saúde pública, por meio da identificação de áreas com maior concentração de metais pesados. Foi escolhido o elemento chumbo (Pb, resultante do transporte aéreo ou do carregamento das partículas causado pela lixiviação do solo, em uma região com grande concentração urbana e industrial na Baixada Santista, São Paulo, Brasil. Elaboraram-se mapas das distribuições espaciais desse elemento por intermédio da krigagem ordinária; posteriormente, utilizando-se a krigagem indicativa, identificaram-se as áreas com valores de contaminação do solo superiores aos níveis máximos aceitáveis pelo órgão de controle ambiental do Estado de São Paulo, originando um mapeamento com áreas com maior probabilidade de risco à saúde pública. Os mapas resultantes mostraram-se ferramentas promissoras para auxiliar a tomada de decisão quanto a questões de políticas públicas relacionadas à saúde e ao planejamento ambiental.The purpose of this paper was to demonstrate an application of geostatistical methods to public health risk maps through the identification of areas with elevated concentrations of heavy metals. The study focused on the element lead (Pb from aerial transportation or loading of particles due to soil leaching in an area with major urban and industrial concentration in the Baixada Santista on the coastland of São Paulo State, Brazil. Maps with the spatial distribution of lead were produced using ordinary kriging; subsequently indicative kriging was performed to identify soil sites with contamination levels higher than the maximum acceptable level defined by the São Paulo State Environmental Control Agency. The resulting maps showed areas with increased probability of public health risk. The methodology proved to be a promising approach for decision-making related to health public policies and

  18. Geostatistical analysis of microrelief of an oxisol as a function of tillage and cumulative rainfall Análise geoestatística do microrrelevo de um Latossolo em função do preparo do solo e da precipitação acumulada

    Directory of Open Access Journals (Sweden)

    Eva Vidal Vázquez

    2009-04-01

    Full Text Available Surface roughness can be influenced by type and intensity of soil tillage among other factors. In tilled soils microrelief may decay considerably as rain progresses. Geostatistics provides some tools that may be useful to study the dynamics of soil surface variability. The objective of this study was to show how it is possible to apply geostatistics to analyze soil microrelief variability. Data were taken at an Oxisol over six tillage treatments, namely, disk harrow, disk plow, chisel plow, disk harrow + disk level, disk plow + disk level and chisel plow + disk level. Measurements were made initially just after tillage and subsequently after cumulative natural rainfall events. Duplicated measurements were taken in each one of the treatments and dates of samplings, yielding a total of 48 experimental surfaces. A pin microrelief meter was used for the surface roughness measurements. The plot area was 1.35 × 1.35 m and the sample spacing was 25 mm, yielding a total of 3,025 data points per measurement. Before geostatistical analysis, trend was removed from the experimental data by two methods for comparison. Models were fitted to the semivariograms of each surface and the model parameters were analyzed. The trend removing method affected the geostatistical results. The geostatistical parameter dependence ratio showed that spatial dependence improved for most of the surfaces as the amount of cumulative rainfall increased.A rugosidade da superfície pode ser influenciada pelo tipo e pela intensidade do preparo do solo, entre outros fatores. Em solos preparados o microrrelevo é aplanado consideravelmente com o acúmulo da chuva. A Geoestatística promove algumas ferramentas que podem ser úteis no estudo da dinâmica da variabilidade da superfície do solo. O objetivo desse estudo foi verificar se é possível aplicar geoestatística na análise da variação do microrrelevo do solo. Os resultados foram obtidos num Latossolo sob seis tratamentos de

  19. Scalable Learning for Geostatistics and Speaker Recognition

    Science.gov (United States)

    2011-01-01

    with Abalone and PumaDyn8NH . . . . . . . . . . 94 5.5 This is a randomly chosen class of pose images from the PIE dataset. The images were assigned...experiment, we used our GPU based kernel 27 CPU GPU Dataset dxN Time Time (s) (s) Diabetes 2x43 0.0473 0.1639 Abalone 7x4177 235.8 0.79 Bank7FM 8x4499...train images to learn poses, [68] uses an online Gaussian process algo- 94 rithm to learn head pose from images. For this experiment, we used the PIE

  20. Cross-covariance functions for multivariate geostatistics

    KAUST Repository

    Genton, Marc G.

    2015-05-01

    Continuously indexed datasets with multiple variables have become ubiquitous in the geophysical, ecological, environmental and climate sciences, and pose substantial analysis challenges to scientists and statisticians. For many years, scientists developed models that aimed at capturing the spatial behavior for an individual process; only within the last few decades has it become commonplace to model multiple processes jointly. The key difficulty is in specifying the cross-covariance function, that is, the function responsible for the relationship between distinct variables. Indeed, these cross-covariance functions must be chosen to be consistent with marginal covariance functions in such a way that the second-order structure always yields a nonnegative definite covariance matrix. We review the main approaches to building cross-covariance models, including the linear model of coregionalization, convolution methods, the multivariate Matérn and nonstationary and space-time extensions of these among others. We additionally cover specialized constructions, including those designed for asymmetry, compact support and spherical domains, with a review of physics-constrained models. We illustrate select models on a bivariate regional climate model output example for temperature and pressure, along with a bivariate minimum and maximum temperature observational dataset; we compare models by likelihood value as well as via cross-validation co-kriging studies. The article closes with a discussion of unsolved problems. © Institute of Mathematical Statistics, 2015.

  1. Rainfall variation by geostatistical interpolation method

    Directory of Open Access Journals (Sweden)

    Glauber Epifanio Loureiro

    2013-08-01

    Full Text Available This article analyses the variation of rainfall in the Tocantins-Araguaia hydrographic region in the last two decades, based upon the rain gauge stations of the ANA (Brazilian National Water Agency HidroWeb database for the years 1983, 1993 and 2003. The information was systemized and treated with Hydrologic methods such as method of contour and interpolation for ordinary kriging. The treatment considered the consistency of the data, the density of the space distribution of the stations and the periods of study. The results demonstrated that the total volume of water precipitated annually did not change significantly in the 20 years analyzed. However, a significant variation occurred in its spatial distribution. By analyzing the isohyet it was shown that there is a displacement of the precipitation at Tocantins Baixo (TOB of approximately 10% of the total precipitated volume. This displacement can be caused by global change, by anthropogenic activities or by regional natural phenomena. However, this paper does not explore possible causes of the displacement.

  2. Bayesian multimodel inference for geostatistical regression models.

    Directory of Open Access Journals (Sweden)

    Devin S Johnson

    Full Text Available The problem of simultaneous covariate selection and parameter inference for spatial regression models is considered. Previous research has shown that failure to take spatial correlation into account can influence the outcome of standard model selection methods. A Markov chain Monte Carlo (MCMC method is investigated for the calculation of parameter estimates and posterior model probabilities for spatial regression models. The method can accommodate normal and non-normal response data and a large number of covariates. Thus the method is very flexible and can be used to fit spatial linear models, spatial linear mixed models, and spatial generalized linear mixed models (GLMMs. The Bayesian MCMC method also allows a priori unequal weighting of covariates, which is not possible with many model selection methods such as Akaike's information criterion (AIC. The proposed method is demonstrated on two data sets. The first is the whiptail lizard data set which has been previously analyzed by other researchers investigating model selection methods. Our results confirmed the previous analysis suggesting that sandy soil and ant abundance were strongly associated with lizard abundance. The second data set concerned pollution tolerant fish abundance in relation to several environmental factors. Results indicate that abundance is positively related to Strahler stream order and a habitat quality index. Abundance is negatively related to percent watershed disturbance.

  3. Uso da geoestatística para caracterização da distribuição espacial de larvas de Diloboderus abderus Geostatistical use for characterization of the spatial distribution of larvae of Diloboderus abderus

    Directory of Open Access Journals (Sweden)

    Elder Dal Prá

    2011-10-01

    Full Text Available No Brasil, existem registradas aproximadamente mil espécies de corós, destacando-se, dentre os de maior importância, Diloboderus abderus Sturm, 1826 (Coleoptera: Melolonthidae, pelos prejuízos que pode causar aos cultivos agrícolas e a ampla ocorrência geográfica. O trabalho teve por objetivo caracterizar, com uso da geoestatística, a distribuição espacial de larvas de D. abderus. O estudo foi realizado no ano de 2009, em lavouras de aveia nos municípios de São Francisco de Assis, Cruz Alta e Lagoa Vermelha, RS. Os perímetros das áreas foram demarcados com receptor de sistema de posicionamento global, e os grides de amostragem tiveram dimensão de 70x70m. A densidade populacional foi estimada com abertura de uma trincheira em cada ponto amostral. As análises da variabilidade espacial e da dependência espacial foram feitas por meio de semivariogramas e classificadas segundo CAMBARDELLA et al. (1994. Já os mapas foram gerados a partir dos dados de contagem de larvas em campo. Os semivariogramas indicam a presença de dependência espacial nas áreas de avaliação. Os grides de amostragem mostraram-se apropriados para caracterizar a distribuição espacial de larvas de D. abderus. A distribuição espacial de D. abderus é agregada e seu conhecimento pode melhorar o manejo da praga.In Brazil, there are about a thousand recorded species of white grubs, and among them the most important is Diloboderus abderus Sturm, 1826 (Coleoptera: Melolonthidae, because of the damage caused to agricultural crops and the wide geographic occurrence. The study aimed to characterize, using geostatistical, the spatial distribution of larvae of D. abderus. The study was conducted during 2009 in oat crops in the counties of São Francisco de Assis, Cruz Alta and Lagoa Vermelha, RS. The perimeters of the fields were delimited with global positioning system receptor, and the sampling grids dimensions were 70x70m. The population density was estimated by

  4. Incorporación de la metodología geoestadística a la vigilancia de la gripe en una red centinela Incorporation of geostatistical methodology for influenza surveillance in a sentinel network

    Directory of Open Access Journals (Sweden)

    J.J. Abellán

    2002-08-01

    Full Text Available Objetivo: Valorar la descripción geoestadística realizada de los datos de gripe recogidos a través de la Red Centinela Sanitaria de la Comunidad Valenciana (RCSCV mediante la utilización del método kriging con la finalidad de evaluar la posibilidad de su incorporación a la vigilancia rutinaria Método: Se han utilizado los datos de vigilancia de gripe de la RCSCV en tres temporadas gripales (1997-1998, 1998-1999 y 1999-2000, construyéndose una matriz de datos de incidencia de gripe geocodificada. La distribución geográfica fue estudiada mediante la técnica geoestadística kriging, que permite estimar la incidencia de la enfermedad en cualquier punto del territorio, a partir de la incidencia observada en unos pocos puntos estratégicamente distribuidos. Se elaboraron mapas de curvas de isoincidencia de gripe para cada semana. La valoración de la técnica se realizó mediante validación cruzada. Resultados: En la mayoría de las semanas, los valores tanto de la desviación estándar (DE reducida, como de la media reducida estuvieron cercanos a los valores considerados óptimos (0 o 1, respectivamente, y sólo en la última temporada se obtuvieron valores de la DE reducida alejados de los considerados como de buen ajuste en 12 de las 20 semanas. La estimación de tasas en todas las temporadas demostró una coherencia en su distribución espacial. También se observó coherencia en la evolución temporal. Conclusiones: En la mayoría de las situaciones los resultados pueden considerarse aceptables, no requiere recursos informáticos extraordinarios ni un empleo de tiempo excesivo, y necesita tan sólo una adaptación anual. Su facilidad de uso lo hace apto para su utilización como una técnica de rutina, pese a que puede mejorarse la precisión de las estimaciones, incrementando la complejidad del modelo.Objectives: To evaluate geostatistical description of influenza data from the Valencian Sentinel Network (VSN in Spain using the

  5. Lithological 3D grid model of the Vuonos area built by using geostatistical simulation honoring the 3D fault model and structural trends of the Outokumpu association rocks in Eastern Finland

    Science.gov (United States)

    Laine, Eevaliisa

    2015-04-01

    The Outokumpu mining district - a metallogenic province about 100 km long x 60 km wide - hosts a Palaeoproterozoic sulfide deposit characterized by an unusual lithological association. It is located in the North Karelia Schist Belt , which was thrust on the late Archaean gneissic-granitoid basement of the Karelian craton during the early stages of the Svecofennian Orogeny between 1.92 and 1.87 Ga (Koistinen 1981). Two major tectono-stratigraphic units can be distinguished, a lower, parautochthonous 'Lower Kaleva' unit and an upper, allochthonous 'upper Kaleva' unit or 'Outokumpu allochthon'. The latter consists of tightly-folded deep marine turbiditic mica schists and metagraywackes containing intercalations of black schist, and the Outo¬kumpu assemblage, which comprises ca. 1950 Ma old, serpentinized peridotites surrounded by carbonate-calc-silicate ('skarn')-quartz rocks. The ore body is enclosed in the Outokumpu assemblage, which is thought to be part of a disrupted and incomplete ophiolite complex (Vuollo & Piirainen 1989) that can be traced to the Kainuu schist belt further north where the well-preserved Jormua ophiolite is ex¬posed (Kontinen 1987, Peltonen & Kontinen 2004). Outokumpu can be divided into blocks divided by faults and shear zones (Saalmann and Laine, 2014). The aim of this study was to make a 3D lithological model of a small part of the Outokumpu association rocks in the Vuonos area honoring the 3D fault model built by Saalmann and Laine (2014). The Vuonos study area is also a part of the Outokumpu mining camp area (Aatos et al. 2013, 2014). Fault and shear structures was used in geostatistical gridding and simulation of the lithologies. Several possible realizations of the structural grids, conforming the main lithological trends were built. Accordingly, it was possible to build a 3D structural grid containing information of the distribution of the possible lithologies and an estimation the associated uncertainties. References: Aatos, S

  6. Geoestatística na determinação da variabilidade espacial de características químicas do solo sob diferentes preparos Geostatistics to determine spatial variability of soil chemical properties using different preparation systems

    Directory of Open Access Journals (Sweden)

    José Ruy Porto de Carvalho

    2002-08-01

    Full Text Available O objetivo deste trabalho foi estudar, mediante a geoestatística, a variabilidade espacial de pH, Ca, Mg, P e K em Latossolo Vermelho-Escuro distrófico, textura argilosa, cultivado durante cinco anos consecutivos (1992-1996, em três sistemas de preparo (arado, grade e plantio direto na Embrapa-Centro Nacional de Pesquisa de Arroz e Feijão, em Santo Antônio de Goiás, GO. Das 30 combinações entre características químicas do solo, profundidades de coleta e sistemas de preparo, 14 apresentaram efeito pepita puro, indicando ausência de dependência espacial. Semivariogramas direcionais revelaram forte e moderada dependência espacial na direção de Y. Experimentos longevos com práticas culturais orientadas em uma única direção tendem a mudar a estrutura espacial das propriedades do solo, o que indica ser a razão dos resultados obtidos. A direção de anisotropia está mais associada com o tratamento arado e a mais forte dependência espacial foi verificada com relação ao pH no sistema de preparo arado na profundidade de 5-20 cm. A localização das amostras para estimar os valores das características químicas do solo deve levar em conta as operações de campo, e cuidados devem ser tomados em relação à amostragem casual.As amostras devem ser retiradas em outras direções, para que uma representação mais realista da área amostrada seja obtida.Spatial variability of pH, Ca, Mg, P and K under three soil preparation systems (moldboard plough, harrow disc and no-tillage was studied using geostatistical concepts in clayey Oxisol, in Santo Antônio de Goiás, GO, Brazil, at Embrapa-Centro Nacional de Pesquisa de Arroz e Feijão, for five consecutive years (1992-1996. Within a total of 30 combinations among soil chemical properties, soil depth and preparation system, 14 presented pure nugget effect, indicating absence of spatial dependence. Directional semivariograms revealed strong and moderate spatial dependence in the direction

  7. 豫西典型烟田土壤颗粒组成的空间变异性分析%Spatial Variability of Soil Particle Composition Based on Geostatistics

    Institute of Scientific and Technical Information of China (English)

    江厚龙; 王新中; 刘国顺; 胡宏超; 刘清华

    2012-01-01

    Spatial variability of soil nutrients provides useful information for improving agricultural practices and ecological management. The objectives of this study were to (i) quantify the spatial variability and spatial correlation of soil particle composition across areas and (ii) generate contour maps for the soil particle composition to reveal the characteristic of distribution. Grid sampling method (100 m×100 m) and geostatistics were applied to analyze the spatial distribution of soil particle composition in tobacco plantation field in Pingdingshan. The results showed that, sample variograms of soil particle composition were fitted well by spherical and exponential models, respectively. Sand and clay had a strong spatial correlation, good structural, while the silt with a moderate spatial correlation and bad structural. The anisotropic of three components of soil particle composition were little. The clay had the strongest of trend effect and the biggest of spatial variability, sand had little spatial variability with certain trends in effects, silt had a little trends effect Kriging interpolation results showed that the high sand content in the north, while the lower in south-west; the distribution of clay and silt content was converse to sand. In the study area, the most account of the area was loam, the clay loam had small distributed only in the southwest comer.%空间变异性研究有利于认识土壤养分的空间分布特征与生态过程的关系.本研究旨在系统量化研究区域土壤颗粒组成的空间变异性和相关性,以揭示土壤颗粒组成的空间分布特征.采用“网格法”取样( 100 m×100 m),利用地统计学方法分析了平顶山典型烟区土壤颗粒组成的空间相关性.结果表明,黏粒和砂粒的最优半方差函数为球状模型,粉粒为指数模型;砂粒和黏粒具有较强的空间相关性和较好的结构性,粉粒具有中等空间相关性,结构性较差;三者的各向异性均较小,

  8. Pollution assessments on heavy metals in sediment in inter-tidal aqua-farm area based on GIS and geostatistics%基于GIS和地统计学的滩涂增养殖区沉积物重金属污染评价

    Institute of Scientific and Technical Information of China (English)

    张博; 郑青松; 赵耕毛; 刘兆普

    2011-01-01

    采用地理信息系统(GIS)、地统计方法和潜在生态危害指数法相结合的方法,对江苏省如东滩涂增养殖区沉积物中的Cu、Zn、Cd、Pb进行了综合定量化污染评价研究.结果生成了沉积物重金属综合生态危害风险指数空间分布图,发现整个调查区域部属轻微生态危害,且靠近排污口的区域生态危害程度较重,与实际情况相符.说明使用GIS和地统计学方法可正确、直观地反映沉积物重金属的污染状况,为其在滩涂增养殖区污染评价中的实际运用提供了思路.%The comprehensive quantitative pollution assessments were carried out by the geographic information system (GIS) ,geo-statistics and potential ecological risk index methods on copper, zinc,cadmium and lead in sediments in Rudong intertidal aqua-farm area of Jiangsu Province. The results showed a spatial distribution of comprehensive ecological risk index for heavy metals in sediments. It was found that the whole survey area belonged to the light ecological risk and the ecological risk level of the region near the sewage ontfall was heavier in the map,which consistent with the actual situation. It proved that the use of GIS and geo-statistics methods could refiect the situation of the heavy metals pollution in sediments correcfiy and intuitively. Thus, it provided a guideline for the practical applieation of the heavy metal pollution assessments.

  9. STUDY OF THE SPACIAL DISTRIBUTION OF ANGICO (Anadenanthera peregrina IN THE "EDMUNDO NAVARRO DE ANDRADE" STATE FOREST - RIO CLARO, SP, BRAZIL, EMPLOYING GEOSTATISTICAL METHODOLOGY = ESTUDO DA DISTRIBUIÇÃO ESPACIAL DO ANGICO (Anadenanthera peregrina NA FLORESTA ESTADUAL "EDMUNDO NAVARRO DE ANDRADE" - RIO CLARO,SP, BRASIL, EMPREGANDO METODOLOGIA GEOESTATÍSTICA

    Directory of Open Access Journals (Sweden)

    Paulo Milton Barbosa Landim

    2003-01-01

    Full Text Available Studies concerning application of geostatistical methodology to spacedistribution and mapping of plant species populations are rare. The main purpose of this study is to evaluate the application of geostatistics in detection and prediction of the space pattern of Anadenanthera peregrina "angico" at the "Edmundo Navarro de Andrade" State Forest (Rio Claro/SP. Simulations of the population data, previously mapped, were made in laboratory, by PCQ method. Using ordinary kriging interpolation technique, a map of "angicos" aggregation occurrence aggregation was generated for the area. Such method showed to be efficient to spatial analysis of the populationagglomerates, as it could be observed by overlapping the population mapped with the map of the aggregation estimates originating from sampling. This case study can contribute to the discussion of the traditional methods of botanical data sampling, proposing a new methodology for analysis using space statistics. = Os estudos pertinentes à aplicação de metodologia geoestatística nos estudos de distribuição espacial e mapeamento de populações de espécies vegetais são escassos. Este estudo objetivou avaliar o emprego da geoestatística na detecção e predição do padrão espacial de Anadenanthera peregrina "angico", em um talhão de eucaliptos naFloresta Estadual Edmundo Navarro de Andrade/Rio Claro-SP. Foram feitas simulações dos dados em laboratório, pelo método PCQ, da população previamente mapeada no campo. A partir da técnica de interpolação da krigagem ordinária, foi gerado o mapa de ocorrência de agregação de angicos na área. Tal método mostrou ser eficiente paraanalisar espacialmente os aglomerados populacionais, como pôde ser observado com a sobreposição da população mapeada com o mapa das estimativas de agregação oriundas da amostragem. Este estudo de caso pode contribuir para a discussão dos métodos tradicionais de coleta de dados botânicos, com a proposta

  10. Spatial Variability of Soil Organic Carbon density of Surface Layer based on GIS and Geostatistics in Changtu City%基于GIS和地统计学的昌图县表层土壤有机碳密度空间变异分析

    Institute of Scientific and Technical Information of China (English)

    解书华

    2014-01-01

    采用GIS和地统计学相结合的方法,分析昌图县表层土壤(0~20 cm)样品中的有机碳密度,探讨有机碳密度的空间变异特性。结果表明,表层土壤有机碳密度在0.55~3.41 g/kg之间,平均值为1.97 g/kg,具有强变异性;有机碳密度的理论变异函数符合线形模型,具有较强的空间相关性。%This paper made analysis for Changtu laboratory data of 70 sample 0~20 cm surface soil samples. Spatial analysis was conducted by GIS and geostatistics in Changtu. Results showed that surface soil organic carbon density varied between 0.55 g/kg and 3.41 g/kg, the mean content was 1.97g/kg, with strong variability, and the semivariograms for surface soil organic carbon density was well fitted by spherical model, with a weaker spatial correlation.

  11. Geostatistical Modelling of the Travertine Formation Associated with the Alicun de las Torres Thermal System by Using Electrical Tomography and Porosity Data; Modelizacion mediante Tecnicas Geoestadisticas de la Formacion de Trevertinos Asociada al Sistema Termal de Alicun de las Torres a partir de Datos de Tomografia Electrica y Porosidad

    Energy Technology Data Exchange (ETDEWEB)

    Prado Perez, A. J.; Aracil, E.; Perez del Villar, L.

    2010-11-17

    In the framework of a Singular Strategic Project entitled: Advanced Technologies of Carbon, Capture and Storage (CCS)1', supported by the MICINN (Spain) and the FEDER founds (EU), specifically in the Carbon Storage Task, a comprehensive study on the CO{sub 2} leakage as DIG (Dissolved Inorganic Carbon) in the Alicun de Las Torres (Prov. of Granada) natural analogue thermal system was envisaged. This analogous system is characterised by the presence of a very important travertine formation, which can be considered as a permanent and stable sink for CO{sub 2}. Consequently, the estimation of the travertine mass has been a main objective of this investigation. For that, data from two complementary electrical tomography campaigns, previously treated by a powerful geostatistical tool, have been used, as well as the porosity average value of this travertine and the calcite density. Besides this, the statistical methodology applied has also allowed the establishment of a 3-D model of the travertine formation, displaying the geological contact between this formation and the underlying materials, as well as the contacts among the three units forming the travertine formation. (Author) 23 refs.

  12. Análise dos atributos do solo e da produtividade da cultura de cana-de-açúcar com o uso da geoestatística e árvore de decisão Analyze the soil attributes and sugarcane yield culture with the use of geostatistics and decision trees

    Directory of Open Access Journals (Sweden)

    Zigomar Menezes de Souza

    2010-04-01

    Full Text Available Um dos desafios da agricultura de precisão é oferecer subsídios para a definição de unidades de manejo para posteriores intervenções. Portanto, o objetivo deste trabalho foi avaliar os atributos químicos do solo e a produtividade da cultura de cana-de-açúcar por meio da geoestatística e mineração de dados pela indução da árvore de decisão. A produtividade da cana-de-açúcar foi mapeada em uma área de aproximadamente 23ha, utilizando-se o critério de célula, por meio de um monitor de produtividade que permitiu a elaboração de um mapa digital que representa a superfície de produção para a área em estudo. Para determinar os atributos de um Argissolo Vermelho-Amarelo, foram coletadas as amostras no início da safra 2006/2007, utilizando-se uma grade regular de 50 x 50m, nas profundidades de 0,0-0,2m e 0,2-0,4m. Os dados dos atributos do solo e da produtividade foram analisados por meio da técnica de goestatística e classificados em três níveis de produção para indução de árvore de decisão. A árvore de decisão foi induzida no programa SAS Enterprise Miner, sendo utilizado algoritmo baseado na redução de entropia. As variáveis altitude e potássio apresentaram os maiores valores de correlação com a produtividade de cana-de-açúcar. A indução de árvores de decisão permitiu verificar que a altitude é a variável com maior potencial para interpretar os mapas de produtividade de cana-de-açúcar, auxiliando na agricultura de precisão e mostrando-se uma ferramenta adequada para o estudo de definição de zonas de manejo em área cultivada com essa cultura.One of the challenges of precision agriculture is to offer subsidies for the definition of management units for posterior interventions. Therefore, the objective of this work was to evaluate soil chemical attributes and sugarcane yield with the use of geostatistics and data mining by decision tree induction. Sugarcane yield was mapped in a 23ha field

  13. Volume 89, Issue4 (September 2004)Articles in the Current Issue:Original PaperSpatial Analysis of Twaite Shad, Alosa fallax (Lacepède, 1803), in the Southern North Sea: Application of Non-Linear Geostatistics as a Tool to Search for Special Areas of Conservation

    Science.gov (United States)

    Stelzenmüller, Vanessa; Maynou, Francesc; Ehrich, Siegfried; Zauke, Gerd-Peter

    2004-09-01

    This study aims to evaluate the suitability of non-linear geostatistics and indicator kriging (IK) as a tool in environmental impact assessment and nature conservation, in particular to search for potential Special Areas of Conservation (SAC) for the endangered fish species twaite shad, Alosa fallax (Lacepède, 1803) within the German Exclusive Economical Zone (EEZ) of the North Sea. To analyse the spatial distribution of this fish species, data on standardised biomass index (catch per unit effort, c.p.u.e., kg × 30 min-1) from 1996 to 2001 were used, regarding the third and fourth quarters of each year, respectively. Thereby we assume that the spatial distribution can be described as a time-invariant process. This assumption is supported by information on annual sampling effort, allocation of hauls and spatial distribution of the positive catches. All indicator variograms obtained for different c.p.u.e. cut-off values displayed distinct spatial structures, clearly indicating that the indicator variables were spatially autocorrelated. Gaussian models were fitted by least-squares methods and were evaluated with a goodness-of-fit statistic. Subsequently, IK was employed to estimate the probability of exceeding the c.p.u.e. cut-off values for the twaite shad in the investigation area. These were highest in the Weser- and Elbe-estuary, probably because of migrations of twaite shad to and from estuaries at the time of investigation due to spawning, while within the German EEZ of the North Sea no such areas with increased probabilities could be discerned. Thus, although available data did not allow to identify and implement any SAC in the German EEZ, the methods employed here can be regarded as a promising management tool in biological conservation issues. (

  14. THE BAYESIAN MAXIMUM ENTROPY GEOSTATISTICAL APPROACH AND ITS APPLICATION IN SOIL AND ENVIRONMENTAL SCIENCES%贝叶斯最大熵地统计学方法及其在土壤和环境科学上的应用

    Institute of Scientific and Technical Information of China (English)

    张贝; 李卫东; 杨勇; 汪善勤; 蔡崇法

    2011-01-01

    The Bayesian maximum entropy ( BME) approach has emerged in recent years as a new spatio-temporal geostatistics methods. By capitalizing on various sources of information and data, BME introduces an epistemological framework which produces predictive maps that are more accurate and in many cases computationally more efficient than those derived with traditional techniques. It is a general approach that does not need to make assumptions regarding linear valuation, spatial homogeneity or normal distribution. BME can integrate a priori knowledge and soft data without losing any useful information they contain and improve accuracy of the analysis. This paper first introduces the basic theory of BME and stages of BME estimation, and then briefly describes its development and application in soil and environmental sciences. Finally the application of this method is also summarized and prospected. After years of development and practice, the BME method has been proved to be a mature outstanding approach, which has a broad prospect of application in evaluation of resources and environment.%贝叶斯最大熵(Bayesian Maximum Entropy,BME)地统计学方法是近年来出现的一种时空地 统计学新方法.相对于传统的克里金方法,该法具有坚实的认识论框架和方法学基础.它不需要作线性估 值、空间匀质和正态分布的假设,能够融入先验知识和软数据,并且不会损失其中蕴含的有用信息,提高了分 析精度.本文首先介绍了BME的基本理论及其估值方法,随后简单描述了该方法的理论发展过程及其在土 壤和环境科学上的应用情况,最后对该方法的应用做了总结与展望.经过国外研究者多年的开发和实践, BME方法已经被证明是一个理论上较为成熟,能够应用到实际研究中的优秀地统计学方法,在资源环境评估 上有着广泛的应用前景.

  15. 基于地统计学方法的区域旅游空间结构研究--以皖南国际文化旅游示范区为例%Regional Tourism Spatial Structure Based on Geostatistical Method:A Case Study of South Anhui International Cultural Tourism Demonstration Area

    Institute of Scientific and Technical Information of China (English)

    祝亚雯; 胡文海

    2016-01-01

    On the basis of the geostatistical method and in combination with GIS technology, a research is being carried out on space distribution pattern and spatial correlation of regional tourist scenic spots in the very pa-per, with South Anhui international cultural tourism demonstration area as the studying sample. In the first place, it has calculated the semivariogram of the number of tourists in the tourist attractions which have been under monitor from 2009 to 2014 with the help of statistical data, so that a semivariogram model has been matched. After that, it starts to interpolate spatial data in it and draws the Kringing maps on number of tourists to the scenic spots in accordance with the optimal semivariogram model which has been matched. In the end, an analysis is done in the paper on the semivariogram and the kringing maps on the number of tourists to sce-nic spots in different specific years. In the light of the results coming from the above analysis, there exists a sig-nificant spatial correlation between the east part and the west part of South Anhui international cultural tourism demonstration area. In the initial stage, it appeared a clustering distribution in the area, while the spatial hetero-geneity being mainly affected by random factors which gradually have become weaker and weaker over time. On the whole, though the spatial level difference of the demonstration area is not significant, the"two moun-tains and one lake"area remains the core tourism sector and guides the development of regional tourism tend to be closer to it. From the perspective of locality, with Fangte Theme Park as the main sector, the Northeast ar-ea has a tendency to become a demonstration zone portal and the attractions around the Huangshan Mountain are closely linked together in space. However, Anqing City has not fully been integrated into the tourism devel-opment pattern of the demonstration area with distance as the major possible influencing factor. In summary, the

  16. Timescape: a simple space-time interpolation geostatistical Algorithm

    Science.gov (United States)

    Ciolfi, Marco; Chiocchini, Francesca; Gravichkova, Olga; Pisanelli, Andrea; Portarena, Silvia; Scartazza, Andrea; Brugnoli, Enrico; Lauteri, Marco

    2016-04-01

    Environmental sciences include both time and space variability in their datasets. Some established tools exist for both spatial interpolation and time series analysis alone, but mixing space and time variability calls for compromise: Researchers are often forced to choose which is the main source of variation, neglecting the other. We propose a simple algorithm, which can be used in many fields of Earth and environmental sciences when both time and space variability must be considered on equal grounds. The algorithm has already been implemented in Java language and the software is currently available at https://sourceforge.net/projects/timescapeglobal/ (it is published under GNU-GPL v3.0 Free Software License). The published version of the software, Timescape Global, is focused on continent- to Earth-wide spatial domains, using global longitude-latitude coordinates for samples localization. The companion Timescape Local software is currently under development ad will be published with an open license as well; it will use projected coordinates for a local to regional space scale. The basic idea of the Timescape Algorithm consists in converting time into a sort of third spatial dimension, with the addition of some causal constraints, which drive the interpolation including or excluding observations according to some user-defined rules. The algorithm is applicable, as a matter of principle, to anything that can be represented with a continuous variable (a scalar field, technically speaking). The input dataset should contain position, time and observed value of all samples. Ancillary data can be included in the interpolation as well. After the time-space conversion, Timescape follows basically the old-fashioned IDW (Inverse Distance Weighted) interpolation Algorithm, although users have a wide choice of customization options that, at least partially, overcome some of the known issues of IDW. The three-dimensional model produced by the Timescape Algorithm can be explored in many ways, including the extraction of time series at fixed locations and GIS layers at constant times, allowing for the inclusion of the model in the users' established workflow. The software requirements are relatively modest since it has been purposely designed for potential users in various research field with a limited computing power at their disposal. Any respectful modern PC or laptop can run it. Users however need a separate database for sample data and models storage because these can be quite bulky in terms of data output: a single model can be composed of several billions of voxels (three-dimensional discrete cells, a sort of 3D pixels). Running times range from a few minutes for sketch models to some days of evaluation for a full-size model, depending on the users' hardware and model size.

  17. Geostatistical analysis of GPS trajectory data: Space-time densities

    NARCIS (Netherlands)

    Hengl, T.; van Loon, E.E.; Shamoun-Baranes, J.; Bouten, W.; Zhang, J.; Goodchild, M.F.

    2008-01-01

    Creation of density maps and estimation of home range is problematic for observations of animal movement at irregular intervals. We propose a technique to estimate space-time densities by separately modeling animal movement paths and velocities, both as continuous fields. First the length of traject

  18. Building on crossvalidation for increasing the quality of geostatistical modeling

    Science.gov (United States)

    Olea, R.A.

    2012-01-01

    The random function is a mathematical model commonly used in the assessment of uncertainty associated with a spatially correlated attribute that has been partially sampled. There are multiple algorithms for modeling such random functions, all sharing the requirement of specifying various parameters that have critical influence on the results. The importance of finding ways to compare the methods and setting parameters to obtain results that better model uncertainty has increased as these algorithms have grown in number and complexity. Crossvalidation has been used in spatial statistics, mostly in kriging, for the analysis of mean square errors. An appeal of this approach is its ability to work with the same empirical sample available for running the algorithms. This paper goes beyond checking estimates by formulating a function sensitive to conditional bias. Under ideal conditions, such function turns into a straight line, which can be used as a reference for preparing measures of performance. Applied to kriging, deviations from the ideal line provide sensitivity to the semivariogram lacking in crossvalidation of kriging errors and are more sensitive to conditional bias than analyses of errors. In terms of stochastic simulation, in addition to finding better parameters, the deviations allow comparison of the realizations resulting from the applications of different methods. Examples show improvements of about 30% in the deviations and approximately 10% in the square root of mean square errors between reasonable starting modelling and the solutions according to the new criteria. ?? 2011 US Government.

  19. An Interactive Bayesian Geostatistical Inverse Protocol for Hydraulic Tomography

    Science.gov (United States)

    2008-07-25

    Boise State University, Boise, Idaho, USA. 3Civil and Environmental Engineering , Stanford University, Stanford, California, USA. Copyright 2008 by...probabilistic approach acknowledges the nonuniqueness of the parameter estimation problem and incorporates uncertainty from multiple sources into the...compression and image focusing, Geophysics, 67(5), 1532–1541, doi:10.1190/1.1512749. Sharma, P. V. (1997), Environmental and Engineering Geophysics, Cam

  20. Geostatistical Evaluation of Natural Tree Regeneration of a Disturbed Forest

    Science.gov (United States)

    José Germán Flores Garnica; David Arturo Moreno Gonzalez; Juan de Dios Benavides Solorio

    2006-01-01

    The implementation of silvicultural strategies in a forest management has to guaranty forest sustainability, which is supported by an adequate regeneration. Therefore, quality and intensity of silvicultural practices is based on an accurate knowledge of the current spatial distribution of regeneration. At the same time, this regeneration is determined by the spatial...

  1. Reservoir Modeling Combining Geostatistics with Markov Chain Monte Carlo Inversion

    DEFF Research Database (Denmark)

    Zunino, Andrea; Lange, Katrine; Melnikova, Yulia;

    2014-01-01

    , multi-step forward model (rock physics and seismology) and to provide realistic estimates of uncertainties. To generate realistic models which represent samples of the prior distribution, and to overcome the high computational demand, we reduce the search space utilizing an algorithm drawn from...

  2. Application of Geostatistical Simulation to Enhance Satellite Image Products

    Science.gov (United States)

    Hlavka, Christine A.; Dungan, Jennifer L.; Thirulanambi, Rajkumar; Roy, David

    2004-01-01

    With the deployment of Earth Observing System (EOS) satellites that provide daily, global imagery, there is increasing interest in defining the limitations of the data and derived products due to its coarse spatial resolution. Much of the detail, i.e. small fragments and notches in boundaries, is lost with coarse resolution imagery such as the EOS MODerate-Resolution Imaging Spectroradiometer (MODIS) data. Higher spatial resolution data such as the EOS Advanced Spaceborn Thermal Emission and Reflection Radiometer (ASTER), Landsat and airborne sensor imagery provide more detailed information but are less frequently available. There are, however, both theoretical and analytical evidence that burn scars and other fragmented types of land covers form self-similar or self-affine patterns, that is, patterns that look similar when viewed at widely differing spatial scales. Therefore small features of the patterns should be predictable, at least in a statistical sense, with knowledge about the large features. Recent developments in fractal modeling for characterizing the spatial distribution of undiscovered petroleum deposits are thus applicable to generating simulations of finer resolution satellite image products. We will present example EOS products, analysis to investigate self-similarity, and simulation results.

  3. Geostatistical independent simulation of spatially correlated soil variables

    Science.gov (United States)

    Boluwade, Alaba; Madramootoo, Chandra A.

    2015-12-01

    The selection of best management practices to reduce soil and water pollution often requires estimation of soil properties. It is important to find an efficient and robust technique to simulate spatially correlated soils parameters. Co-kriging and co-simulation are techniques that can be used. These methods are limited in terms of computer simulation due to the problem of solving large co-kriging systems and difficulties in fitting a valid model of coregionalization. The order of complexity increases as the number of covariables increases. This paper presents a technique for the conditional simulation of a non-Gaussian vector random field on point support scale. The technique is termed Independent Component Analysis (ICA). The basic principle underlining ICA is the determination of a linear representation of non-Gaussian data so that the components are considered statistically independent. With such representation, it would be easy and more computationally efficient to develop direct variograms for the components. The process is presented in two stages. The first stage involves the ICA decomposition. The second stage involves sequential Gaussian simulation of the generated components (which are derived from the first stage). This technique was applied for spatially correlated extractable cations such as magnesium (Mg) and iron (Fe) in a Canadian watershed. This paper has a strong application in stochastic quantification of uncertainties of soil attributes in soil remediation and soil rehabilitation.

  4. GEOSTATISTICAL INTERPOLATION OF CHEMICAL CONCENTRATION. (R825689C037)

    Science.gov (United States)

    AbstractMeasurements of contaminant concentration at a hazardous waste site typically vary over many orders of magnitude and have highly skewed distributions. This work presents a practical methodology for the estimation of solute concentration contour maps and volume...

  5. Optimization-based multiple-point geostatistics: A sparse way

    Science.gov (United States)

    Kalantari, Sadegh; Abdollahifard, Mohammad Javad

    2016-10-01

    In multiple-point simulation the image should be synthesized consistent with the given training image and hard conditioning data. Existing sequential simulation methods usually lead to error accumulation which is hardly manageable in future steps. Optimization-based methods are capable of handling inconsistencies by iteratively refining the simulation grid. In this paper, the multiple-point stochastic simulation problem is formulated in an optimization-based framework using a sparse model. Sparse model allows each patch to be constructed as a superposition of a few atoms of a dictionary formed using training patterns, leading to a significant increase in the variability of the patches. To control the creativity of the model, a local histogram matching method is proposed. Furthermore, effective solutions are proposed for different issues arisen in multiple-point simulation. In order to handle hard conditioning data a weighted matching pursuit method is developed in this paper. Moreover, a simple and efficient thresholding method is developed which allows working with categorical variables. The experiments show that the proposed method produces acceptable realizations in terms of pattern reproduction, increases the variability of the realizations, and properly handles numerous conditioning data.

  6. Penalized maximum likelihood estimation and variable selection in geostatistics

    CERN Document Server

    Chu, Tingjin; Wang, Haonan; 10.1214/11-AOS919

    2012-01-01

    We consider the problem of selecting covariates in spatial linear models with Gaussian process errors. Penalized maximum likelihood estimation (PMLE) that enables simultaneous variable selection and parameter estimation is developed and, for ease of computation, PMLE is approximated by one-step sparse estimation (OSE). To further improve computational efficiency, particularly with large sample sizes, we propose penalized maximum covariance-tapered likelihood estimation (PMLE$_{\\mathrm{T}}$) and its one-step sparse estimation (OSE$_{\\mathrm{T}}$). General forms of penalty functions with an emphasis on smoothly clipped absolute deviation are used for penalized maximum likelihood. Theoretical properties of PMLE and OSE, as well as their approximations PMLE$_{\\mathrm{T}}$ and OSE$_{\\mathrm{T}}$ using covariance tapering, are derived, including consistency, sparsity, asymptotic normality and the oracle properties. For covariance tapering, a by-product of our theoretical results is consistency and asymptotic normal...

  7. Characterizing regional soil mineral composition using spectroscopyand geostatistics

    Science.gov (United States)

    Mulder, V.L.; de Bruin, S.; Weyermann, J.; Kokaly, Raymond F.; Schaepman, M.E.

    2013-01-01

    This work aims at improving the mapping of major mineral variability at regional scale using scale-dependent spatial variability observed in remote sensing data. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and statistical methods were combined with laboratory-based mineral characterization of field samples to create maps of the distributions of clay, mica and carbonate minerals and their abundances. The Material Identification and Characterization Algorithm (MICA) was used to identify the spectrally-dominant minerals in field samples; these results were combined with ASTER data using multinomial logistic regression to map mineral distributions. X-ray diffraction (XRD)was used to quantify mineral composition in field samples. XRD results were combined with ASTER data using multiple linear regression to map mineral abundances. We testedwhether smoothing of the ASTER data to match the scale of variability of the target sample would improve model correlations. Smoothing was donewith Fixed Rank Kriging (FRK) to represent the mediumand long-range spatial variability in the ASTER data. Stronger correlations resulted using the smoothed data compared to results obtained with the original data. Highest model accuracies came from using both medium and long-range scaled ASTER data as input to the statistical models. High correlation coefficients were obtained for the abundances of calcite and mica (R2 = 0.71 and 0.70, respectively). Moderately-high correlation coefficients were found for smectite and kaolinite (R2 = 0.57 and 0.45, respectively). Maps of mineral distributions, obtained by relating ASTER data to MICA analysis of field samples, were found to characterize major soil mineral variability (overall accuracies for mica, smectite and kaolinite were 76%, 89% and 86% respectively). The results of this study suggest that the distributions of minerals and their abundances derived using FRK-smoothed ASTER data more closely match the spatial variability of soil and environmental properties at regional scale.

  8. Joint space-time geostatistical model for air quality surveillance

    Science.gov (United States)

    Russo, A.; Soares, A.; Pereira, M. J.

    2009-04-01

    Air pollution and peoples' generalized concern about air quality are, nowadays, considered to be a global problem. Although the introduction of rigid air pollution regulations has reduced pollution from industry and power stations, the growing number of cars on the road poses a new pollution problem. Considering the characteristics of the atmospheric circulation and also the residence times of certain pollutants in the atmosphere, a generalized and growing interest on air quality issues led to research intensification and publication of several articles with quite different levels of scientific depth. As most natural phenomena, air quality can be seen as a space-time process, where space-time relationships have usually quite different characteristics and levels of uncertainty. As a result, the simultaneous integration of space and time is not an easy task to perform. This problem is overcome by a variety of methodologies. The use of stochastic models and neural networks to characterize space-time dispersion of air quality is becoming a common practice. The main objective of this work is to produce an air quality model which allows forecasting critical concentration episodes of a certain pollutant by means of a hybrid approach, based on the combined use of neural network models and stochastic simulations. A stochastic simulation of the spatial component with a space-time trend model is proposed to characterize critical situations, taking into account data from the past and a space-time trend from the recent past. To identify near future critical episodes, predicted values from neural networks are used at each monitoring station. In this paper, we describe the design of a hybrid forecasting tool for ambient NO2 concentrations in Lisbon, Portugal.

  9. SEQUENTIAL KRIGING AND COKRIGING: TWO POWERFUL GEOSTATISTICAL APPROACHES. (R827114)

    Science.gov (United States)

    The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

  10. Top-kriging – geostatistics on stream networks

    Directory of Open Access Journals (Sweden)

    J. O. Skøien

    2005-11-01

    Full Text Available We present Top-kriging, or topological kriging, as a method for estimating streamflow-related variables in ungauged catchments. It takes both the area and the nested nature of catchments into account. The main appeal of the method is that it is a best linear unbiased estimator (BLUE adapted for the case of stream networks without any additional assumptions. The concept builds on the work of Sauquet et al. (2000 and extends it in a number of ways. We test the method for the case of the specific 100-year flood for two Austrian regions. The method provides more plausible and, indeed, more accurate estimates than Ordinary Kriging. Top-kriging also provides estimates of the uncertainty of the variable of interest. On the main stream the estimated uncertainties are smallest and they gradually increase as one moves towards the headwaters. The method as presented here is able to exploit the information contained in short records by accounting for the uncertainty of each gauge. We suggest that Top-kriging can be used for spatially interpolating a range of streamflow-related variables including mean annual discharge, flood characteristics, low flow characteristics, concentrations, turbidity and stream temperature.

  11. Characterizing regional soil mineral composition using spectroscopy and geostatistics

    NARCIS (Netherlands)

    Mulder, V.L.; Bruin, de S.; Weyermann, J.; Kokaly, R.F.; Schaepman, M.E.

    2013-01-01

    This work aims at improving the mapping of major mineral variability at regional scale using scale-dependent spatial variability observed in remote sensing data. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and statistical methods were combined with laboratory-based

  12. Estimating malaria burden in Nigeria: a geostatistical modelling approach

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    Nnadozie Onyiri

    2015-11-01

    Full Text Available This study has produced a map of malaria prevalence in Nigeria based on available data from the Mapping Malaria Risk in Africa (MARA database, including all malaria prevalence surveys in Nigeria that could be geolocated, as well as data collected during fieldwork in Nigeria between March and June 2007. Logistic regression was fitted to malaria prevalence to identify significant demographic (age and environmental covariates in STATA. The following environmental covariates were included in the spatial model: the normalized difference vegetation index, the enhanced vegetation index, the leaf area index, the land surface temperature for day and night, land use/landcover (LULC, distance to water bodies, and rainfall. The spatial model created suggests that the two main environmental covariates correlating with malaria presence were land surface temperature for day and rainfall. It was also found that malaria prevalence increased with distance to water bodies up to 4 km. The malaria risk map estimated from the spatial model shows that malaria prevalence in Nigeria varies from 20% in certain areas to 70% in others. The highest prevalence rates were found in the Niger Delta states of Rivers and Bayelsa, the areas surrounding the confluence of the rivers Niger and Benue, and also isolated parts of the north-eastern and north-western parts of the country. Isolated patches of low malaria prevalence were found to be scattered around the country with northern Nigeria having more such areas than the rest of the country. Nigeria’s belt of middle regions generally has malaria prevalence of 40% and above.

  13. A geostatistical approach for describing spatial pattern in stream networks

    Science.gov (United States)

    Ganio, L.M.; Torgersen, C.E.; Gresswell, R.E.

    2005-01-01

    The shape and configuration of branched networks influence ecological patterns and processes. Recent investigations of network influences in riverine ecology stress the need to quantify spatial structure not only in a two-dimensional plane, but also in networks. An initial step in understanding data from stream networks is discerning non-random patterns along the network. On the other hand, data collected in the network may be spatially autocorrelated and thus not suitable for traditional statistical analyses. Here we provide a method that uses commercially available software to construct an empirical variogram to describe spatial pattern in the relative abundance of coastal cutthroat trout in headwater stream networks. We describe the mathematical and practical considerations involved in calculating a variogram using a non-Euclidean distance metric to incorporate the network pathway structure in the analysis of spatial variability, and use a non-parametric technique to ascertain if the pattern in the empirical variogram is non-random.

  14. History Matching with Geostatistical Prior: A Smooth Formulation

    DEFF Research Database (Denmark)

    Melnikova, Yulia; Lange, Katrine; Zunino, Andrea;

    2014-01-01

    We present a new method for solving the history matching problem by gradient-based optimization within a probabilistic framework. The focus is on minimizing the number of forward simulations and conserving geological realism of the solutions. Geological a priori information is taken into account...

  15. Geostatistics and soil attributes in area cultivated with sugar cane

    OpenAIRE

    Zigomar Menezes de Souza; José Marques Júnior; Gener Tadeu Pereira

    2010-01-01

    Em solos sob cultivo de cana-de-açúcar, o tráfego intenso de máquinas agrícolas pode provocar estado de compactação ao solo. Portanto, o objetivo deste trabalho foi caracterizar a variabilidade espacial de atributos físicos e teor de matéria orgânica de um Latossolo Vermelho eutroférrico gibbsítico (sob Basalto) e Latossolo Vermelho distroférrico caulinítico (sob Arenito) nas profundidades de 0,0-0,2m e 0,2-0,4m, em áreas sob cultivo de cana-de-açúcar. Os solos foram amostrados nos pontos de ...

  16. Geostatistics as a basis to the CMLS pesticide simulation model with validation in soil columns Geoestatística como suporte ao modelo de simulação de agrotóxico CMLS com validação em colunas de solos

    Directory of Open Access Journals (Sweden)

    Gilberto Nicolella

    2005-01-01

    Full Text Available The use of simulation models is probably the most efficient means for predicting the behavior of pesticides in the soil-plant-water system. The CMLS (Chemical Movement in Layered Soils simulation model for predicting the fate of pesticides was used for studying the behavior of tebuthiuron, a herbicide used in sugar cane crops, from a sampling grid with 111 sampling points 200 m apart from one another and encompassing three types of soil: Ustic Quartzipsamment, Rhodic Hapludox and Typic Hapludox, all with medium and clay textures. The 373 points assessed by the simulator, generated from samples coming from the original grid and through the geostatistical methods of variography and ordinary kriging, returned the depth values reached by the herbicide after six years of simulation (1989-1995. For the Ustic Quartzipsamment, tebuthiuron, in four simulated points, returned depth values above 43 m and a maximum 50 m, with a certain amount of the product still remaining in the soil that was close to 10% of the original 1.1 kg ha-1 applied. Results from the column assay used for validating the model showed that the model overestimated the depth reached by the herbicide in 6.6% as compared to the column value for the Ustic Quartzipsamment. The depth was underestimated in 4.5% and 20% for the Typic Hapludox and the Rhodic Hapludox, respectively. These data support the adequacy of the model for assessing the fate of tebuthiuron in both Ustic Quartzipsamment and Typic Hapludox.O uso de modelos de simulação é provavelmente a maneira mais eficiente para predizer o comportamento de agrotóxicos no sistema solo/água/planta. O modelo de simulação de destino de agrotóxicos CMLS (Chemical Movement in Layered Soils, foi usado para estudar o comportamento do herbicida tebuthiuron, utilizado na cultura de cana-de-açúcar, a partir de uma grade de amostragem composta de 111 pontos amostrais, equi-espaçados de 200 m e englobando três tipos de solo: Neossolo

  17. Geostatistics and GIS analysis of spatial patterns of Myzus persicae and Hylyphantes graminicola in peach orchards under chemical pesticide stress%基于地统计学和 GIS 的化学农药胁迫下桃树桃蚜与草间钻头蛛种群空间格局

    Institute of Scientific and Technical Information of China (English)

    蒋杰贤; 万年峰; 季香云

    2015-01-01

    The overuse of chemical pesticides not only kills insect pests and natural enemies, but also affects the spatial relationship between insect pests and their natural enemies. Hylyphantes graminicola is a key predator of Myzus persicae in peach orchard eosystems. In this paper, we systematically investigated M. persicae and its predator H. graminicola populations in peach orchards under chemical pesticide stress at different times (from mid April to early September), used geostatistics and geographic information system (GIS) to analyze the spatial structure, and used ordinary Kriging interpretation with Gaussian, Exponential, Spherical and Circular models to simulate the spatial distribution of the two species.The aim of the study was to understand the spacial distribution of M. persicae and its predator H. graminicola under long-term applicaiton of chemical pestcides and provide the theoretical support for ecological control of peach garden pests. The results suggested that both H. graminicola and M. persicae had random spatial arrangement within 10 iterative times of investigations. The proportions of spatical sturcture [C0/(C+ C0)] of populations of H. graminicola and M. persicae were 0.788 8-0.983 9 and 0.811 6-0.980 6 indicating weak spacial relathship bewteen two populaitons under long-term chemical pestcide stress. The nugget values and partial sills of H. Graminicola, M. persicae were respectivel 0.254 2-4.896 3, 0.218 4-0.749 9 and 0.010 5-0.250 0, 0.004 8-0.075 7, respectively, indicating random spatial arrangement, too. Thecorrelation of spatial distribution distance of two species was relatively weak, the distance ranges for the two species was 6.863 0-43.174 1 m. Though the model parameters of semivariograms for M. persicae and H. graminicola at different times were changed greatly due to population density, temperature, and peach growth, the spatial patterns of the two populations were random under long-term chemical pesticide stress. Our study

  18. Aplicação de métodos geoestatísticos para identificação de dependência espacial na análise de dados de um ensaio de espaçamento florestal em delineamento sistemático tipo leque Application of geostatistical methods to identify spatial dependence in the data analysis of a forest spacing experiment with a fan systematic design

    Directory of Open Access Journals (Sweden)

    Melissa Oda-Souza

    2008-06-01

    arrangement (non-randomized of the plants and the high sensibility for missing values. The aim of this work was to describe the geostatistic model and associated methods of inference in the analysis context of non-randomized experiment, reporting applied results to identify the spatial dependence in a fan systematic design of Eucalyptus dunnii. Furthermore, different alternatives for treating missing values that can occur from flaws and/or mortality of plants were proposed, analyzed and compared. Data were analyzed by three models that differed, with covariates, in the form of modeling missing data values. A semivariogram was built for each model, adjusting three correlation function models, being the parameters estimated through the maximum likelihood method and selected by the Akaike's criterion. These models, with and without the spatial component, were compared by the likelihood ratio test. The results showed that: (1 the covariates interacted positively with the response variable, avoiding data to be discarded; (2 the model comparison, with and without the spatial component, did not confirm the existence of dependence; (3 the incorporation of the spatial dependence structure into the observational models recovered the capacity to make valid inferences in the absence of randomization, overcoming operational problems and guaranteeing that the data can be subjected to classic analysis.

  19. Three-Dimensional Geostatistical Analysis of Rock Fracture Roughness and Its Degradation with Shearing

    Directory of Open Access Journals (Sweden)

    Nima Babanouri

    2013-12-01

    Full Text Available Three-dimensional surface geometry of rock discontinuities and its evolution with shearing are of great importance in understanding the deformability and hydro-mechanical behavior of rock masses. In the present research, surfaces of three natural rock fractures were digitized and studied before and after the direct shear test. The variography analysis of the surfaces indicated a strong non-linear trend in the data. Therefore, the spatial variability of rock fracture surfaces was decomposed to one deterministic component characterized by a base polynomial function, and one stochastic component described by the variogram of residuals. By using an image-processing technique, 343 damaged zones with different sizes, shapes, initial roughness characteristics, local stress fields, and asperity strength values were spatially located and clustered. In order to characterize the overall spatial structure of the degraded zones, the concept of ‘pseudo-zonal variogram’ was introduced. The results showed that the spatial continuity at the damage locations increased due to asperity degradation. The increase in the variogram range was anisotropic and tended to be higher in the shear direction; thus, the direction of maximum continuity rotated towards the shear direction. Finally, the regression-kriging method was used to reconstruct the morphology of the intact surfaces and degraded areas. The cross-validation error of interpolation for the damaged zones was found smaller than that obtained for the intact surface.

  20. Geostatistical model-based estimates of schistosomiasis prevalence among individuals aged = 20 years in West Africa

    DEFF Research Database (Denmark)

    Schur, Nadine; Hürlimann, Eveline; Garba, Amadou

    2011-01-01

    Schistosomiasis is a water-based disease that is believed to affect over 200 million people with an estimated 97% of the infections concentrated in Africa. However, these statistics are largely based on population re-adjusted data originally published by Utroska and colleagues more than 20 years...... ago. Hence, these estimates are outdated due to large-scale preventive chemotherapy programs, improved sanitation, water resources development and management, among other reasons. For planning, coordination, and evaluation of control activities, it is essential to possess reliable schistosomiasis...

  1. Characterization of Drain Surface Water: Environmental Profile, Degradation Level and Geo-statistic Monitoring

    Directory of Open Access Journals (Sweden)

    Muhammad Waseem Mumtaz

    2015-12-01

    Full Text Available The physico-chemical characterization of the surface water. Samples was carried out collected from nine sampling points of drain passing by the territory of Hafizabad city, Punjab, Pakistan. The water of drain is used by farmers for irrigation purposes in nearby agricultural fields. Twenty water quality parameters were evaluated in three turns and the results obtained were compared with the National Environmental Quality Standards (NEQS municipal and industrial effluents prescribed limits. The highly significant difference (p0.05 was noted for temperature, pH, electrical conductivity, hardness, calcium, sodium, chemical oxygen demand and chloride among water samples from different sampling points. Furthermore, the experimental results of different water quality parameters studied at nine sampling points of the drain were used and interpolated in ArcGIS 9.3 environment system using kriging techniques to obtain calculated values for the remaining locations of the Drain.

  2. Determining treatment needs at different spatial scales using geostatistical model-based risk estimates of schistosomiasis.

    Directory of Open Access Journals (Sweden)

    Nadine Schur

    Full Text Available BACKGROUND: After many years of neglect, schistosomiasis control is going to scale. The strategy of choice is preventive chemotherapy, that is the repeated large-scale administration of praziquantel (a safe and highly efficacious drug to at-risk populations. The frequency of praziquantel administration is based on endemicity, which usually is defined by prevalence data summarized at an arbitrarily chosen administrative level. METHODO