Comparative study of the geostatistical ore reserve estimation method over the conventional methods

International Nuclear Information System (INIS)

Kim, Y.C.; Knudsen, H.P.

1975-01-01

Part I contains a comprehensive treatment of the comparative study of the geostatistical ore reserve estimation method over the conventional methods. The conventional methods chosen for comparison were: (a) the polygon method, (b) the inverse of the distance squared method, and (c) a method similar to (b) but allowing different weights in different directions. Briefly, the overall result from this comparative study is in favor of the use of geostatistics in most cases because the method has lived up to its theoretical claims. A good exposition on the theory of geostatistics, the adopted study procedures, conclusions and recommended future research are given in Part I. Part II of this report contains the results of the second and the third study objectives, which are to assess the potential benefits that can be derived by the introduction of the geostatistical method to the current state-of-the-art in uranium reserve estimation method and to be instrumental in generating the acceptance of the new method by practitioners through illustrative examples, assuming its superiority and practicality. These are given in the form of illustrative examples on the use of geostatistics and the accompanying computer program user's guide

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...

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.

A Bayesian Markov geostatistical model for estimation of hydrogeological properties

International Nuclear Information System (INIS)

Rosen, L.; Gustafson, G.

1996-01-01

A geostatistical methodology based on Markov-chain analysis and Bayesian statistics was developed for probability estimations of hydrogeological and geological properties in the siting process of a nuclear waste repository. The probability estimates have practical use in decision-making on issues such as siting, investigation programs, and construction design. The methodology is nonparametric which makes it possible to handle information that does not exhibit standard statistical distributions, as is often the case for classified information. Data do not need to meet the requirements on additivity and normality as with the geostatistical methods based on regionalized variable theory, e.g., kriging. The methodology also has a formal way for incorporating professional judgments through the use of Bayesian statistics, which allows for updating of prior estimates to posterior probabilities each time new information becomes available. A Bayesian Markov Geostatistical Model (BayMar) software was developed for implementation of the methodology in two and three dimensions. This paper gives (1) a theoretical description of the Bayesian Markov Geostatistical Model; (2) a short description of the BayMar software; and (3) an example of application of the model for estimating the suitability for repository establishment with respect to the three parameters of lithology, hydraulic conductivity, and rock quality designation index (RQD) at 400--500 meters below ground surface in an area around the Aespoe Hard Rock Laboratory in southeastern Sweden

Geostatistical ore reserve estimation for a roll-front type uranium deposit (practitioner's guide)

International Nuclear Information System (INIS)

Kim, Y.C.; Knudsen, H.P.

1977-01-01

This report comprises two parts. Part I contains illustrative examples of each phase of a geostatistical study using a roll-front type uranium deposit. Part II contains five computer programs and comprehensive users' manuals for these programs which are necessary to make a practical geostatistical study

A comparison between geostatistical analyses and sedimentological studies at the Hartbeestfontien gold mine

International Nuclear Information System (INIS)

Magri, E.J.

1978-01-01

For life-of-mine planning, as well as for short- and medium-term planning of grades and mine layouts, it is extremely important to have a clear understanding of the patterns followed by the distribution of gold and uranium within the mining area. This study is an attempt to reconcile the geostatistical approach to the determination of ore-shoot directions, via an analysis of the spatial distribution of gold and uranium values, with the sedimentological approach, which is based on the direct measurement of geological features. For the routine geostatistical estimation of ore reserves, the Hartebeestfontein gold mine was divided into ll sections. In each of these sections, the ore-shoot directions were calculated for gold and uranium from the anisotropies disclosed by geostatistical variogram analyses. This study presents a comparison of these results with those obtained from direct geological measurements of paleo-current directions. The results suggest that geological and geostatistical studies could be of significant mutual benefit [af

Geostatistical methodology for waste optimization of contaminated premises - 59344

International Nuclear Information System (INIS)

Desnoyers, Yvon; Dubot, Didier

2012-01-01

The presented methodological study illustrates a Geo-statistical approach suitable for radiological evaluation in nuclear premises. The waste characterization is mainly focused on floor concrete surfaces. By modeling the spatial continuity of activities, Geo-statistics provide sound methods to estimate and map radiological activities, together with their uncertainty. The multivariate approach allows the integration of numerous surface radiation measurements in order to improve the estimation of activity levels from concrete samples. This way, a sequential and iterative investigation strategy proves to be relevant to fulfill the different evaluation objectives. Waste characterization is performed on risk maps rather than on direct interpolation maps (due to bias of the selection on kriging results). The use of several estimation supports (punctual, 1 m 2 , room) allows a relevant radiological waste categorization thanks to cost-benefit analysis according to the risk of exceeding a given activity threshold. Global results, mainly total activity, are similarly quantified to precociously lead the waste management for the dismantling and decommissioning project. This paper recalled the geo-statistics principles and demonstrated how this methodology provides innovative tools for the radiological evaluation of contaminated premises. The relevance of this approach relies on the presence of a spatial continuity for radiological contamination. In this case, geo-statistics provides reliable activity estimates, uncertainty quantification and risk analysis, which are essential decision-making tools for decommissioning and dismantling projects of nuclear installations. Waste characterization is then performed taking all relevant information into account: historical knowledge, surface measurements and samples. Thanks to the multivariate processing, the different investigation stages can be rationalized as regards quantity and positioning. Waste characterization is finally

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...

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...

Assessment and modeling of the groundwater hydrogeochemical quality parameters via geostatistical approaches

Science.gov (United States)

Karami, Shawgar; Madani, Hassan; Katibeh, Homayoon; Fatehi Marj, Ahmad

2018-03-01

Geostatistical methods are one of the advanced techniques used for interpolation of groundwater quality data. The results obtained from geostatistics will be useful for decision makers to adopt suitable remedial measures to protect the quality of groundwater sources. Data used in this study were collected from 78 wells in Varamin plain aquifer located in southeast of Tehran, Iran, in 2013. Ordinary kriging method was used in this study to evaluate groundwater quality parameters. According to what has been mentioned in this paper, seven main quality parameters (i.e. total dissolved solids (TDS), sodium adsorption ratio (SAR), electrical conductivity (EC), sodium (Na+), total hardness (TH), chloride (Cl-) and sulfate (SO4 2-)), have been analyzed and interpreted by statistical and geostatistical methods. After data normalization by Nscore method in WinGslib software, variography as a geostatistical tool to define spatial regression was compiled and experimental variograms were plotted by GS+ software. Then, the best theoretical model was fitted to each variogram based on the minimum RSS. Cross validation method was used to determine the accuracy of the estimated data. Eventually, estimation maps of groundwater quality were prepared in WinGslib software and estimation variance map and estimation error map were presented to evaluate the quality of estimation in each estimated point. Results showed that kriging method is more accurate than the traditional interpolation methods.

Constrained optimisation of spatial sampling : a geostatistical approach

NARCIS (Netherlands)

Groenigen, van J.W.

1999-01-01

Aims

This thesis aims at the development of optimal sampling strategies for geostatistical studies. Special emphasis is on the optimal use of ancillary data, such as co-related imagery, preliminary observations and historic knowledge. Although the object of all studies

Geostatistical interpolation for modelling SPT data in northern Izmir

Indian Academy of Sciences (India)

data scatter' stems from the natural randomness of the system under con- ... Geostatistical methods were originally used for ore reserve calculations by the ... ing grain size distribution, plasticity, strength parameters and water content, for ...

Benchmarking a geostatistical procedure for the homogenisation of annual precipitation series

Science.gov (United States)

Caineta, Júlio; Ribeiro, Sara; Henriques, Roberto; Soares, Amílcar; Costa, Ana Cristina

2014-05-01

The European project COST Action ES0601, Advances in homogenisation methods of climate series: an integrated approach (HOME), has brought to attention the importance of establishing reliable homogenisation methods for climate data. In order to achieve that, a benchmark data set, containing monthly and daily temperature and precipitation data, was created to be used as a comparison basis for the effectiveness of those methods. Several contributions were submitted and evaluated by a number of performance metrics, validating the results against realistic inhomogeneous data. HOME also led to the development of new homogenisation software packages, which included feedback and lessons learned during the project. Preliminary studies have suggested a geostatistical stochastic approach, which uses Direct Sequential Simulation (DSS), as a promising methodology for the homogenisation of precipitation data series. Based on the spatial and temporal correlation between the neighbouring stations, DSS calculates local probability density functions at a candidate station to detect inhomogeneities. The purpose of the current study is to test and compare this geostatistical approach with the methods previously presented in the HOME project, using surrogate precipitation series from the HOME benchmark data set. The benchmark data set contains monthly precipitation surrogate series, from which annual precipitation data series were derived. These annual precipitation series were subject to exploratory analysis and to a thorough variography study. The geostatistical approach was then applied to the data set, based on different scenarios for the spatial continuity. Implementing this procedure also promoted the development of a computer program that aims to assist on the homogenisation of climate data, while minimising user interaction. Finally, in order to compare the effectiveness of this methodology with the homogenisation methods submitted during the HOME project, the obtained results

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.

Data analysis for radiological characterisation: Geostatistical and statistical complementarity

International Nuclear Information System (INIS)

Desnoyers, Yvon; Dubot, Didier

2012-01-01

Radiological characterisation may cover a large range of evaluation objectives during a decommissioning and dismantling (D and D) project: removal of doubt, delineation of contaminated materials, monitoring of the decontamination work and final survey. At each stage, collecting relevant data to be able to draw the conclusions needed is quite a big challenge. In particular two radiological characterisation stages require an advanced sampling process and data analysis, namely the initial categorization and optimisation of the materials to be removed and the final survey to demonstrate compliance with clearance levels. On the one hand the latter is widely used and well developed in national guides and norms, using random sampling designs and statistical data analysis. On the other hand a more complex evaluation methodology has to be implemented for the initial radiological characterisation, both for sampling design and for data analysis. The geostatistical framework is an efficient way to satisfy the radiological characterisation requirements providing a sound decision-making approach for the decommissioning and dismantling of nuclear premises. The relevance of the geostatistical methodology relies on the presence of a spatial continuity for radiological contamination. Thus geo-statistics provides reliable methods for activity estimation, uncertainty quantification and risk analysis, leading to a sound classification of radiological waste (surfaces and volumes). This way, 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 or not) surface survey of the contamination is implemented on a regular grid. Finally, in order to assess activity levels and contamination depths, destructive samples are collected at several locations within the premises (based on the surface survey results) and analysed. Combined with

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.

Risk Assessment of Sediment Pollution Using Geostatistical Simulations

Science.gov (United States)

Golay, J.; Kanevski, M.

2012-04-01

Environmental monitoring networks (EMN) discreetly measure the intensities of continuous phenomena (e.g. pollution, temperature, etc.). Spatial prediction models, like kriging, are then used for modeling. But, they give rise to smooth representations of phenomena which leads to overestimations or underestimations of extreme values. Moreover, they do not reproduce the spatial variability of the original data and the corresponding uncertainties. When dealing with risk assessment, this is unacceptable, since extreme values must be retrieved and probabilities of exceeding given thresholds must be computed [Kanevski et al., 2009]. In order to overcome these obstacles, geostatistics provides another approach: conditional stochastic simulations. Here, the basic idea is to generate multiple estimates of variable values (e.g. pollution concentration) at every location of interest which are calculated as stochastic realizations of an unknown random function (see, for example, [Kanevski, 2008], where both theoretical concepts and real data case studies are presented in detail). Many algorithms implement this approach. The most widely used in spatial modeling are sequential Gaussian simulations/cosimulations, sequential indicator simulations/cosimulations and direct simulations. In the present study, several algorithms of geostatistical conditional simulations were applied on real data collected from Lake Geneva. The main objectives were to compare their effectiveness in reproducing global statistics (histograms, variograms) and the way they characterize the variability and uncertainty of the contamination patterns. The dataset is composed of 200 measurements of the contamination of the lake sediments by heavy metals (i.e. Cadmium, Mercury, Zinc, Copper, Titanium and Chromium). The results obtained show some differences highlighting that risk assessment can be influenced by the algorithm it relies on. Moreover, hybrid models based on machine learning algorithms and

Geostatistical methods for radiological evaluation and risk analysis of contaminated premises

International Nuclear Information System (INIS)

Desnoyers, Y.; Jeannee, N.; Chiles, J.P.; Dubot, D.

2009-01-01

Full text: At the end of process equipment dismantling, the complete decontamination of nuclear facilities requires the radiological assessment of residual activity levels of building structures. As stated by the IAEA, 'Segregation and characterization of contaminated materials are the key elements of waste minimization'. From this point of view, the set up of an appropriate evaluation methodology is of primordial importance. The radiological characterization of contaminated premises can be divided into three steps. First, the most exhaustive facility analysis provides historical, functional and qualitative information. Then, a systematic (exhaustive or not) control of the emergent signal is performed by means of in situ measurement methods such as surface control device combined with in situ gamma spectrometry. Besides, in order to assess the contamination depth, samples can be collected from boreholes at several locations within the premises and analyzed. Combined with historical information and emergent signal maps, such data improve and reinforce the preliminary waste zoning. 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 relevance of the geostatistical methodology relies on the presence of a spatial continuity for radiological contamination. In this case, geostatistics provides reliable methods for activity estimation, uncertainty quantification and risk analysis, which are essential decision-making tools for decommissioning and dismantling projects of nuclear installations. Besides, the ability of this geostatistical framework to provide answers to several key issues that generally occur during the clean-up preparation phase is discussed: How to optimise the investigation costs? How to deal with data quality issues? How to consistently take into account auxiliary information such as historical

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.

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

International Nuclear Information System (INIS)

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

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.

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...

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...... inverse problems, such as full-waveform inversion. Sequential Gibbs sampling is a method that allows efficient sampling of a priori probability densities described by geostatistical algorithms based on either two-point (e.g., Gaussian) or multiple-point statistics. We outline the theoretical framework......) 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...

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

Science.gov (United States)

Troldborg, Mads; Nowak, Wolfgang; Lange, Ida V.; Santos, Marta C.; Binning, Philip J.; Bjerg, Poul L.

2012-09-01

Mass discharge estimates are increasingly being used when assessing risks of groundwater contamination and designing remedial systems at contaminated sites. Such estimates are, however, rather uncertain as they integrate uncertain spatial distributions of both concentration and groundwater flow. 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, and (3) uncertain source zone and transport parameters. The method generates conditional realizations of the spatial flow and concentration distribution. An analytical macrodispersive transport solution is employed to simulate the mean concentration distribution, and a geostatistical model of the Box-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 flow and transport simulation coupled with geostatistical inversion. It may therefore be of practical relevance to practitioners compared to existing methods that are either too simple or computationally demanding. The method is demonstrated on a field site contaminated with chlorinated ethenes. For this site, we show that including a physically meaningful concentration trend and the cosimulation of hydraulic conductivity and hydraulic gradient across the transect helps constrain the mass discharge uncertainty. The number of sampling points required for accurate mass discharge estimation and the relative influence of different data types on mass discharge uncertainty is discussed.

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

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...... reflection profile. Furthermore, the inferred values of the subsurface global variance and the mean velocity have been corroborated with moisturecontent measurements, obtained gravimetrically from samples collected at the field site....

Geostatistical analysis of prevailing groundwater conditions and potential solute migration at Elstow, Bedfordshire

International Nuclear Information System (INIS)

MacKay, R.; Cooper, T.A.; Porter, J.D.; O'Connell, P.E.; Metcalfe, A.V.

1988-06-01

A geostatistical approach is applied in a study of the potential migration of contaminants from a hypothetical waste disposal facility near Elstow, Bedfordshire. A deterministic numerical model of groundwater flow in the Kellaways Sands formation and adjacent layers is coupled with geostatistical simulation of the heterogeneous transmissivity field of this principal formation. A particle tracking technique is used to predict the migration pathways for alternative realisations of flow. Alternative statistical descriptions of the spatial structure of the transmissivity field are implemented and the temporal and spatial distributions of escape of contaminants to the biosphere are investigated. (author)

Estimating Rainfall in Rodrigues by Geostatistics: (A) Theory | Proag ...

African Journals Online (AJOL)

This paper introduces the geostatistical method. Originally devised to treat problems that arise when conventional statistical theory is used in estimating changes in ore grade within a mine, it is, however, an abstract theory of statistical behaviour that is applicable to many circumstances in different areas of geology and other ...

Topsoil moisture mapping using geostatistical techniques under different Mediterranean climatic conditions.

Science.gov (United States)

Martínez-Murillo, J F; Hueso-González, P; Ruiz-Sinoga, J D

2017-10-01

Soil mapping has been considered as an important factor in the widening of Soil Science and giving response to many different environmental questions. Geostatistical techniques, through kriging and co-kriging techniques, have made possible to improve the understanding of eco-geomorphologic variables, e.g., soil moisture. This study is focused on mapping of topsoil moisture using geostatistical techniques under different Mediterranean climatic conditions (humid, dry and semiarid) in three small watersheds and considering topography and soil properties as key factors. A Digital Elevation Model (DEM) with a resolution of 1×1m was derived from a topographical survey as well as soils were sampled to analyzed soil properties controlling topsoil moisture, which was measured during 4-years. Afterwards, some topography attributes were derived from the DEM, the soil properties analyzed in laboratory, and the topsoil moisture was modeled for the entire watersheds applying three geostatistical techniques: i) ordinary kriging; ii) co-kriging considering as co-variate topography attributes; and iii) co-kriging ta considering as co-variates topography attributes and gravel content. The results indicated topsoil moisture was more accurately mapped in the dry and semiarid watersheds when co-kriging procedure was performed. The study is a contribution to improve the efficiency and accuracy of studies about the Mediterranean eco-geomorphologic system and soil hydrology in field conditions. Copyright © 2017 Elsevier B.V. All rights reserved.

Exploring prediction uncertainty of spatial data in geostatistical and machine learning Approaches

Science.gov (United States)

Klump, J. F.; Fouedjio, F.

2017-12-01

Geostatistical methods such as kriging with external drift as well as machine learning techniques such as quantile regression forest have been intensively used for modelling spatial data. In addition to providing predictions for target variables, both approaches are able to deliver a quantification of the uncertainty associated with the prediction at a target location. Geostatistical approaches are, by essence, adequate for providing such prediction uncertainties and their behaviour is well understood. However, they often require significant data pre-processing and rely on assumptions that are rarely met in practice. Machine learning algorithms such as random forest regression, on the other hand, require less data pre-processing and are non-parametric. This makes the application of machine learning algorithms to geostatistical problems an attractive proposition. The objective of this study is to compare kriging with external drift and quantile regression forest with respect to their ability to deliver reliable prediction uncertainties of spatial data. In our comparison we use both simulated and real world datasets. Apart from classical performance indicators, comparisons make use of accuracy plots, probability interval width plots, and the visual examinations of the uncertainty maps provided by the two approaches. By comparing random forest regression to kriging we found that both methods produced comparable maps of estimated values for our variables of interest. However, the measure of uncertainty provided by random forest seems to be quite different to the measure of uncertainty provided by kriging. In particular, the lack of spatial context can give misleading results in areas without ground truth data. These preliminary results raise questions about assessing the risks associated with decisions based on the predictions from geostatistical and machine learning algorithms in a spatial context, e.g. mineral exploration.

Multiobjective design of aquifer monitoring networks for optimal spatial prediction and geostatistical parameter estimation

Science.gov (United States)

Alzraiee, Ayman H.; Bau, Domenico A.; Garcia, Luis A.

2013-06-01

Effective sampling of hydrogeological systems is essential in guiding groundwater management practices. Optimal sampling of groundwater systems has previously been formulated based on the assumption that heterogeneous subsurface properties can be modeled using a geostatistical approach. Therefore, the monitoring schemes have been developed to concurrently minimize the uncertainty in the spatial distribution of systems' states and parameters, such as the hydraulic conductivity K and the hydraulic head H, and the uncertainty in the geostatistical model of system parameters using a single objective function that aggregates all objectives. However, it has been shown that the aggregation of possibly conflicting objective functions is sensitive to the adopted aggregation scheme and may lead to distorted results. In addition, the uncertainties in geostatistical parameters affect the uncertainty in the spatial prediction of K and H according to a complex nonlinear relationship, which has often been ineffectively evaluated using a first-order approximation. In this study, we propose a multiobjective optimization framework to assist the design of monitoring networks of K and H with the goal of optimizing their spatial predictions and estimating the geostatistical parameters of the K field. The framework stems from the combination of a data assimilation (DA) algorithm and a multiobjective evolutionary algorithm (MOEA). The DA algorithm is based on the ensemble Kalman filter, a Monte-Carlo-based Bayesian update scheme for nonlinear systems, which is employed to approximate the posterior uncertainty in K, H, and the geostatistical parameters of K obtained by collecting new measurements. Multiple MOEA experiments are used to investigate the trade-off among design objectives and identify the corresponding monitoring schemes. The methodology is applied to design a sampling network for a shallow unconfined groundwater system located in Rocky Ford, Colorado. Results indicate that

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.

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.

Geostatistical Borehole Image-Based Mapping of Karst-Carbonate Aquifer Pores.

Science.gov (United States)

Sukop, Michael C; Cunningham, Kevin J

2016-03-01

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. © 2015, National Ground Water Association.

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.

A geostatistical methodology to assess the accuracy of unsaturated flow models

International Nuclear Information System (INIS)

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

Overview and technical and practical aspects for use of geostatistics in hazardous-, toxic-, and radioactive-waste-site investigations

International Nuclear Information System (INIS)

Bossong, C.R.; Karlinger, M.R.; Troutman, B.M.; Vecchia, A.V.

1999-01-01

Technical and practical aspects of applying geostatistics are developed for individuals involved in investigation at hazardous-, toxic-, and radioactive-waste sites. Important geostatistical concepts, such as variograms and ordinary, universal, and indicator kriging, are described in general terms for introductory purposes and in more detail for practical applications. Variogram modeling using measured ground-water elevation data is described in detail to illustrate principles of stationarity, anisotropy, transformations, and cross validation. Several examples of kriging applications are described using ground-water-level elevations, bedrock elevations, and ground-water-quality data. A review of contemporary literature and selected public domain software associated with geostatistics also is provided, as is a discussion of alternative methods for spatial modeling, including inverse distance weighting, triangulation, splines, trend-surface analysis, and simulation

Geostatistics applied to estimation of uranium bearing ore reserves

International Nuclear Information System (INIS)

Urbina Galan, L.I.

1982-01-01

A computer assisted method for assessing uranium-bearing ore deposit reserves is analyzed. Determinations of quality-thickness, namely quality by thickness calculations of mineralization, were obtained by means of a mathematical method known as the theory of rational variables for each drill-hole layer. Geostatistical results were derived based on a Fortrand computer program on a DEC 20/40 system. (author)

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

International Nuclear Information System (INIS)

Li, B.G.; Cao, J.; Liu, W.X.; Shen, W.R.; Wang, X.J.; Tao, S.

2006-01-01

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

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.

Forecasting Interest Rates Using Geostatistical Techniques

Directory of Open Access Journals (Sweden)

Giuseppe Arbia

2015-11-01

Full Text Available Geostatistical spatial models are widely used in many applied fields to forecast data observed on continuous three-dimensional surfaces. We propose to extend their use to finance and, in particular, to forecasting yield curves. We present the results of an empirical application where we apply the proposed method to forecast Euro Zero Rates (2003–2014 using the Ordinary Kriging method based on the anisotropic variogram. Furthermore, a comparison with other recent methods for forecasting yield curves is proposed. The results show that the model is characterized by good levels of predictions’ accuracy and it is competitive with the other forecasting models considered.

Geostatistical risk estimation at waste disposal sites in the presence of hot spots

International Nuclear Information System (INIS)

Komnitsas, Kostas; Modis, Kostas

2009-01-01

The present paper aims to estimate risk by using geostatistics at the wider coal mining/waste disposal site of Belkovskaya, Tula region, in Russia. In this area the presence of hot spots causes a spatial trend in the mean value of the random field and a non-Gaussian data distribution. Prior to application of geostatistics, subtraction of trend and appropriate smoothing and transformation of the data into a Gaussian form were carried out; risk maps were then generated for the wider study area in order to assess the probability of exceeding risk thresholds. Finally, the present paper discusses the need for homogenization of soil risk thresholds regarding hazardous elements that will enhance reliability of risk estimation and enable application of appropriate rehabilitation actions in contaminated areas.

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.

Assessment of effectiveness of geologic isolation systems: geostatistical modeling of pore velocity

International Nuclear Information System (INIS)

Devary, J.L.; Doctor, P.G.

1981-06-01

A significant part of evaluating a geologic formation as a nuclear waste repository involves the modeling of contaminant transport in the surrounding media in the event the repository is breached. The commonly used contaminant transport models are deterministic. However, the spatial variability of hydrologic field parameters introduces uncertainties into contaminant transport predictions. This paper discusses the application of geostatistical techniques to the modeling of spatially varying hydrologic field parameters required as input to contaminant transport analyses. Kriging estimation techniques were applied to Hanford Reservation field data to calculate hydraulic conductivity and the ground-water potential gradients. These quantities were statistically combined to estimate the groundwater pore velocity and to characterize the pore velocity estimation error. Combining geostatistical modeling techniques with product error propagation techniques results in an effective stochastic characterization of groundwater pore velocity, a hydrologic parameter required for contaminant transport analyses

Delineating Hydrofacies Spatial Distribution by Integrating Ensemble Data Assimilation and Indicator Geostatistics

Energy Technology Data Exchange (ETDEWEB)

Song, Xuehang [Florida State Univ., Tallahassee, FL (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Chen, Xingyuan [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Ye, Ming [Florida State Univ., Tallahassee, FL (United States); Dai, Zhenxue [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Hammond, Glenn Edward [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-07-01

This study develops a new framework of facies-based data assimilation for characterizing spatial distribution of hydrofacies and estimating their associated hydraulic properties. This framework couples ensemble data assimilation with transition probability-based geostatistical model via a parameterization based on a level set function. The nature of ensemble data assimilation makes the framework efficient and flexible to be integrated with various types of observation data. The transition probability-based geostatistical model keeps the updated hydrofacies distributions under geological constrains. The framework is illustrated by using a two-dimensional synthetic study that estimates hydrofacies spatial distribution and permeability in each hydrofacies from transient head data. Our results show that the proposed framework can characterize hydrofacies distribution and associated permeability with adequate accuracy even with limited direct measurements of hydrofacies. Our study provides a promising starting point for hydrofacies delineation in complex real problems.

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.

Application of a computationally efficient geostatistical approach to characterizing variably spaced water-table data

International Nuclear Information System (INIS)

Quinn, J.J.

1996-01-01

Geostatistical analysis of hydraulic head data is useful in producing unbiased contour plots of head estimates and relative errors. However, at most sites being characterized, monitoring wells are generally present at different densities, with clusters of wells in some areas and few wells elsewhere. The problem that arises when kriging data at different densities is in achieving adequate resolution of the grid while maintaining computational efficiency and working within software limitations. For the site considered, 113 data points were available over a 14-mi 2 study area, including 57 monitoring wells within an area of concern of 1.5 mi 2 . Variogram analyses of the data indicate a linear model with a negligible nugget effect. The geostatistical package used in the study allows a maximum grid of 100 by 100 cells. Two-dimensional kriging was performed for the entire study area with a 500-ft grid spacing, while the smaller zone was modeled separately with a 100-ft spacing. In this manner, grid cells for the dense area and the sparse area remained small relative to the well separation distances, and the maximum dimensions of the program were not exceeded. The spatial head results for the detailed zone were then nested into the regional output by use of a graphical, object-oriented database that performed the contouring of the geostatistical output. This study benefitted from the two-scale approach and from very fine geostatistical grid spacings relative to typical data separation distances. The combining of the sparse, regional results with those from the finer-resolution area of concern yielded contours that honored the actual data at every measurement location. The method applied in this study can also be used to generate reproducible, unbiased representations of other types of spatial data

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

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

Modelling Geomechanical Heterogeneity of Rock Masses Using Direct and Indirect Geostatistical Conditional Simulation Methods

Science.gov (United States)

Eivazy, Hesameddin; Esmaieli, Kamran; Jean, Raynald

2017-12-01

An accurate characterization and modelling of rock mass geomechanical heterogeneity can lead to more efficient mine planning and design. Using deterministic approaches and random field methods for modelling rock mass heterogeneity is known to be limited in simulating the spatial variation and spatial pattern of the geomechanical properties. Although the applications of geostatistical techniques have demonstrated improvements in modelling the heterogeneity of geomechanical properties, geostatistical estimation methods such as Kriging result in estimates of geomechanical variables that are not fully representative of field observations. This paper reports on the development of 3D models for spatial variability of rock mass geomechanical properties using geostatistical conditional simulation method based on sequential Gaussian simulation. A methodology to simulate the heterogeneity of rock mass quality based on the rock mass rating is proposed and applied to a large open-pit mine in Canada. Using geomechanical core logging data collected from the mine site, a direct and an indirect approach were used to model the spatial variability of rock mass quality. The results of the two modelling approaches were validated against collected field data. The study aims to quantify the risks of pit slope failure and provides a measure of uncertainties in spatial variability of rock mass properties in different areas of the pit.

Preliminary evaluation of uranium deposits. A geostatistical study of drilling density in Wyoming solution fronts

International Nuclear Information System (INIS)

Sandefur, R.L.; Grant, D.C.

1976-01-01

Studies of a roll-front uranium deposit in Shirley Basin Wyoming indicate that preliminary evaluation of the reserve potential of an ore body is possible with less drilling than currently practiced in industry. Estimating ore reserves from sparse drilling is difficult because most reserve calculation techniques do not give the accuracy of the estimate. A study of several deposits with a variety of drilling densities shows that geostatistics consistently provides a method of assessing the accuracy of an ore reserve estimate. Geostatistics provides the geologist with an additional descriptive technique - one which is valuable in the economic assessment of a uranium deposit. Closely spaced drilling on past properties provides both geological and geometric insight into the occurrence of uranium in roll-front type deposits. Just as the geological insight assists in locating new ore bodies and siting preferential drill locations, the geometric insight can be applied mathematically to evaluate the accuracy of a new ore reserve estimate. By expressing the geometry in numerical terms, geostatistics extracts important geological characteristics and uses this information to aid in describing the unknown characteristics of a property. (author)

How to evaluate the risks of exceeding limits: geostatistical models and their application to air pollution

International Nuclear Information System (INIS)

Fouquet, Ch. de; Deraisme, J.; Bobbia, M.

2007-01-01

Geo-statistics is increasingly applied to the study of environmental risks in a variety of sectors, especially in the fields of soil decontamination and the evaluation of the risks due to air pollution. Geo-statistics offers a rigorous stochastic modeling approach that makes it possible to answer questions expressed in terms of uncertainty and risk. This article focusses on nonlinear geo-statistical methods, based on the Gaussian random function model, whose essential properties are summarised. We use two examples to characterize situations where direct and thus rapid methods provide appropriate solutions and cases that inevitably require more laborious simulation techniques. Exposure of the population of the Rouen metropolitan area to the risk of NO 2 pollution is assessed by simulations, but the surface area where the pollution exceeds the threshold limit can be easily estimated with nonlinear conditional expectation techniques. A second example is used to discuss the bias introduced by direct simulation, here of a percentile of daily SO 2 concentration for one year in the city of Le Havre; an operational solution is proposed. (authors)

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.

Geostatistical analyses and hazard assessment on soil lead in Silvermines area, Ireland

International Nuclear Information System (INIS)

McGrath, David; Zhang Chaosheng; Carton, Owen T.

2004-01-01

Spatial distribution and hazard assessment of soil lead in the mining site of Silvermines, Ireland, were investigated using statistics, geostatistics and geographic information system (GIS) techniques. Positively skewed distribution and possible outlying values of Pb and other heavy metals were observed. Box-Cox transformation was applied in order to achieve normality in the data set and to reduce the effect of outliers. Geostatistical analyses were carried out, including calculation of experimental variograms and model fitting. The ordinary point kriging estimates of Pb concentration were mapped. Kriging standard deviations were regarded as the standard deviations of the interpolated pixel values, and a second map was produced, that quantified the probability of Pb concentration higher than a threshold value of 1000 mg/kg. These maps provide valuable information for hazard assessment and for decision support. - A probability map was produced that was useful for hazard assessment and decision support

Geostatistical analyses and hazard assessment on soil lead in Silvermines area, Ireland

Energy Technology Data Exchange (ETDEWEB)

McGrath, David; Zhang Chaosheng; Carton, Owen T

2004-01-01

Spatial distribution and hazard assessment of soil lead in the mining site of Silvermines, Ireland, were investigated using statistics, geostatistics and geographic information system (GIS) techniques. Positively skewed distribution and possible outlying values of Pb and other heavy metals were observed. Box-Cox transformation was applied in order to achieve normality in the data set and to reduce the effect of outliers. Geostatistical analyses were carried out, including calculation of experimental variograms and model fitting. The ordinary point kriging estimates of Pb concentration were mapped. Kriging standard deviations were regarded as the standard deviations of the interpolated pixel values, and a second map was produced, that quantified the probability of Pb concentration higher than a threshold value of 1000 mg/kg. These maps provide valuable information for hazard assessment and for decision support. - A probability map was produced that was useful for hazard assessment and decision support.

Two-point versus multiple-point geostatistics: the ability of geostatistical methods to capture complex geobodies and their facies associations—an application to a channelized carbonate reservoir, southwest Iran

International Nuclear Information System (INIS)

Hashemi, Seyyedhossein; Javaherian, Abdolrahim; Ataee-pour, Majid; Khoshdel, Hossein

2014-01-01

Facies models try to explain facies architectures which have a primary control on the subsurface heterogeneities and the fluid flow characteristics of a given reservoir. In the process of facies modeling, geostatistical methods are implemented to integrate different sources of data into a consistent model. The facies models should describe facies interactions; the shape and geometry of the geobodies as they occur in reality. Two distinct categories of geostatistical techniques are two-point and multiple-point (geo) statistics (MPS). In this study, both of the aforementioned categories were applied to generate facies models. A sequential indicator simulation (SIS) and a truncated Gaussian simulation (TGS) represented two-point geostatistical methods, and a single normal equation simulation (SNESIM) selected as an MPS simulation representative. The dataset from an extremely channelized carbonate reservoir located in southwest Iran was applied to these algorithms to analyze their performance in reproducing complex curvilinear geobodies. The SNESIM algorithm needs consistent training images (TI) in which all possible facies architectures that are present in the area are included. The TI model was founded on the data acquired from modern occurrences. These analogies delivered vital information about the possible channel geometries and facies classes that are typically present in those similar environments. The MPS results were conditioned to both soft and hard data. Soft facies probabilities were acquired from a neural network workflow. In this workflow, seismic-derived attributes were implemented as the input data. Furthermore, MPS realizations were conditioned to hard data to guarantee the exact positioning and continuity of the channel bodies. A geobody extraction workflow was implemented to extract the most certain parts of the channel bodies from the seismic data. These extracted parts of the channel bodies were applied to the simulation workflow as hard data

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

A geostatistical approach to the change-of-support problem and variable-support data fusion in spatial analysis

Science.gov (United States)

Wang, Jun; Wang, Yang; Zeng, Hui

2016-01-01

A key issue to address in synthesizing spatial data with variable-support in spatial analysis and modeling is the change-of-support problem. We present an approach for solving the change-of-support and variable-support data fusion problems. This approach is based on geostatistical inverse modeling that explicitly accounts for differences in spatial support. The inverse model is applied here to produce both the best predictions of a target support and prediction uncertainties, based on one or more measurements, while honoring measurements. Spatial data covering large geographic areas often exhibit spatial nonstationarity and can lead to computational challenge due to the large data size. We developed a local-window geostatistical inverse modeling approach to accommodate these issues of spatial nonstationarity and alleviate computational burden. We conducted experiments using synthetic and real-world raster data. Synthetic data were generated and aggregated to multiple supports and downscaled back to the original support to analyze the accuracy of spatial predictions and the correctness of prediction uncertainties. Similar experiments were conducted for real-world raster data. Real-world data with variable-support were statistically fused to produce single-support predictions and associated uncertainties. The modeling results demonstrate that geostatistical inverse modeling can produce accurate predictions and associated prediction uncertainties. It is shown that the local-window geostatistical inverse modeling approach suggested offers a practical way to solve the well-known change-of-support problem and variable-support data fusion problem in spatial analysis and modeling.

Spatial analysis of groundwater levels using Fuzzy Logic and geostatistical tools

Science.gov (United States)

Theodoridou, P. G.; Varouchakis, E. A.; Karatzas, G. P.

2017-12-01

The spatial variability evaluation of the water table of an aquifer provides useful information in water resources management plans. 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 is very important for the optimal method performance. This work compares three different criteria to assess the theoretical variogram that fits to the experimental one: the Least Squares Sum method, the Akaike Information Criterion and the Cressie's Indicator. Moreover, variable distance metrics such as the Euclidean, Minkowski, Manhattan, Canberra and Bray-Curtis are applied to calculate the distance between the observation and the prediction points, that affects both the variogram calculation and the Kriging estimator. A Fuzzy Logic System is then applied to define the appropriate neighbors for each estimation point used in the Kriging algorithm. The two criteria used during the Fuzzy Logic process are the distance between observation and estimation points and the groundwater level value at each observation point. The proposed techniques are applied to a data set of 250 hydraulic head measurements distributed over an alluvial aquifer. The analysis showed that the Power-law variogram model and Manhattan distance metric within ordinary kriging provide the best results when the comprehensive geostatistical analysis process is applied. On the other hand, the Fuzzy Logic approach leads to a Gaussian variogram model and significantly improves the estimation performance. The two different variogram models can be explained in terms of a fractional Brownian motion approach and of aquifer behavior at local scale. Finally, maps of hydraulic head spatial variability and of predictions uncertainty are constructed for the area with the two different approaches comparing their advantages and drawbacks.

Applicability of geostatistical procedures for the evaluation of hydrogeological parameters of a fractured aquifer in the Ronneburg mine district

International Nuclear Information System (INIS)

Grasshoff, C.; Schetelig, K.; Tomschi, H.

1998-01-01

The following paper demonstrates, how a geostatistical approach can help interpolating hydrogeological parameters over a certain area. The basic elements developed by G. Matheron in the sixties are represented as the preconditions and assumptions, which provide the best results of the estimation. The variogram as the most important tool in geostatistics offers the opportunity to describe the correlating behaviour of a regionalized variable. Some kriging procedures are briefly introduced, which provide under varying circumstances estimating of non-measured values with the theoretical variogram-model. In the Ronneburg mine district 108 screened drill-holes could provide coefficients of hydraulic conductivity. These were interpolated with ordinary kriging over the whole investigation area. An error calculation was performed, which could prove the accuracy of the estimation. Short prospects point out some difficulties handling with geostatistic procedures and make suggestions for further investigations. (orig.) [de

Redesigning rain gauges network in Johor using geostatistics and simulated annealing

International Nuclear Information System (INIS)

Aziz, Mohd Khairul Bazli Mohd; Yusof, Fadhilah; Daud, Zalina Mohd; Yusop, Zulkifli; Kasno, Mohammad Afif

2015-01-01

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

Redesigning rain gauges network in Johor using geostatistics and simulated annealing

Science.gov (United States)

Aziz, Mohd Khairul Bazli Mohd; Yusof, Fadhilah; Daud, Zalina Mohd; Yusop, Zulkifli; Kasno, Mohammad Afif

2015-02-01

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.

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.

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

Science.gov (United States)

Troldborg, M.; Nowak, W.; Binning, P. J.; Bjerg, P. L.

2012-12-01

Estimates of mass discharge (mass/time) are increasingly being used when assessing risks of groundwater contamination and designing remedial systems at contaminated sites. Mass discharge estimates are, however, prone to rather large uncertainties as they integrate uncertain spatial distributions of both concentration and groundwater flow velocities. For risk assessments or any other decisions that are being based on mass discharge estimates, it is essential to address these uncertainties. We present a novel Bayesian geostatistical approach for quantifying the uncertainty of the mass discharge across 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 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 (including the uncertainty in covariance functions), ii) measurement uncertainty, and iii) uncertain source zone geometry and transport parameters. The method generates multiple equally likely realizations of the spatial flow and concentration distribution, which all honour the measured data at the control plane. The flow realizations are generated by analytical co-simulation of the hydraulic conductivity and the hydraulic gradient across the control plane. These realizations are made consistent with measurements of both hydraulic conductivity and head at the site. An analytical macro-dispersive transport solution is employed to simulate the mean concentration distribution across the control plane, and a geostatistical model of the Box-Cox transformed concentration data is used to simulate observed

Geostatistical radar-raingauge combination with nonparametric correlograms: methodological considerations and application in Switzerland

Science.gov (United States)

Schiemann, R.; Erdin, R.; Willi, M.; Frei, C.; Berenguer, M.; Sempere-Torres, D.

2011-05-01

Modelling spatial covariance is an essential part of all geostatistical methods. Traditionally, parametric semivariogram models are fit from available data. More recently, it has been suggested to use nonparametric correlograms obtained from spatially complete data fields. Here, both estimation techniques are compared. Nonparametric correlograms are shown to have a substantial negative bias. Nonetheless, when combined with the sample variance of the spatial field under consideration, they yield an estimate of the semivariogram that is unbiased for small lag distances. This justifies the use of this estimation technique in geostatistical applications. Various formulations of geostatistical combination (Kriging) methods are used here for the construction of hourly precipitation grids for Switzerland based on data from a sparse realtime network of raingauges and from a spatially complete radar composite. Two variants of Ordinary Kriging (OK) are used to interpolate the sparse gauge observations. In both OK variants, the radar data are only used to determine the semivariogram model. One variant relies on a traditional parametric semivariogram estimate, whereas the other variant uses the nonparametric correlogram. The variants are tested for three cases and the impact of the semivariogram model on the Kriging prediction is illustrated. For the three test cases, the method using nonparametric correlograms performs equally well or better than the traditional method, and at the same time offers great practical advantages. Furthermore, two variants of Kriging with external drift (KED) are tested, both of which use the radar data to estimate nonparametric correlograms, and as the external drift variable. The first KED variant has been used previously for geostatistical radar-raingauge merging in Catalonia (Spain). The second variant is newly proposed here and is an extension of the first. Both variants are evaluated for the three test cases as well as an extended evaluation

A geostatistical estimation of zinc grade in bore-core samples

International Nuclear Information System (INIS)

Starzec, A.

1987-01-01

Possibilities and preliminary results of geostatistical interpretation of the XRF determination of zinc in bore-core samples are considered. For the spherical model of the variogram the estimation variance of grade in a disk-shape sample (estimated from the grade on the circumference sample) is calculated. Variograms of zinc grade in core samples are presented and examples of the grade estimation are discussed. 4 refs., 7 figs., 1 tab. (author)

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. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Geostatistical evaluation of travel time uncertainties

International Nuclear Information System (INIS)

Devary, J.L.

1983-08-01

Data on potentiometric head and hydraulic conductivity, gathered from the Wolfcamp Formation of the Permian System, have exhibited tremendous spatial variability as a result of heterogeneities in the media and the presence of petroleum and natural gas deposits. Geostatistical data analysis and error propagation techniques (kriging and conditional simulation) were applied to determine the effect of potentiometric head uncertainties on radionuclide travel paths and travel times through the Wolfcamp Formation. Blok-average kriging was utilized to remove measurement error from potentiometric head data. The travel time calculations have been enhanced by the use of an inverse technique to determine the relative hydraulic conductivity along travel paths. In this way, the spatial variability of the hydraulic conductivity corresponding to streamline convergence and divergence may be included in the analysis. 22 references, 11 figures, 1 table

Monte Carlo Analysis of Reservoir Models Using Seismic Data and Geostatistical Models

Science.gov (United States)

Zunino, A.; Mosegaard, K.; Lange, K.; Melnikova, Y.; Hansen, T. M.

2013-12-01

We present a study on the analysis of petroleum reservoir models consistent with seismic data and geostatistical constraints performed on a synthetic reservoir model. Our aim is to invert directly for structure and rock bulk properties of the target reservoir zone. To infer the rock facies, porosity and oil saturation seismology alone is not sufficient but a rock physics model must be taken into account, which links the unknown properties to the elastic parameters. We then combine a rock physics model with a simple convolutional approach for seismic waves to invert the "measured" seismograms. To solve this inverse problem, we employ a Markov chain Monte Carlo (MCMC) method, because it offers the possibility to handle non-linearity, complex and multi-step forward models and provides realistic estimates of uncertainties. However, for large data sets the MCMC method may be impractical because of a very high computational demand. To face this challenge one strategy is to feed the algorithm with realistic models, hence relying on proper prior information. To address this problem, we utilize an algorithm drawn from geostatistics to generate geologically plausible models which represent samples of the prior distribution. The geostatistical algorithm learns the multiple-point statistics from prototype models (in the form of training images), then generates thousands of different models which are accepted or rejected by a Metropolis sampler. To further reduce the computation time we parallelize the software and run it on multi-core machines. The solution of the inverse problem is then represented by a collection of reservoir models in terms of facies, porosity and oil saturation, which constitute samples of the posterior distribution. We are finally able to produce probability maps of the properties we are interested in by performing statistical analysis on the collection of solutions.

A Comparison of Traditional, Step-Path, and Geostatistical Techniques in the Stability Analysis of a Large Open Pit

Science.gov (United States)

Mayer, J. M.; Stead, D.

2017-04-01

With the increased drive towards deeper and more complex mine designs, geotechnical engineers are often forced to reconsider traditional deterministic design techniques in favour of probabilistic methods. These alternative techniques allow for the direct quantification of uncertainties within a risk and/or decision analysis framework. However, conventional probabilistic practices typically discretize geological materials into discrete, homogeneous domains, with attributes defined by spatially constant random variables, despite the fact that geological media display inherent heterogeneous spatial characteristics. This research directly simulates this phenomenon using a geostatistical approach, known as sequential Gaussian simulation. The method utilizes the variogram which imposes a degree of controlled spatial heterogeneity on the system. Simulations are constrained using data from the Ok Tedi mine site in Papua New Guinea and designed to randomly vary the geological strength index and uniaxial compressive strength using Monte Carlo techniques. Results suggest that conventional probabilistic techniques have a fundamental limitation compared to geostatistical approaches, as they fail to account for the spatial dependencies inherent to geotechnical datasets. This can result in erroneous model predictions, which are overly conservative when compared to the geostatistical results.

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.

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.

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.

Geostatistical prediction of microbial water quality throughout a stream network using meteorology, land cover, and spatiotemporal autocorrelation.

Science.gov (United States)

Holcomb, David Andrew; Messier, Kyle P; Serre, Marc L; Rowny, Jakob G; Stewart, Jill R

2018-06-11

Predictive modeling is promising as an inexpensive tool to assess water quality. We developed geostatistical predictive models of microbial water quality that empirically modelled spatiotemporal autocorrelation in measured fecal coliform (FC) bacteria concentrations to improve prediction. We compared five geostatistical models featuring different autocorrelation structures, fit to 676 observations from 19 locations in North Carolina's Jordan Lake watershed using meteorological and land cover predictor variables. Though stream distance metrics (with and without flow-weighting) failed to improve prediction over the Euclidean distance metric, incorporating temporal autocorrelation substantially improved prediction over the space-only models. We predicted FC throughout the stream network daily for one year, designating locations "impaired", "unimpaired", or "unassessed" if the probability of exceeding the state standard was >90%, 10% but <90%, respectively. We could assign impairment status to more of the stream network on days any FC were measured, suggesting frequent sample-based monitoring remains necessary, though implementing spatiotemporal predictive models may reduce the number of concurrent sampling locations required to adequately assess water quality. Together, these results suggest that prioritizing sampling at different times and conditions using geographically sparse monitoring networks is adequate to build robust and informative geostatistical models of water quality impairment.

Optimal design of sampling and mapping schemes in the radiometric exploration of Chipilapa, El Salvador (Geo-statistics)

International Nuclear Information System (INIS)

Balcazar G, M.; Flores R, J.H.

1992-01-01

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)

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

Qualitative and quantitative comparison of geostatistical techniques of porosity prediction from the seismic and logging data: a case study from the Blackfoot Field, Alberta, Canada

Science.gov (United States)

Maurya, S. P.; Singh, K. H.; Singh, N. P.

2018-05-01

In present study, three recently developed geostatistical methods, single attribute analysis, multi-attribute analysis and probabilistic neural network algorithm have been used to predict porosity in inter well region for Blackfoot field, Alberta, Canada, an offshore oil field. These techniques make use of seismic attributes, generated by model based inversion and colored inversion techniques. The principle objective of the study is to find the suitable combination of seismic inversion and geostatistical techniques to predict porosity and identification of prospective zones in 3D seismic volume. The porosity estimated from these geostatistical approaches is corroborated with the well log porosity. The results suggest that all the three implemented geostatistical methods are efficient and reliable to predict the porosity but the multi-attribute and probabilistic neural network analysis provide more accurate and high resolution porosity sections. A low impedance (6000-8000 m/s g/cc) and high porosity (> 15%) zone is interpreted from inverted impedance and porosity sections respectively between 1060 and 1075 ms time interval and is characterized as reservoir. The qualitative and quantitative results demonstrate that of all the employed geostatistical methods, the probabilistic neural network along with model based inversion is the most efficient method for predicting porosity in inter well region.

Seismic forecast using geostatistics

International Nuclear Information System (INIS)

Grecu, Valeriu; Mateiciuc, Doru

2007-01-01

The main idea of this research direction consists in the special way of constructing a new type of mathematical function as being a correlation between a computed statistical quantity and another physical quantity. This type of function called 'position function' was taken over by the authors of this study in the field of seismology with the hope of solving - at least partially - the difficult problem of seismic forecast. The geostatistic method of analysis focuses on the process of energy accumulation in a given seismic area, completing this analysis by a so-called loading function. This function - in fact a temporal function - describes the process of energy accumulation during a seismic cycle from a given seismic area. It was possible to discover a law of evolution of the seismic cycles that was materialized in a so-called characteristic function. This special function will help us to forecast the magnitude and the occurrence moment of the largest earthquake in the analysed area. Since 2000, the authors have been evolving to a new stage of testing: real - time analysis, in order to verify the quality of the method. There were five large earthquakes forecasts. (authors)

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

International Nuclear Information System (INIS)

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

Geostatistical Analysis of Mesoscale Spatial Variability and Error in SeaWiFS and MODIS/Aqua Global Ocean Color Data

Science.gov (United States)

Glover, David M.; Doney, Scott C.; Oestreich, William K.; Tullo, Alisdair W.

2018-01-01

Mesoscale (10-300 km, weeks to months) physical variability strongly modulates the structure and dynamics of planktonic marine ecosystems via both turbulent advection and environmental impacts upon biological rates. Using structure function analysis (geostatistics), we quantify the mesoscale biological signals within global 13 year SeaWiFS (1998-2010) and 8 year MODIS/Aqua (2003-2010) chlorophyll a ocean color data (Level-3, 9 km resolution). We present geographical distributions, seasonality, and interannual variability of key geostatistical parameters: unresolved variability or noise, resolved variability, and spatial range. Resolved variability is nearly identical for both instruments, indicating that geostatistical techniques isolate a robust measure of biophysical mesoscale variability largely independent of measurement platform. In contrast, unresolved variability in MODIS/Aqua is substantially lower than in SeaWiFS, especially in oligotrophic waters where previous analysis identified a problem for the SeaWiFS instrument likely due to sensor noise characteristics. Both records exhibit a statistically significant relationship between resolved mesoscale variability and the low-pass filtered chlorophyll field horizontal gradient magnitude, consistent with physical stirring acting on large-scale gradient as an important factor supporting observed mesoscale variability. Comparable horizontal length scales for variability are found from tracer-based scaling arguments and geostatistical decorrelation. Regional variations between these length scales may reflect scale dependence of biological mechanisms that also create variability directly at the mesoscale, for example, enhanced net phytoplankton growth in coastal and frontal upwelling and convective mixing regions. Global estimates of mesoscale biophysical variability provide an improved basis for evaluating higher resolution, coupled ecosystem-ocean general circulation models, and data assimilation.

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

Use of geostatistics on broiler production for evaluation of different minimum ventilation systems during brooding phase

Directory of Open Access Journals (Sweden)

Thayla Morandi Ridolfi de Carvalho

2012-01-01

Full Text Available The objective of this research was to evaluate different minimum ventilation systems, in relation to air quality and thermal comfort using geostatistics in brooding phase. The minimum ventilation systems were: Blue House I: exhaust fans + curtain management (end of the building; Blue House II: exhaust fans + side curtain management; and Dark House: exhaust fans + flag. The climate variables evaluated were: dry bulb temperature, relative humidity, air velocity, carbon dioxide and ammonia concentration, during winter time, at 9 a.m., in 80 equidistant points in brooding area. Data were evaluated by geostatistic technique. The results indicate that Wider broiler houses (above 15.0 m width present the greatest ammonia and humidity concentration. Blue House II present the best results in relation to air quality. However, none of the studied broiler houses present an ideal thermal comfort.

Deep fracturing of granite bodies. Literature survey, geostructural and geostatistic investigations

International Nuclear Information System (INIS)

Bles, J.L.; Blanchin, R.

1986-01-01

This report deals with investigations about deep fracturing of granite bodies, which were performed within two cost-sharing contracts between the Commission of the European Communities, the Commissariat a l'Energie Atomique and the Bureau de Recherches Geologiques et Minieres. The aim of this work was to study the evolution of fracturing in granite from the surface to larger depths, so that guidelines can be identified in order to extrapolate, at depth, the data obtained from surface investigations. These guidelines could eventually be used for feasibility studies about radioactive waste disposal. The results of structural and geostatistic investigations about the St. Sylvestre granite, as well as the literature survey about fractures encountered in two long Alpine galleries (Mont-Blanc tunnel and Arc-Isere water gallery), in the 1000 m deep borehole at Auriat, and in the Bassies granite body (Pyrenees) are presented. These results show that, for radioactive waste disposal feasibility studies: 1. The deep state of fracturing in a granite body can be estimated from results obtained at the surface; 2. Studying only the large fault network would be insufficient, both for surface investigations and for studies in deep boreholes and/or in underground galleries; 3. It is necessary to study orientations and frequencies of small fractures, so that structural mapping and statistical/geostatistical methods can be used in order to identify zones of higher and lower fracturing

Ore reserve evalution, through geostatistical methods, in sector C-09, Pocos de Caldas, MG-Brazil

International Nuclear Information System (INIS)

Guerra, P.A.G.; Censi, A.C.; Marques, J.P.M.; Huijbregts, Ch.

1978-01-01

In sector C-09, Pocos de Caldas in the state of Minas Gerais, geostatistical techniques have been used to evaluate the tonnage of U 3 O 8 and associated minerals and to delimit ore from sterile areas. The calculation of reserve was based on borehole information including the results of chemical and/or radiometric analysis. Two-and three dimensional evalutions were made following the existing geological models. Initially, the evaluation was based on chemical analysis using the more classical geostatistical technique of kriging. This was followed by a second evaluation using the more recent technique of co-kriging which permited the incorporation of radiometric information in the calculations. The correlation between ore grade and radiometric was studied using the method of cross-covariance. Following restrictions imposed by mining considerations, a probabilistic selection was made of blocks of appropriate dimensions so as to evaluate the grade tonnage curve for each panel. (Author) [pt

A Geostatistical Approach to Indoor Surface Sampling Strategies

DEFF Research Database (Denmark)

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

1990-01-01

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...... 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...... 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...

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

International Nuclear Information System (INIS)

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. - Highlights: • The housing class is inserted into co-kriging via an indicator function. • Inserting the housing classes in a co-kriging improves predictions. • The housing class has a structured component in space. • A nested model is implemented into the multigaussian algorithm. • A collection of risk maps is merged into one to create RPA.

Evaluation of stationary and non-stationary geostatistical models for inferring hydraulic conductivity values at Aespoe

International Nuclear Information System (INIS)

La Pointe, P.R.

1994-11-01

This report describes the comparison of stationary and non-stationary geostatistical models for the purpose of inferring block-scale hydraulic conductivity values from packer tests at Aespoe. The comparison between models is made through the evaluation of cross-validation statistics for three experimental designs. The first experiment consisted of a 'Delete-1' test previously used at Finnsjoen. The second test consisted of 'Delete-10%' and the third test was a 'Delete-50%' test. Preliminary data analysis showed that the 3 m and 30 m packer test data can be treated as a sample from a single population for the purposes of geostatistical analyses. Analysis of the 3 m data does not indicate that there are any systematic statistical changes with depth, rock type, fracture zone vs non-fracture zone or other mappable factor. Directional variograms are ambiguous to interpret due to the clustered nature of the data, but do not show any obvious anisotropy that should be accounted for in geostatistical analysis. Stationary analysis suggested that there exists a sizeable spatially uncorrelated component ('Nugget Effect') in the 3 m data, on the order of 60% of the observed variance for the various models fitted. Four different nested models were automatically fit to the data. Results for all models in terms of cross-validation statistics were very similar for the first set of validation tests. Non-stationary analysis established that both the order of drift and the order of the intrinsic random functions is low. This study also suggests that conventional cross-validation studies and automatic variogram fitting are not necessarily evaluating how well a model will infer block scale hydraulic conductivity values. 20 refs, 20 figs, 14 tabs

Spatial distribution of Munida intermedia and M. sarsi (crustacea: Anomura) on the Galician continental shelf (NW Spain): Application of geostatistical analysis

Science.gov (United States)

Freire, J.; González-Gurriarán, E.; Olaso, I.

1992-12-01

Geostatistical methodology was used to analyse spatial structure and distribution of the epibenthic crustaceans Munida intermedia and M. sarsi within sets of data which had been collected during three survey cruises carried out on the Galician continental shelf (1983 and 1984). This study investigates the feasibility of using geostatistics for data collected according to traditional methods and of enhancing such methodology. The experimental variograms were calculated (pooled variance minus spatial covariance between samples taken one pair at a time vs. distance) and fitted to a 'spherical' model. The spatial structure model was used to estimate the abundance and distribution of the populations studied using the technique of kriging. The species display spatial structures, which are well marked during high density periods and in some areas (especially northern shelf). Geostatistical analysis allows identification of the density gradients in space as well as the patch grain along the continental shelf of 16-25 km diameter for M. intermedia and 12-20 km for M. sarsi. Patches of both species have a consistent location throughout the different cruises. As in other geographical areas, M. intermedia and M. sarsi usually appear at depths ranging from 200 to 500 m, with the highest densities in the continental shelf area located between Fisterra and Estaca de Bares. Althouh sampling was not originally designed specifically for geostatistics, this assay provides a measurement of spatial covariance, and shows variograms with variable structure depending on population density and geographical area. These ideas are useful in improving the design of future sampling cruises.

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

Soil properties show signifficant spatial variability at local, regional and continental scales. This is a challenge for life cycle impact assessment (LCIA) of metals, because fate, bioavailability and effect factors are controlled by environmental chemistry and can vary orders of magnitude...... 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...

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 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 present ecological study (i.e. analysis of

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

Downscaling remotely sensed imagery using area-to-point cokriging and multiple-point geostatistical simulation

Science.gov (United States)

Tang, Yunwei; Atkinson, Peter M.; Zhang, Jingxiong

2015-03-01

A cross-scale data integration method was developed and tested based on the theory of geostatistics and multiple-point geostatistics (MPG). The goal was to downscale remotely sensed images while retaining spatial structure by integrating images at different spatial resolutions. During the process of downscaling, a rich spatial correlation model in the form of a training image was incorporated to facilitate reproduction of similar local patterns in the simulated images. Area-to-point cokriging (ATPCK) was used as locally varying mean (LVM) (i.e., soft data) to deal with the change of support problem (COSP) for cross-scale integration, which MPG cannot achieve alone. Several pairs of spectral bands of remotely sensed images were tested for integration within different cross-scale case studies. The experiment shows that MPG can restore the spatial structure of the image at a fine spatial resolution given the training image and conditioning data. The super-resolution image can be predicted using the proposed method, which cannot be realised using most data integration methods. The results show that ATPCK-MPG approach can achieve greater accuracy than methods which do not account for the change of support issue.

Geostatistical Analysis Methods for Estimation of Environmental Data Homogeneity

Directory of Open Access Journals (Sweden)

Aleksandr Danilov

2018-01-01

Full Text Available The methodology for assessing the spatial homogeneity of ecosystems with the possibility of subsequent zoning of territories in terms of the degree of disturbance of the environment is considered in the study. The degree of pollution of the water body was reconstructed on the basis of hydrochemical monitoring data and information on the level of the technogenic load in one year. As a result, the greatest environmental stress zones were isolated and correct zoning using geostatistical analysis techniques was proved. Mathematical algorithm computing system was implemented in an object-oriented programming C #. A software application has been obtained that allows quickly assessing the scale and spatial localization of pollution during the initial analysis of the environmental situation.

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

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

Improved Assimilation of Streamflow and Satellite Soil Moisture with the Evolutionary Particle Filter and Geostatistical Modeling

Science.gov (United States)

Yan, Hongxiang; Moradkhani, Hamid; Abbaszadeh, Peyman

2017-04-01

Assimilation of satellite soil moisture and streamflow data into hydrologic models using has received increasing attention over the past few years. Currently, these observations are increasingly used to improve the model streamflow and soil moisture predictions. However, the performance of this land data assimilation (DA) system still suffers from two limitations: 1) satellite data scarcity and quality; and 2) particle weight degeneration. In order to overcome these two limitations, we propose two possible solutions in this study. First, the general Gaussian geostatistical approach is proposed to overcome the limitation in the space/time resolution of satellite soil moisture products thus improving their accuracy at uncovered/biased grid cells. Secondly, an evolutionary PF approach based on Genetic Algorithm (GA) and Markov Chain Monte Carlo (MCMC), the so-called EPF-MCMC, is developed to further reduce weight degeneration and improve the robustness of the land DA system. This study provides a detailed analysis of the joint and separate assimilation of streamflow and satellite soil moisture into a distributed Sacramento Soil Moisture Accounting (SAC-SMA) model, with the use of recently developed EPF-MCMC and the general Gaussian geostatistical approach. Performance is assessed over several basins in the USA selected from Model Parameter Estimation Experiment (MOPEX) and located in different climate regions. The results indicate that: 1) the general Gaussian approach can predict the soil moisture at uncovered grid cells within the expected satellite data quality threshold; 2) assimilation of satellite soil moisture inferred from the general Gaussian model can significantly improve the soil moisture predictions; and 3) in terms of both deterministic and probabilistic measures, the EPF-MCMC can achieve better streamflow predictions. These results recommend that the geostatistical model is a helpful tool to aid the remote sensing technique and the EPF-MCMC is a

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

, and (3) uncertain source zone and transport parameters. The method generates conditional realizations of the spatial flow and concentration distribution. An analytical macrodispersive transport solution is employed to simulate the mean concentration distribution, and a geostatistical model of the Box-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...

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.

Latin hypercube sampling and geostatistical modeling of spatial uncertainty in a spatially explicit forest landscape model simulation

Science.gov (United States)

Chonggang Xu; Hong S. He; Yuanman Hu; Yu Chang; Xiuzhen Li; Rencang Bu

2005-01-01

Geostatistical stochastic simulation is always combined with Monte Carlo method to quantify the uncertainty in spatial model simulations. However, due to the relatively long running time of spatially explicit forest models as a result of their complexity, it is always infeasible to generate hundreds or thousands of Monte Carlo simulations. Thus, it is of great...

Determining site-specific background level with geostatistics for remediation of heavy metals in neighborhood soils

OpenAIRE

Tammy M. Milillo; Gaurav Sinha; Joseph A. Gardella Jr.

2017-01-01

The choice of a relevant, uncontaminated site for the determination of site-specific background concentrations for pollutants is critical for planning remediation of a contaminated site. The guidelines used to arrive at concentration levels vary from state to state, complicating this process. The residential neighborhood of Hickory Woods in Buffalo, NY is an area where heavy metal concentrations and spatial distributions were measured to plan remediation. A novel geostatistics based decision ...

Indoor radon variations in central Iran and its geostatistical map

Science.gov (United States)

Hadad, Kamal; Mokhtari, Javad

2015-02-01

We present the results of 2 year indoor radon survey in 10 cities of Yazd province in Central Iran (covering an area of 80,000 km2). We used passive diffusive samplers with LATEX polycarbonate films as Solid State Nuclear Track Detector (SSNTD). This study carried out in central Iran where there are major minerals and uranium mines. Our results indicate that despite few extraordinary high concentrations, average annual concentrations of indoor radon are within ICRP guidelines. When geostatistical spatial distribution of radon mapped onto geographical features of the province it was observed that risk of high radon concentration increases near the Saqand, Bafq, Harat and Abarkooh cities, this depended on the elevation and vicinity of the ores and mines.

Geostatistical modeling of groundwater properties and assessment of their uncertainties

International Nuclear Information System (INIS)

Honda, Makoto; Yamamoto, Shinya; Sakurai, Hideyuki; Suzuki, Makoto; Sanada, Hiroyuki; Matsui, Hiroya; Sugita, Yutaka

2010-01-01

The distribution of groundwater properties is important for understanding of the deep underground hydrogeological environments. This paper proposes a geostatistical system for modeling the groundwater properties which have a correlation with the ground resistivity data obtained from widespread and exhaustive survey. That is, the methodology for the integration of resistivity data measured by various methods and the methodology for modeling the groundwater properties using the integrated resistivity data has been developed. The proposed system has also been validated using the data obtained in the Horonobe Underground Research Laboratory project. Additionally, the quantification of uncertainties in the estimated model has been tried by numerical simulations based on the data. As a result, the uncertainties of the proposal model have been estimated lower than other traditional model's. (author)

The use of sequential indicator simulation to characterize geostatistical uncertainty

International Nuclear Information System (INIS)

Hansen, K.M.

1992-10-01

Sequential indicator simulation (SIS) is a geostatistical technique designed to aid in the characterization of uncertainty about the structure or behavior of natural systems. This report discusses a simulation experiment designed to study the quality of uncertainty bounds generated using SIS. The results indicate that, while SIS may produce reasonable uncertainty bounds in many situations, factors like the number and location of available sample data, the quality of variogram models produced by the user, and the characteristics of the geologic region to be modeled, can all have substantial effects on the accuracy and precision of estimated confidence limits. It is recommended that users of SIS conduct validation studies for the technique on their particular regions of interest before accepting the output uncertainty bounds

Comparing the performance of geostatistical models with additional information from covariates for sewage plume characterization.

Science.gov (United States)

Del Monego, Maurici; Ribeiro, Paulo Justiniano; Ramos, Patrícia

2015-04-01

In this work, kriging with covariates is used to model and map the spatial distribution of salinity measurements gathered by an autonomous underwater vehicle in a sea outfall monitoring campaign aiming to distinguish the effluent plume from the receiving waters and characterize its spatial variability in the vicinity of the discharge. Four different geostatistical linear models for salinity were assumed, where the distance to diffuser, the west-east positioning, and the south-north positioning were used as covariates. Sample variograms were fitted by the Matèrn models using weighted least squares and maximum likelihood estimation methods as a way to detect eventual discrepancies. Typically, the maximum likelihood method estimated very low ranges which have limited the kriging process. So, at least for these data sets, weighted least squares showed to be the most appropriate estimation method for variogram fitting. The kriged maps show clearly the spatial variation of salinity, and it is possible to identify the effluent plume in the area studied. The results obtained show some guidelines for sewage monitoring if a geostatistical analysis of the data is in mind. It is important to treat properly the existence of anomalous values and to adopt a sampling strategy that includes transects parallel and perpendicular to the effluent dispersion.

Geostatistical and adjoint sensitivity techniques applied to a conceptual model of ground-water flow in the Paradox Basin, Utah

International Nuclear Information System (INIS)

Metcalfe, D.E.; Campbell, J.E.; RamaRao, B.S.; Harper, W.V.; Battelle Project Management Div., Columbus, OH)

1985-01-01

Sensitivity and uncertainty analysis are important components of performance assessment activities for potential high-level radioactive waste repositories. The application of geostatistical and adjoint sensitivity techniques to aid in the calibration of an existing conceptual model of ground-water flow is demonstrated for the Leadville Limestone in Paradox Basin, Utah. The geostatistical method called kriging is used to statistically analyze the measured potentiometric data for the Leadville. This analysis consists of identifying anomalous data and data trends and characterizing the correlation structure between data points. Adjoint sensitivity analysis is then performed to aid in the calibration of a conceptual model of ground-water flow to the Leadville measured potentiometric data. Sensitivity derivatives of the fit between the modeled Leadville potentiometric surface and the measured potentiometric data to model parameters and boundary conditions are calculated by the adjoint method. These sensitivity derivatives are used to determine which model parameter and boundary condition values should be modified to most efficiently improve the fit of modeled to measured potentiometric conditions

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

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.

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

International Nuclear Information System (INIS)

Flipo, Nicolas; Jeannee, Nicolas; Poulin, Michel; Even, Stephanie; Ledoux, Emmanuel

2007-01-01

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 . - Combined use of geostatistics and physically based modeling allows assessment of nitrate concentrations in aquifer systems

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

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

KAUST Repository

Jha, Sanjeev Kumar; Mariethoz, Gregoire; Evans, Jason P.; McCabe, Matthew

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.

Bridges between multiple-point geostatistics and texture synthesis: Review and guidelines for future research

Science.gov (United States)

Mariethoz, Gregoire; Lefebvre, Sylvain

2014-05-01

Multiple-Point Simulations (MPS) is a family of geostatistical tools that has received a lot of attention in recent years for the characterization of spatial phenomena in geosciences. It relies on the definition of training images to represent a given type of spatial variability, or texture. We show that the algorithmic tools used are similar in many ways to techniques developed in computer graphics, where there is a need to generate large amounts of realistic textures for applications such as video games and animated movies. Similarly to MPS, these texture synthesis methods use training images, or exemplars, to generate realistic-looking graphical textures. Both domains of multiple-point geostatistics and example-based texture synthesis present similarities in their historic development and share similar concepts. These disciplines have however remained separated, and as a result significant algorithmic innovations in each discipline have not been universally adopted. Texture synthesis algorithms present drastically increased computational efficiency, patterns reproduction and user control. At the same time, MPS developed ways to condition models to spatial data and to produce 3D stochastic realizations, which have not been thoroughly investigated in the field of texture synthesis. In this paper we review the possible links between these disciplines and show the potential and limitations of using concepts and approaches from texture synthesis in MPS. We also provide guidelines on how recent developments could benefit both fields of research, and what challenges remain open.

Use of geostatistics in high level radioactive waste repository site characterization

Energy Technology Data Exchange (ETDEWEB)

Doctor, P G [Pacific Northwest Laboratory, Richland, WA (USA)

1980-09-01

In evaluating and characterizing sites that are candidates for use as repositories for high-level radioactive waste, there is an increasing need to estimate the uncertainty in hydrogeologic data and in the quantities calculated from them. This paper discusses the use of geostatistical techniques to estimate hydrogeologic surfaces, such as the top of a basalt formation, and to provide a measure of the uncertainty in that estimate. Maps of the uncertainty estimate, called the kriging error, can be used to evaluate where new data should be taken to affect the greatest reduction in uncertainty in the estimated surface. The methods are illustrated on a set of site-characterization data; the top-of-basalt elevations at the Hanford Site near Richland, Washington.

Geostatistical analysis of potentiometric data in Wolfcamp aquifer of the Palo Duro Basin, Texas

International Nuclear Information System (INIS)

Harper, W.V.; Furr, J.M.

1986-04-01

This report details a geostatistical analysis of potentiometric data from the Wolfcamp aquifer in the Palo Duro Basin, Texas. Such an analysis is a part of an overall uncertainty analysis for a high-level waste repository in salt. Both an expected potentiometric surface and the associated standard error surface are produced. The Wolfcamp data are found to be well explained by a linear trend with a superimposed spherical semivariogram. A cross-validation of the analysis confirms this. In addition, the cross-validation provides a point-by-point check to test for possible anomalous data

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.

Geostatistical modelling of the spatial life history of post-larval deepwater hake Merluccius paradoxus in the Benguela Current Large Marine Ecosystem

DEFF Research Database (Denmark)

Jansen, T; Kristensen, K; Fairweather, T. P.

2017-01-01

paradoxus are not reflected in the current assessment and management practices for the Benguela Current Large Marine Ecosystem. In this study, we compiled data from multiple demersal trawl surveys from the entire distribution area and applied state-of the-art geostatistical population modelling (Geo...

Comparing the applicability of some geostatistical methods to predict the spatial distribution of topsoil Calcium Carbonate in part of farmland of Zanjan Province

Science.gov (United States)

Sarmadian, Fereydoon; Keshavarzi, Ali

2010-05-01

Most of soils in iran, were located in the arid and semi-arid regions and have high pH (more than 7) and high amount of calcium carbonate and this problem cause to their calcification.In calcareous soils, plant growing and production is difficult. Most part of this problem, in relation to high pH and high concentration of calcium ion that cause to fixation and unavailability of elements which were dependent to pH, especially Phosphorous and some micro nutrients such as Fe, Zn, Mn and Cu. Prediction of soil calcium carbonate in non-sampled areas and mapping the calcium carbonate variability in order to sustainable management of soil fertility is very important.So, this research was done with the aim of evaluation and analyzing spatial variability of topsoil calcium carbonate as an aspect of soil fertility and plant nutrition, comparing geostatistical methods such as kriging and co-kriging and mapping topsoil calcium carbonate. For geostatistical analyzing, sampling was done with stratified random method and soil samples from 0 to 15 cm depth were collected with auger within 23 locations.In co-kriging method, salinity data was used as auxiliary variable. For comparing and evaluation of geostatistical methods, cross validation were used by statistical parameters of RMSE. The results showed that co-kriging method has the highest correlation coefficient and less RMSE and has the higher accuracy than kriging method to prediction of calcium carbonate content in non-sampled areas.

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.

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)

Estimating the burden of malaria in Senegal: Bayesian zero-inflated binomial geostatistical modeling of the MIS 2008 data.

Directory of Open Access Journals (Sweden)

Federica Giardina

Full Text Available The Research Center for Human Development in Dakar (CRDH with the technical assistance of ICF Macro and the National Malaria Control Programme (NMCP conducted in 2008/2009 the Senegal Malaria Indicator Survey (SMIS, the first nationally representative household survey collecting parasitological data and malaria-related indicators. In this paper, we present spatially explicit parasitaemia risk estimates and number of infected children below 5 years. Geostatistical Zero-Inflated Binomial models (ZIB were developed to take into account the large number of zero-prevalence survey locations (70% in the data. Bayesian variable selection methods were incorporated within a geostatistical framework in order to choose the best set of environmental and climatic covariates associated with the parasitaemia risk. Model validation confirmed that the ZIB model had a better predictive ability than the standard Binomial analogue. Markov chain Monte Carlo (MCMC methods were used for inference. Several insecticide treated nets (ITN coverage indicators were calculated to assess the effectiveness of interventions. After adjusting for climatic and socio-economic factors, the presence of at least one ITN per every two household members and living in urban areas reduced the odds of parasitaemia by 86% and 81% respectively. Posterior estimates of the ORs related to the wealth index show a decreasing trend with the quintiles. Infection odds appear to be increasing with age. The population-adjusted prevalence ranges from 0.12% in Thillé-Boubacar to 13.1% in Dabo. Tambacounda has the highest population-adjusted predicted prevalence (8.08% whereas the region with the highest estimated number of infected children under the age of 5 years is Kolda (13940. The contemporary map and estimates of malaria burden identify the priority areas for future control interventions and provide baseline information for monitoring and evaluation. Zero-Inflated formulations are more appropriate

Bayesian geostatistical modeling of leishmaniasis incidence in Brazil.

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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.

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

and the hydraulic gradient across the control plane and are consistent with measurements of both hydraulic conductivity and head at the site. An analytical macro-dispersive transport solution is employed to simulate the mean concentration distribution across the control plane, and a geostatistical model of the Box-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. Tests show that the decoupled approach is both efficient and able to provide accurate uncertainty...

Model-Based Geostatistical Mapping of the Prevalence of Onchocerca volvulus in West Africa.

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Simon J O'Hanlon

2016-01-01

Full Text Available The initial endemicity (pre-control prevalence of onchocerciasis has been shown to be an important determinant of the feasibility of elimination by mass ivermectin distribution. We present the first geostatistical map of microfilarial prevalence in the former Onchocerciasis Control Programme in West Africa (OCP before commencement of antivectorial and antiparasitic interventions.Pre-control microfilarial prevalence data from 737 villages across the 11 constituent countries in the OCP epidemiological database were used as ground-truth data. These 737 data points, plus a set of statistically selected environmental covariates, were used in a Bayesian model-based geostatistical (B-MBG approach to generate a continuous surface (at pixel resolution of 5 km x 5km of microfilarial prevalence in West Africa prior to the commencement of the OCP. Uncertainty in model predictions was measured using a suite of validation statistics, performed on bootstrap samples of held-out validation data. The mean Pearson's correlation between observed and estimated prevalence at validation locations was 0.693; the mean prediction error (average difference between observed and estimated values was 0.77%, and the mean absolute prediction error (average magnitude of difference between observed and estimated values was 12.2%. Within OCP boundaries, 17.8 million people were deemed to have been at risk, 7.55 million to have been infected, and mean microfilarial prevalence to have been 45% (range: 2-90% in 1975.This is the first map of initial onchocerciasis prevalence in West Africa using B-MBG. Important environmental predictors of infection prevalence were identified and used in a model out-performing those without spatial random effects or environmental covariates. Results may be compared with recent epidemiological mapping efforts to find areas of persisting transmission. These methods may be extended to areas where data are sparse, and may be used to help inform the

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)

A geostatistical study of the uranium deposit at Kvanefjeld, the Ilimaussaq intrusion, South Greenland

International Nuclear Information System (INIS)

Lund Clausen, F.

1982-05-01

The uranium deposit at Kvanefjeld within the Ilimaussaq intrusion in South Greenland has been tested by diamond drilling, hole logging, chip sampling and field gamma-spectrometric surveys. Based on these different types of spatially distributed samples the uranium variation within the deposit was studied. The spatial variation, which comprises a large random component, was modelled, and the intrinsic function was used to establish gradetonnage curves by the best linear unbiased estimator of geostatistics (kriging). From data obtained by a ground surface gamma-spectrometric survey it is shown that the uranium variation is possibly subject to a spatial anisotropy consistent with the geology. The uranium variation has a second-order stationarity. A global estimation of the total reserves shows that single block grade values are always estimated with high errors. This is mainly caused by the poor spatial structure and the very sparse sampling pattern. The best way to solve this problem appears to be a selective type of kriging. The overall uranium reserves are estimated as 23600 tons with a mean grade of 297 ppm (cutoff grade 250 ppm U). Studies of data from a test adit show that local geostatistical estimation can be done with acceptably small errors provided that a close sampling pattern is used. A regression relationship is established to correct field gamma-spectrometric measures of bulk grades towards truer values. Multivariate cluster and discriminant analyses were used to classify lujavrite samples based on their trace element content. Misclassification is due to a possibly continuous transition between naujakasite lujavrite and arfvedsonite lujavrite. Some of the main mineralogical differences between the geological units are identified by the discriminating effect of the individual variable. (author)

Evaluating the effect of sampling and spatial correlation on ground-water travel time uncertainty coupling geostatistical, stochastic, and first order, second moment methods

International Nuclear Information System (INIS)

Andrews, R.W.; LaVenue, A.M.; McNeish, J.A.

1989-01-01

Ground-water travel time predictions at potential high-level waste repositories are subject to a degree of uncertainty due to the scale of averaging incorporated in conceptual models of the ground-water flow regime as well as the lack of data on the spatial variability of the hydrogeologic parameters. The present study describes the effect of limited observations of a spatially correlated permeability field on the predicted ground-water travel time uncertainty. Varying permeability correlation lengths have been used to investigate the importance of this geostatistical property on the tails of the travel time distribution. This study uses both geostatistical and differential analysis techniques. Following the generation of a spatially correlated permeability field which is considered reality, semivariogram analyses are performed upon small random subsets of the generated field to determine the geostatistical properties of the field represented by the observations. Kriging is then employed to generate a kriged permeability field and the corresponding standard deviation of the estimated field conditioned by the limited observations. Using both the real and kriged fields, the ground-water flow regime is simulated and ground-water travel paths and travel times are determined for various starting points. These results are used to define the ground-water travel time uncertainty due to path variability. The variance of the ground-water travel time along particular paths due to the variance of the permeability field estimated using kriging is then calculated using the first order, second moment method. The uncertainties in predicted travel time due to path and parameter uncertainties are then combined into a single distribution

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.

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

Directory of Open Access Journals (Sweden)

Xujun Han

Full Text Available 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.

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.

DEM-based delineation for improving geostatistical interpolation of rainfall in mountainous region of Central Himalayas, India

Science.gov (United States)

Kumari, Madhuri; Singh, Chander Kumar; Bakimchandra, Oinam; Basistha, Ashoke

2017-10-01

In mountainous region with heterogeneous topography, the geostatistical modeling of the rainfall using global data set may not confirm to the intrinsic hypothesis of stationarity. This study was focused on improving the precision of the interpolated rainfall maps by spatial stratification in complex terrain. Predictions of the normal annual rainfall data were carried out by ordinary kriging, universal kriging, and co-kriging, using 80-point observations in the Indian Himalayas extending over an area of 53,484 km2. A two-step spatial clustering approach is proposed. In the first step, the study area was delineated into two regions namely lowland and upland based on the elevation derived from the digital elevation model. The delineation was based on the natural break classification method. In the next step, the rainfall data was clustered into two groups based on its spatial location in lowland or upland. The terrain ruggedness index (TRI) was incorporated as a co-variable in co-kriging interpolation algorithm. The precision of the kriged and co-kriged maps was assessed by two accuracy measures, root mean square error and Chatfield's percent better. It was observed that the stratification of rainfall data resulted in 5-20 % of increase in the performance efficiency of interpolation methods. Co-kriging outperformed the kriging models at annual and seasonal scale. The result illustrates that the stratification of the study area improves the stationarity characteristic of the point data, thus enhancing the precision of the interpolated rainfall maps derived using geostatistical methods.

Effective property determination for input to a geostatistical model of regional groundwater flow: Wellenberg T→K

International Nuclear Information System (INIS)

Lanyon, G.W.; Marschall, P.; Vomvoris, S.; Jaquet, O.; Mazurek, M.

1998-01-01

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

Geostatistical mapping of Cs-137 contamination depth in building structures by integrating ISOCS measurements of different spatial supports

Energy Technology Data Exchange (ETDEWEB)

Boden, S.; Jacques, D. [Institute for Environment, Health and Safety, Belgian Nuclear Research Centre (SCK-CEN), Boeretang 200, BE-2400, Mol (Belgium); Rogiers, B. [Dept. of Earth and Environmental Sciences, KU Leuven - University of Leuven Celestijnenlaan 200e - bus 2410, BE-3001, Leuven (Belgium)

2013-07-01

Reliable methods to determine the contamination depth in nuclear building structures are very much needed for minimizing the radioactive waste volume and the decontamination workload. This paper investigates the geostatistical integration of in situ gamma-ray spectroscopy measurements of different spatial supports. A case study is presented from the BR3 decommissioning project, yielding an estimated reduction of waste volume of ∼35%, and recommendations are made for future application of the proposed methodology. (authors)

Applicability of geostatistical methods and optimization of data for assessing hydraulic and geological conditions as a basis for remediation measures in the Ronneburg ore mining district

International Nuclear Information System (INIS)

Post, C.

2001-01-01

The remediation of the former Wismut mines in Thuringia has been planed and prepared since 1990. Objects of remediation are mines, tailing ponds and waste rock piles. Since more than 40 years of mining have had a great affect on the exploited aquifer, special emphasis is given to groundwater recharge so that minery-flooding is one of the conceivable remedial options. Controlled flooding supports minimising the expanded oxidation zone, which renders an immense pollutant potential, while at the same time the flooding reduces the quantity of acid mine water, that has to be treated. One of the main tasks of modelling the flooding progress is to determine and prognosticate the wateroutlet-places. Due to the inadequacy of the database from the production period, limited accuracy of the available data and because of the inherent uncertainty of approximations used in numerical modelling, a stochastic approach is prospected. The flooding predictions, i.e. modelling of hydrodynamical and hydrochemical conditions during and after completion of flooding predominantly depend on the spatial distribution of the hydraulic conductivity. In order to get a better understanding of the spatial heterogeneity of the Palaeozoic fractured rock aquifer, certain geostatistical interpolation methods are tested to achieve the best approach for describing the hydrogeological parameters in space. This work deals in detail with two selected geostatistical interpolation methods (ordinary and indicator kriging) and discusses their applicability and limitations including the application of the presented case. Another important target is the specification of the database and the improvement of consistency with statistical standards. The main emphasis lies on the spatial distribution of the measured hydraulic conductivity coefficient, its estimation at non-measured places and the influence of its spatial variability on modelling results. This topic is followed by the calculation of the estimation

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

In low-permeability clay tills subsurface transport is governed by preferential flow in sand lenses and fractures. A proper geological model requires the integration of these features, i.e. the spatial distribution of the geological heterogeneities. Detailed mapping of sand lenses has been done...... 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....

Study on geological environment model using geostatistics method

International Nuclear Information System (INIS)

Honda, Makoto; Suzuki, Makoto; Sakurai, Hideyuki; Iwasa, Kengo; Matsui, Hiroya

2005-03-01

The purpose of this study is to develop the geostatistical procedure for modeling geological environments and to evaluate the quantitative relationship between the amount of information and the reliability of the model using the data sets obtained in the surface-based investigation phase (Phase 1) of the Horonobe Underground Research Laboratory Project. This study lasts for three years from FY2004 to FY2006 and this report includes the research in FY2005 as the second year of three-year study. In FY2005 research, the hydrogeological model was built as well as FY2004 research using the data obtained from the deep boreholes (HDB-6, 7 and 8) and the ground magnetotelluric (AMT) survey which were executed in FY2004 in addition to the data sets used in the first year of study. Above all, the relationship between the amount of information and the reliability of the model was demonstrated through a comparison of the models at each step which corresponds to the investigation stage in each FY. Furthermore, the statistical test was applied for detecting the difference of basic statistics of various data due to geological features with a view to taking the geological information into the modeling procedures. (author)

Geostatistical Investigations of Displacements on the Basis of Data from the Geodetic Monitoring of a Hydrotechnical Object

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Namysłowska-Wilczyńska, Barbara; Wynalek, Janusz

2017-12-01

Geostatistical methods make the analysis of measurement data possible. This article presents the problems directed towards the use of geostatistics in spatial analysis of displacements based on geodetic monitoring. Using methods of applied (spatial) statistics, the research deals with interesting and current issues connected to space-time analysis, modeling displacements and deformations, as applied to any large-area objects on which geodetic monitoring is conducted (e.g., water dams, urban areas in the vicinity of deep excavations, areas at a macro-regional scale subject to anthropogenic influences caused by mining, etc.). These problems are very crucial, especially for safety assessment of important hydrotechnical constructions, as well as for modeling and estimating mining damage. Based on the geodetic monitoring data, a substantial basic empirical material was created, comprising many years of research results concerning displacements of controlled points situated on the crown and foreland of an exemplary earth dam, and used to assess the behaviour and safety of the object during its whole operating period. A research method at a macro-regional scale was applied to investigate some phenomena connected with the operation of the analysed big hydrotechnical construction. Applying a semivariogram function enabled the spatial variability analysis of displacements. Isotropic empirical semivariograms were calculated and then, theoretical parameters of analytical functions were determined, which approximated the courses of the mentioned empirical variability measure. Using ordinary (block) kriging at the grid nodes of an elementary spatial grid covering the analysed object, the values of the Z* estimated means of displacements were calculated together with the accompanying assessment of uncertainty estimation - a standard deviation of estimation σk. Raster maps of the distribution of estimated averages Z* and raster maps of deviations of estimation σk (in perspective

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

(TPROGS) of alternating geological facies. The second method, multiple-point statistics, uses training images to estimate the conditional probability of sand-lenses at a certain location. Both methods respect field observations such as local stratigraphy, however, only the multiple-point statistics can...... 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...

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.

MoisturEC: an R application for geostatistical estimation of moisture content from electrical conductivity data

Science.gov (United States)

Terry, N.; Day-Lewis, F. D.; Werkema, D. D.; Lane, J. W., Jr.

2017-12-01

Soil moisture is a critical parameter for agriculture, water supply, and management of landfills. Whereas direct data (as from TDR or soil moisture probes) provide localized point scale information, it is often more desirable to produce 2D and/or 3D estimates of soil moisture from noninvasive measurements. To this end, geophysical methods for indirectly assessing soil moisture have great potential, yet are limited in terms of quantitative interpretation due to uncertainty in petrophysical transformations and inherent limitations in resolution. Simple tools to produce soil moisture estimates from geophysical data are lacking. We present a new standalone program, MoisturEC, for estimating moisture content distributions from electrical conductivity data. The program uses an indicator kriging method within a geostatistical framework to incorporate hard data (as from moisture probes) and soft data (as from electrical resistivity imaging or electromagnetic induction) to produce estimates of moisture content and uncertainty. The program features data visualization and output options as well as a module for calibrating electrical conductivity with moisture content to improve estimates. The user-friendly program is written in R - a widely used, cross-platform, open source programming language that lends itself to further development and customization. We demonstrate use of the program with a numerical experiment as well as a controlled field irrigation experiment. Results produced from the combined geostatistical framework of MoisturEC show improved estimates of moisture content compared to those generated from individual datasets. This application provides a convenient and efficient means for integrating various data types and has broad utility to soil moisture monitoring in landfills, agriculture, and other problems.

LSHSIM: A Locality Sensitive Hashing based method for multiple-point geostatistics

Science.gov (United States)

Moura, Pedro; Laber, Eduardo; Lopes, Hélio; Mesejo, Daniel; Pavanelli, Lucas; Jardim, João; Thiesen, Francisco; Pujol, Gabriel

2017-10-01

Reservoir modeling is a very important task that permits the representation of a geological region of interest, so as to generate a considerable number of possible scenarios. Since its inception, many methodologies have been proposed and, in the last two decades, multiple-point geostatistics (MPS) has been the dominant one. This methodology is strongly based on the concept of training image (TI) and the use of its characteristics, which are called patterns. In this paper, we propose a new MPS method that combines the application of a technique called Locality Sensitive Hashing (LSH), which permits to accelerate the search for patterns similar to a target one, with a Run-Length Encoding (RLE) compression technique that speeds up the calculation of the Hamming similarity. Experiments with both categorical and continuous images show that LSHSIM is computationally efficient and produce good quality realizations. In particular, for categorical data, the results suggest that LSHSIM is faster than MS-CCSIM, one of the state-of-the-art methods.

Implementation of the Iterative Proportion Fitting Algorithm for Geostatistical Facies Modeling

International Nuclear Information System (INIS)

Li Yupeng; Deutsch, Clayton V.

2012-01-01

In geostatistics, most stochastic algorithm for simulation of categorical variables such as facies or rock types require a conditional probability distribution. The multivariate probability distribution of all the grouped locations including the unsampled location permits calculation of the conditional probability directly based on its definition. In this article, the iterative proportion fitting (IPF) algorithm is implemented to infer this multivariate probability. Using the IPF algorithm, the multivariate probability is obtained by iterative modification to an initial estimated multivariate probability using lower order bivariate probabilities as constraints. The imposed bivariate marginal probabilities are inferred from profiles along drill holes or wells. In the IPF process, a sparse matrix is used to calculate the marginal probabilities from the multivariate probability, which makes the iterative fitting more tractable and practical. This algorithm can be extended to higher order marginal probability constraints as used in multiple point statistics. The theoretical framework is developed and illustrated with estimation and simulation example.

Evaluation of permeability of compacted bentonite ground considering heterogeneity by geostatistics

International Nuclear Information System (INIS)

Tanaka, Yukihisa; Nakamura, Kunihiko; Kudo, Kohji; Hironaga, Michihiko; Nakagami, Motonori; Niwase, Kazuhito; Komatsu, Shin-ichi

2007-01-01

The permeability of the bentonite ground as an engineered barrier is possibly designed to the value which is lower than that determined in terms of required performance because of heterogeneous distribution of permeability in the ground, which might be considerable when the ground is created by the compaction method. The effect of heterogeneity in the ground on the permeability of the bentonite ground should be evaluated by overall permeability of the ground, whereas in practice, the effect is evaluated by the distribution of permeability in the ground. Thus, in this study, overall permeability of the bentonite ground is evaluated from the permeability of the bentonite ground is evaluated from the permeability distribution determined using the geostatistical method with the dry density data as well as permeability data of the undisturbed sample recovered from the bentonite ground. Consequently, it was proved through this study that possibility of overestimation of permeability of the bentonite ground can be reduced if the overall permeability is used. (author)

Use of stratigraphic, petrographic, hydrogeologic and geochemical information for hydrogeologic modelling based on geostatistical simulation

International Nuclear Information System (INIS)

Rohlig, K.J.; Fischer, H.; Poltl, B.

2004-01-01

This paper describes the stepwise utilization of geologic information from various sources for the construction of hydrogeological models of a sedimentary site by means of geostatistical simulation. It presents a practical application of aquifer characterisation by firstly simulating hydrogeological units and then the hydrogeological parameters. Due to the availability of a large amount of hydrogeological, geophysical and other data and information, the Gorleben site (Northern Germany) has been used for a case study in order to demonstrate the approach. The study, which has not yet been completed, tries to incorporate as much as possible of the available information and to characterise the remaining uncertainties. (author)

Accuracy and uncertainty analysis of soil Bbf spatial distribution estimation at a coking plant-contaminated site based on normalization geostatistical technologies.

Science.gov (United States)

Liu, Geng; Niu, Junjie; Zhang, Chao; Guo, Guanlin

2015-12-01

Data distribution is usually skewed severely by the presence of hot spots in contaminated sites. This causes difficulties for accurate geostatistical data transformation. Three types of typical normal distribution transformation methods termed the normal score, Johnson, and Box-Cox transformations were applied to compare the effects of spatial interpolation with normal distribution transformation data of benzo(b)fluoranthene in a large-scale coking plant-contaminated site in north China. Three normal transformation methods decreased the skewness and kurtosis of the benzo(b)fluoranthene, and all the transformed data passed the Kolmogorov-Smirnov test threshold. Cross validation showed that Johnson ordinary kriging has a minimum root-mean-square error of 1.17 and a mean error of 0.19, which was more accurate than the other two models. The area with fewer sampling points and that with high levels of contamination showed the largest prediction standard errors based on the Johnson ordinary kriging prediction map. We introduce an ideal normal transformation method prior to geostatistical estimation for severely skewed data, which enhances the reliability of risk estimation and improves the accuracy for determination of remediation boundaries.

Multivariate Analysis and Modeling of Sediment Pollution Using Neural Network Models and Geostatistics

Science.gov (United States)

Golay, Jean; Kanevski, Mikhaïl

2013-04-01

The present research deals with the exploration and modeling of a complex dataset of 200 measurement points of sediment pollution by heavy metals in Lake Geneva. The fundamental idea was to use multivariate Artificial Neural Networks (ANN) along with geostatistical models and tools in order to improve the accuracy and the interpretability of data modeling. The results obtained with ANN were compared to those of traditional geostatistical algorithms like ordinary (co)kriging and (co)kriging with an external drift. Exploratory data analysis highlighted a great variety of relationships (i.e. linear, non-linear, independence) between the 11 variables of the dataset (i.e. Cadmium, Mercury, Zinc, Copper, Titanium, Chromium, Vanadium and Nickel as well as the spatial coordinates of the measurement points and their depth). Then, exploratory spatial data analysis (i.e. anisotropic variography, local spatial correlations and moving window statistics) was carried out. It was shown that the different phenomena to be modeled were characterized by high spatial anisotropies, complex spatial correlation structures and heteroscedasticity. A feature selection procedure based on General Regression Neural Networks (GRNN) was also applied to create subsets of variables enabling to improve the predictions during the modeling phase. The basic modeling was conducted using a Multilayer Perceptron (MLP) which is a workhorse of ANN. MLP models are robust and highly flexible tools which can incorporate in a nonlinear manner different kind of high-dimensional information. In the present research, the input layer was made of either two (spatial coordinates) or three neurons (when depth as auxiliary information could possibly capture an underlying trend) and the output layer was composed of one (univariate MLP) to eight neurons corresponding to the heavy metals of the dataset (multivariate MLP). MLP models with three input neurons can be referred to as Artificial Neural Networks with EXternal

Dose rate estimates and spatial interpolation maps of outdoor gamma dose rate with geostatistical methods; A case study from Artvin, Turkey

International Nuclear Information System (INIS)

Yeşilkanat, Cafer Mert; Kobya, Yaşar; Taşkin, Halim; Çevik, Uğur

2015-01-01

In this study, compliance of geostatistical estimation methods is compared to ensure investigation and imaging natural Fon radiation using the minimum number of data. Artvin province, which has a quite hilly terrain and wide variety of soil and located in the north–east of Turkey, is selected as the study area. Outdoor gamma dose rate (OGDR), which is an important determinant of environmental radioactivity level, is measured in 204 stations. Spatial structure of OGDR is determined by anisotropic, isotropic and residual variograms. Ordinary kriging (OK) and universal kriging (UK) interpolation estimations were calculated with the help of model parameters obtained from these variograms. In OK, although calculations are made based on positions of points where samples are taken, in the UK technique, general soil groups and altitude values directly affecting OGDR are included in the calculations. When two methods are evaluated based on their performances, it has been determined that UK model (r = 0.88, p < 0.001) gives quite better results than OK model (r = 0.64, p < 0.001). In addition, as a result of the maps created at the end of the study, it was illustrated that local changes are better reflected by UK method compared to OK method and its error variance is found to be lower. - Highlights: • The spatial dispersion of gamma dose rates in Artvin, which possesses one of the roughest lands in Turkey were studied. • The performance of different Geostatistic methods (OK and UK methods) for dispersion of gamma dose rates were compared. • Estimation values were calculated for non-sampling points by using the geostatistical model, the results were mapped. • The general radiological structure was determined in much less time with lower costs compared to experimental methods. • When theoretical methods are evaluated, it was obtained that UK gives more descriptive results compared to OK.

Characterization of a deep radiological contamination: integration of geostatistical processing and historical data - 59062

International Nuclear Information System (INIS)

Desnoyers, Yvon; De Moura, Patrick

2012-01-01

The problem of site characterization is quite complex, especially for deep radiological contamination. This article illustrates the added value of the geo-statistical processing on a real application case dealing with grounds of facilities partially dismantled at the end of the 1950's in Fontenay-aux-Roses CEA Center (France). 12 years ago, a first exploratory drill-hole confirmed the presence of a deep radiological contamination (more than 4 m deep). More recently, 8 additional drill-holes failed to delineate the contamination extension. The integration of the former topography and other geological data led to the realization of 10 additional drill holes. This final stage significantly improved the characterization of the radiological contamination, which impacted the remediation project and the initially estimated volumes. (authors)

Geostatistical modelling of carbon monoxide levels in Khartoum State (Sudan) - GIS pilot based study

Energy Technology Data Exchange (ETDEWEB)

Alhuseen, A [Comenius University in Bratislava, Faculty of Natural Sciences, Dept. of Landscape Ecology, 84215 Bratislava (Slovakia); Madani, M [Ministry of Environment and Physical Development, 1111 Khartoum (Sudan)

2012-04-25

The objective of this study is to develop a digital GIS model; that can evaluate, predict and visualize carbon monoxide (CO) levels in Khartoum state. To achieve this aim, sample data had been collected, processed and managed to generate a dynamic GIS model of carbon monoxide levels in the study area. Parametric data collected from the field and analysis carried throughout this study show that (CO) emissions were lower than the allowable ambient air quality standards released by National Environment Protection Council (NEPC-USA) for 1998. However, this pilot study has found emissions of (CO) in Omdurman city were the highest. This pilot study shows that GIS and geostatistical modeling can be used as a powerful tool to produce maps of exposure. (authors)

Application of geostatistical simulation to compile seismotectonic provinces based on earthquake databases (case study: Iran)

Science.gov (United States)

Jalali, Mohammad; Ramazi, Hamidreza

2018-04-01

This article is devoted to application of a simulation algorithm based on geostatistical methods to compile and update seismotectonic provinces in which Iran has been chosen as a case study. Traditionally, tectonic maps together with seismological data and information (e.g., earthquake catalogues, earthquake mechanism, and microseismic data) have been used to update seismotectonic provinces. In many cases, incomplete earthquake catalogues are one of the important challenges in this procedure. To overcome this problem, a geostatistical simulation algorithm, turning band simulation, TBSIM, was applied to make a synthetic data to improve incomplete earthquake catalogues. Then, the synthetic data was added to the traditional information to study the seismicity homogeneity and classify the areas according to tectonic and seismic properties to update seismotectonic provinces. In this paper, (i) different magnitude types in the studied catalogues have been homogenized to moment magnitude (Mw), and earthquake declustering was then carried out to remove aftershocks and foreshocks; (ii) time normalization method was introduced to decrease the uncertainty in a temporal domain prior to start the simulation procedure; (iii) variography has been carried out in each subregion to study spatial regressions (e.g., west-southwestern area showed a spatial regression from 0.4 to 1.4 decimal degrees; the maximum range identified in the azimuth of 135 ± 10); (iv) TBSIM algorithm was then applied to make simulated events which gave rise to make 68,800 synthetic events according to the spatial regression found in several directions; (v) simulated events (i.e., magnitudes) were classified based on their intensity in ArcGIS packages and homogenous seismic zones have been determined. Finally, according to the synthetic data, tectonic features, and actual earthquake catalogues, 17 seismotectonic provinces were introduced in four major classes introduced as very high, high, moderate, and low

Using river distance and existing hydrography data can improve the geostatistical estimation of fish tissue mercury at unsampled locations.

Science.gov (United States)

Money, Eric S; Sackett, Dana K; Aday, D Derek; Serre, Marc L

2011-09-15

Mercury in fish tissue is a major human health concern. Consumption of mercury-contaminated fish poses risks to the general population, including potentially serious developmental defects and neurological damage in young children. Therefore, it is important to accurately identify areas that have the potential for high levels of bioaccumulated mercury. However, due to time and resource constraints, it is difficult to adequately assess fish tissue mercury on a basin wide scale. We hypothesized that, given the nature of fish movement along streams, an analytical approach that takes into account distance traveled along these streams would improve the estimation accuracy for fish tissue mercury in unsampled streams. Therefore, we used a river-based Bayesian Maximum Entropy framework (river-BME) for modern space/time geostatistics to estimate fish tissue mercury at unsampled locations in the Cape Fear and Lumber Basins in eastern North Carolina. We also compared the space/time geostatistical estimation using river-BME to the more traditional Euclidean-based BME approach, with and without the inclusion of a secondary variable. Results showed that this river-based approach reduced the estimation error of fish tissue mercury by more than 13% and that the median estimate of fish tissue mercury exceeded the EPA action level of 0.3 ppm in more than 90% of river miles for the study domain.

Assessing the spatial distribution of Tuta absoluta (Lepidoptera: Gelechiidae) eggs in open-field tomato cultivation through geostatistical analysis.

Science.gov (United States)

Martins, Júlio C; Picanço, Marcelo C; Silva, Ricardo S; Gonring, Alfredo Hr; Galdino, Tarcísio Vs; Guedes, Raul Nc

2018-01-01

The spatial distribution of insects is due to the interaction between individuals and the environment. Knowledge about the within-field pattern of spatial distribution of a pest is critical to planning control tactics, developing efficient sampling plans, and predicting pest damage. The leaf miner Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) is the main pest of tomato crops in several regions of the world. Despite the importance of this pest, the pattern of spatial distribution of T. absoluta on open-field tomato cultivation remains unknown. Therefore, this study aimed to characterize the spatial distribution of T. absoluta in 22 commercial open-field tomato cultivations with plants at the three phenological development stages by using geostatistical analysis. Geostatistical analysis revealed that there was strong evidence for spatially dependent (aggregated) T. absoluta eggs in 19 of the 22 sample tomato cultivations. The maps that were obtained demonstrated the aggregated structure of egg densities at the edges of the crops. Further, T. absoluta was found to accomplish egg dispersal along the rows more frequently than it does between rows. Our results indicate that the greatest egg densities of T. absoluta occur at the edges of tomato crops. These results are discussed in relation to the behavior of T. absoluta distribution within fields and in terms of their implications for improved sampling guidelines and precision targeting control methods that are essential for effective pest monitoring and management. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

Science.gov (United States)

Manzione, Rodrigo L.; Wendland, Edson; Tanikawa, Diego H.

2012-11-01

Stochastic methods based on time-series modeling combined with geostatistics can be useful tools to describe the variability of water-table levels in time and space and to account for uncertainty. Monitoring water-level networks can give information about the dynamic of the aquifer domain in both dimensions. Time-series modeling is an elegant way to treat monitoring data without the complexity of physical mechanistic models. Time-series model predictions can be interpolated spatially, with the spatial differences in water-table dynamics determined by the spatial variation in the system properties and the temporal variation driven by the dynamics of the inputs into the system. An integration of stochastic methods is presented, based on time-series modeling and geostatistics as a framework to predict water levels for decision making in groundwater management and land-use planning. The methodology is applied in a case study in a Guarani Aquifer System (GAS) outcrop area located in the southeastern part of Brazil. Communication of results in a clear and understandable form, via simulated scenarios, is discussed as an alternative, when translating scientific knowledge into applications of stochastic hydrogeology in large aquifers with limited monitoring network coverage like the GAS.

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

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

Science.gov (United States)

Rochlin, Ilia; Iwanejko, Tom; Dempsey, Mary E; Ninivaggi, Dominick V

2009-06-23

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. 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. 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 areas led to a significant decrease (approximately 44%) in

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

International Nuclear Information System (INIS)

Chen, DI-WEN

2001-01-01

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 methodology, quantitatively combines soft information

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

Geostatistics – a tool applied to the distribution of Legionella pneumophila in a hospital water system

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Pasqualina Laganà

2015-12-01

Full Text Available [b]Introduction.[/b] Legionnaires’ disease is normally acquired by inhalation of legionellae from a contaminated environmental source. Water systems of large buildings, such as hospitals, are often contaminated with legionellae and therefore represent a potential risk for the hospital population. The aim of this study was to evaluate the potential contamination of [i]Legionella pneumophila[/i] (LP in a large hospital in Italy through georeferential statistical analysis to assess the possible sources of dispersion and, consequently, the risk of exposure for both health care staff and patients. [b]Materials and Method. [/b]LP serogroups 1 and 2–14 distribution was considered in the wards housed on two consecutive floors of the hospital building. On the basis of information provided by 53 bacteriological analysis, a ‘random’ grid of points was chosen and spatial geostatistics or [i]FAIk Kriging[/i] was applied and compared with the results of classical statistical analysis. [b]Results[/b]. Over 50% of the examined samples were positive for [i]Legionella pneumophila[/i]. LP 1 was isolated in 69% of samples from the ground floor and in 60% of sample from the first floor; LP 2–14 in 36% of sample from the ground floor and 24% from the first. The iso-estimation maps show clearly the most contaminated pipe and the difference in the diffusion of the different [i]L. pneumophila[/i] serogroups. [b]Conclusion.[/b] Experimental work has demonstrated that geostatistical methods applied to the microbiological analysis of water matrices allows a better modeling of the phenomenon under study, a greater potential for risk management and a greater choice of methods of prevention and environmental recovery to be put in place with respect to the classical statistical analysis.

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

International Nuclear Information System (INIS)

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. -- Highlights: • A map of Rn concentrations in primary schools of Southern Serbia. • Application of geostatistical methods. • Correlation with geology found. • Can serve as proxy to identify radon prone areas

Geostatistical Study of Precipitation on the Island of Crete

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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.

Characterisation and geostatistical analysis of clay rocks in underground facilities using hyper-spectral images

International Nuclear Information System (INIS)

Becker, J.K.; Marschall, P.; Brunner, P.; Cholet, C.; Renard, P.; Buckley, S.; Kurz, T.

2012-01-01

Document available in extended abstract form only. Flow and transport processes in geological formations are controlled by the porosity and permeability which in turn are mainly controlled by the fabric and the mineralogical composition of the rock. For the assessment of transport processes in water-saturated Clay-stone formations, the relevant scales are ranging essentially from kilometers to nanometers. The spatial variability of the mineralogical composition is a key indicator for the separation of transport scales and for the derivation of the effective transport properties at a given scale. Various laboratory and in-situ techniques are available for characterizing the mineralogical composition of a rock on different scales. The imaging spectroscopy presented in this paper is a new site investigation method suitable for mapping the mineralogical composition of geological formations in 2D on a large range of scales. A combination of imaging spectrometry with other site characterization methods allows the inference of the spatial variability of the mineralogical composition in 3D over a wide range of scales with the help of advanced geostatistical methods. The method of image spectrometry utilizes the fact that the reflection of electromagnetic radiation from a surface is a function of the wavelength, the chemical-mineralogical surface properties, and physical parameters such as the grain size and surface roughness. In remote sensing applications using the sun as the light source, the reflectance is measured within the visible and infrared range, according to the atmospheric transmissibility. Many rock-forming minerals exhibit diagnostic absorption features within this range, which are caused by electronic and vibrational processes within the crystal lattice. The exact wavelength of an absorption feature is controlled by the type of ion, as well as the position of the ion within the lattice. Spectral signatures of minerals are described by a number of authors

Application of Geostatistics to the resolution of structural problems in homogeneous rocky massifs

International Nuclear Information System (INIS)

Lucero Michaut, H.N.

1985-01-01

The nature and possibilities of application of intrinsic functions to the structural research and the delimitation of the areas of influence in an ore deposit are briefly described. Main models to which the different distributions may be assimilated: 'logarithmic' and 'linear' among those with no sill value, and on the other hand, 'spherical', 'exponential' and 'gaussian' among those having a sill level, which allows the establishment of a range value liable to separate the field of independent samples from that of non-independent ones are shown. Thereafter as an original contribution to applied geostatistics the autor postulates 1) the application of the 'fracturing rank' as a regionalized variable after verifying its validity through strict probabilistic methodologies, and 2) a methodological extension of the conventional criterion of 'rock quality designation' to the analysis of the quality and degree of structural discontinuity in the rock surface. Finally, some examples are given of these applications. (M.E.L.) [es

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

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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.

Analysis and simulation of wireless signal propagation applying geostatistical interpolation techniques

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Kolyaie, S.; Yaghooti, M.; Majidi, G.

2011-12-01

This paper is a part of an ongoing research to examine the capability of geostatistical analysis for mobile networks coverage prediction, simulation and tuning. Mobile network coverage predictions are used to find network coverage gaps and areas with poor serviceability. They are essential data for engineering and management in order to make better decision regarding rollout, planning and optimisation of mobile networks.The objective of this research is to evaluate different interpolation techniques in coverage prediction. In method presented here, raw data collected from drive testing a sample of roads in study area is analysed and various continuous surfaces are created using different interpolation methods. Two general interpolation methods are used in this paper with different variables; first, Inverse Distance Weighting (IDW) with various powers and number of neighbours and second, ordinary kriging with Gaussian, spherical, circular and exponential semivariogram models with different number of neighbours. For the result comparison, we have used check points coming from the same drive test data. Prediction values for check points are extracted from each surface and the differences with actual value are computed. The output of this research helps finding an optimised and accurate model for coverage prediction.

A general parallelization strategy for random path based geostatistical simulation methods

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Mariethoz, Grégoire

2010-07-01

The size of simulation grids used for numerical models has increased by many orders of magnitude in the past years, and this trend is likely to continue. Efficient pixel-based geostatistical simulation algorithms have been developed, but for very large grids and complex spatial models, the computational burden remains heavy. As cluster computers become widely available, using parallel strategies is a natural step for increasing the usable grid size and the complexity of the models. These strategies must profit from of the possibilities offered by machines with a large number of processors. On such machines, the bottleneck is often the communication time between processors. We present a strategy distributing grid nodes among all available processors while minimizing communication and latency times. It consists in centralizing the simulation on a master processor that calls other slave processors as if they were functions simulating one node every time. The key is to decouple the sending and the receiving operations to avoid synchronization. Centralization allows having a conflict management system ensuring that nodes being simulated simultaneously do not interfere in terms of neighborhood. The strategy is computationally efficient and is versatile enough to be applicable to all random path based simulation methods.

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

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Alinune N Kabaghe

Full Text Available 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.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.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.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 greatest health impact and is

Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations

Science.gov (United States)

Babcock, Chad; Finley, Andrew O.; Andersen, Hans-Erik; Pattison, Robert; Cook, Bruce D.; Morton, Douglas C.; Alonzo, Michael; Nelson, Ross; Gregoire, Timothy; Ene, Liviu; Gobakken, Terje; Næsset, Erik

2018-06-01

The goal of this research was to develop and examine the performance of a geostatistical coregionalization modeling approach for combining field inventory measurements, strip samples of airborne lidar and Landsat-based remote sensing data products to predict aboveground biomass (AGB) in interior Alaska's Tanana Valley. The proposed modeling strategy facilitates pixel-level mapping of AGB density predictions across the entire spatial domain. Additionally, the coregionalization framework allows for statistically sound estimation of total AGB for arbitrary areal units within the study area---a key advance to support diverse management objectives in interior Alaska. This research focuses on appropriate characterization of prediction uncertainty in the form of posterior predictive coverage intervals and standard deviations. Using the framework detailed here, it is possible to quantify estimation uncertainty for any spatial extent, ranging from pixel-level predictions of AGB density to estimates of AGB stocks for the full domain. The lidar-informed coregionalization models consistently outperformed their counterpart lidar-free models in terms of point-level predictive performance and total AGB precision. Additionally, the inclusion of Landsat-derived forest cover as a covariate further improved estimation precision in regions with lower lidar sampling intensity. Our findings also demonstrate that model-based approaches that do not explicitly account for residual spatial dependence can grossly underestimate uncertainty, resulting in falsely precise estimates of AGB. On the other hand, in a geostatistical setting, residual spatial structure can be modeled within a Bayesian hierarchical framework to obtain statistically defensible assessments of uncertainty for AGB estimates.

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.

Geostatistics and Geographic Information System to Analyze the Spatial Distribution of the Diversity of Anastrepha Species (Diptera: Tephritidae): the Effect of Forest Fragments in an Urban Area.

Science.gov (United States)

Garcia, A G; Araujo, M R; Uramoto, K; Walder, J M M; Zucchi, R A

2017-12-08

Fruit flies are among the most damaging insect pests of commercial fruit in Brazil. It is important to understand the landscape elements that may favor these flies. In the present study, spatial data from surveys of species of Anastrepha Schiner (Diptera: Tephritidae) in an urban area with forest fragments were analyzed, using geostatistics and Geographic Information System (GIS) to map the diversity of insects and evaluate how the forest fragments drive the spatial patterns. The results indicated a high diversity of species associated with large fragments, and a trend toward lower diversity in the more urbanized area, as the fragment sizes decreased. We concluded that the diversity of Anastrepha species is directly and positively related to large and continuous forest fragments in urbanized areas, and that combining geostatistics and GIS is a promising method for use in insect-pest management and sampling involving fruit flies. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Unsupervised classification of multivariate geostatistical data: Two algorithms

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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.

Robust spatialization of soil water content at the scale of an agricultural field using geophysical and geostatistical methods

Science.gov (United States)

Henine, Hocine; Tournebize, Julien; Laurent, Gourdol; Christophe, Hissler; Cournede, Paul-Henry; Clement, Remi

2017-04-01

Research on the Critical Zone (CZ) is a prerequisite for undertaking issues related to ecosystemic services that human societies rely on (nutrient cycles, water supply and quality). However, while the upper part of CZ (vegetation, soil, surface water) is readily accessible, knowledge of the subsurface remains limited, due to the point-scale character of conventional direct observations. While the potential for geophysical methods to overcome this limitation is recognized, the translation of the geophysical information into physical properties or states of interest remains a challenge (e.g. the translation of soil electrical resistivity into soil water content). In this study, we propose a geostatistical framework using the Bayesian Maximum Entropy (BME) approach to assimilate geophysical and point-scale data. We especially focus on the prediction of the spatial distribution of soil water content using (1) TDR point-scale measurements of soil water content, which are considered as accurate data, and (2) soil water content data derived from electrical resistivity measurements, which are uncertain data but spatially dense. We used a synthetic dataset obtained with a vertical 2D domain to evaluate the performance of this geostatistical approach. Spatio-temporal simulations of soil water content were carried out using Hydrus-software for different scenarios: homogeneous or heterogeneous hydraulic conductivity distribution, and continuous or punctual infiltration pattern. From the simulations of soil water content, conceptual soil resistivity models were built using a forward modeling approach and point sampling of water content values, vertically ranged, were done. These two datasets are similar to field measurements of soil electrical resistivity (using electrical resistivity tomography, ERT) and soil water content (using TDR probes) obtained at the Boissy-le-Chatel site, in Orgeval catchment (East of Paris, France). We then integrated them into a specialization

Fine-grained sediment spatial distribution on the basis of a geostatistical analysis: Example of the eastern Bay of the Seine (France)

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Méar, Y.; Poizot, E.; Murat, A.; Lesueur, P.; Thomas, M.

2006-12-01

The eastern Bay of the Seine (English Channel) was the subject in 1991 of a sampling survey of superficial sediments. Geostatistic tools were used to examine the complexity of the spatial distribution of the fine-grained fraction (discussed. Within this sedimentary unit, the distribution of the fine fraction is very heterogeneous, with mud patches of less than 4000 m diameter; the boundary between these mud patches and their substratum is very sharp. The distribution of this fine fraction appears to be controlled by an anticyclonic eddy located off the Pays de Caux. Under the influence of this, the suspended material expelled from the Seine estuary moves along the coast and swings off Antifer harbour, towards the NW. It is trapped within this eddy because of the settling of suspended particulate matter. Both at a general scale and a local scale the morphology (whether inherited or due to modern processes) has a strong influence on the spatial distribution of the fine fraction. At the general scale, the basin-like shape of the area facilitates the silting, and the presence of the submarine dunes, called "Ridins d'Antifer", clearly determines the northern limit of the muddy zone. At a local scale, the same influence is obvious: paleovalleys trap the fine sediments, whereas isolated sand dunes and ripples limit the silting. This duality of role of the morphology is therefore one of the reasons why the muddy surface is extremely heterogeneous spatially. The presence of an important population of suspension feeding echinoderm, the brittle-star Ophiothrix fragilis Abildgaard, has led to a local increase in the silting, and to the modification of the physicochemical and sedimentological parameters. A complex relationship is shown to occur between the amount of fine fraction and the number of brittle-stars (ind. m -2). Classical statistical methods are not appropriate to study the spatial distribution of the mud fraction, because the spatial component of the percentage of

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.

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

Geostatistical exploration of dataset assessing the heavy metal contamination in Ewekoro limestone, Southwestern Nigeria

Directory of Open Access Journals (Sweden)

Kehinde D. Oyeyemi

2017-10-01

Full Text Available The dataset for this article contains geostatistical analysis of heavy metals contamination from limestone samples collected from Ewekoro Formation in the eastern Dahomey basin, Ogun State Nigeria. The samples were manually collected and analysed using Microwave Plasma Atomic Absorption Spectrometer (MPAS. Analysis of the twenty different samples showed different levels of heavy metals concentration. The analysed nine elements are Arsenic, Mercury, Cadmium, Cobalt, Chromium, Nickel, Lead, Vanadium and Zinc. Descriptive statistics was used to explore the heavy metal concentrations individually. Pearson, Kendall tau and Spearman rho correlation coefficients was used to establish the relationships among the elements and the analysis of variance showed that there is a significant difference in the mean distribution of the heavy metals concentration within and between the groups of the 20 samples analysed. The dataset can provide insights into the health implications of the contaminants especially when the mean concentration levels of the heavy metals are compared with recommended regulatory limit concentration.

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.

Geostatistical analysis of allele presence patterns among American black bears in eastern North Carolina

Science.gov (United States)

Thompson, L.M.; Van Manen, F.T.; King, T.L.

2005-01-01

Highways are one of the leading causes of wildlife habitat fragmentation and may particularly affect wide-ranging species, such as American black bears (Ursus americanus). We initiated a research project in 2000 to determine potential effects of a 4-lane highway on black bear ecology in Washington County, North Carolina. The research design included a treatment area (highway construction) and a control area and a pre- and post-construction phase. We used data from the pre-construction phase to determine whether we could detect scale dependency or directionality among allele occurrence patterns using geostatistics. Detection of such patterns could provide a powerful tool to measure the effects of landscape fragmentation on gene flow. We sampled DNA from roots of black bear hair at 70 hair-sampling sites on each study area for 7 weeks during fall of 2000. We used microsatellite analysis based on 10 loci to determine unique multi-locus genotypes. We examined all alleles sampled at ???25 sites on each study area and mapped their presence or absence at each hair-sample site. We calculated semivariograms, which measure the strength of statistical correlation as a function of distance, and adjusted them for anisotropy to determine the maximum direction of spatial continuity. We then calculated the mean direction of spatial continuity for all examined alleles. The mean direction of allele frequency variation was 118.3?? (SE = 8.5) on the treatment area and 172.3?? (SE = 6.0) on the control area. Rayleigh's tests showed that these directions differed from random distributions (P = 0.028 and P < 0.001, respectively), indicating consistent directional patterns for the alleles we examined in each area. Despite the small spatial scale of our study (approximately 11,000 ha for each study area), we observed distinct and consistent patterns of allele occurrence, suggesting different directions of gene flow between the study areas. These directions seemed to coincide with the

Applicability of geostatistical procedures for the evaluation of hydrogeological parameters of a fractured aquifer in the Ronneburg mine district; Anwendbarkeit geostatistischer Verfahren zur Beurteilung hydrogeologischer Parameter eines heterogenen Kluftaquifers im Ronneburger Bergbaurevier

Energy Technology Data Exchange (ETDEWEB)

Grasshoff, C.; Schetelig, K. [RWTH Aachen, Lehrstuhl fuer Ingenieurgeologie und Hydrogeologie (Germany); Tomschi, H. [Harress Pickel Consult GmbH, Huerth (Germany)

1998-12-31

The following paper demonstrates, how a geostatistical approach can help interpolating hydrogeological parameters over a certain area. The basic elements developed by G. Matheron in the sixties are represented as the preconditions and assumptions, which provide the best results of the estimation. The variogram as the most important tool in geostatistics offers the opportunity to describe the correlating behaviour of a regionalized variable. Some kriging procedures are briefly introduced, which provide under varying circumstances estimating of non-measured values with the theoretical variogram-model. In the Ronneburg mine district 108 screened drill-holes could provide coefficients of hydraulic conductivity. These were interpolated with ordinary kriging over the whole investigation area. An error calculation was performed, which could prove the accuracy of the estimation. Short prospects point out some difficulties handling with geostatistic procedures and make suggestions for further investigations. (orig.) [Deutsch] Der folgende Artikel soll aufzeigen, inwiefern ein geostatistischer Ansatz hilfreich ist, um hydrogeologische Parameter flaechenhaft zu interpolieren. Dabei werden die von Matheron in den sechziger Jahren entwickelten Grundlagen vorgestellt und die Voraussetzungen definiert, unter denen die geostatistischen Schaetzmethoden die besten Ergebnisse liefern. Das Variogramm, als wichtigstes Werkzeug, bietet die Moeglichkeit, die raeumliche Korrelation der untersuchten Variable zu belegen. Mehrere Kriging-Verfahren werden skizzenhaft vorgestellt, die es unter unterschiedlichen Voraussetzungen ermoeglichen, an den Stellen des Untersuchungsgebietes, wo keine Messungen vorliegen, Schaetzungen aus dem Variogramm-Modell zu errechnen. Im Ronneburger Bergbaugebiet wurden aus 108 verfilterten Bohrungen k{sub f}-Werte gewonnen, die mittels Ordinary Kriging flaechenhaft ueber das gesamte Untersuchungsgebiet interpoliert wurden. Eine Fehlerabschaetzung gibt ueber die

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.

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

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

2007-01-01

In the Hugoton embayment of southwestern Kansas, reservoirs composed of relatively thin (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.

Geostatistical characterisation of geothermal parameters for a thermal aquifer storage site in Germany

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Rodrigo-Ilarri, J.; Li, T.; Grathwohl, P.; Blum, P.; Bayer, P.

2009-04-01

The design of geothermal systems such as aquifer thermal energy storage systems (ATES) must account for a comprehensive characterisation of all relevant parameters considered for the numerical design model. Hydraulic and thermal conductivities are the most relevant parameters and its distribution determines not only the technical design but also the economic viability of such systems. Hence, the knowledge of the spatial distribution of these parameters is essential for a successful design and operation of such systems. This work shows the first results obtained when applying geostatistical techniques to the characterisation of the Esseling Site in Germany. In this site a long-term thermal tracer test (> 1 year) was performed. On this open system the spatial temperature distribution inside the aquifer was observed over time in order to obtain as much information as possible that yield to a detailed characterisation both of the hydraulic and thermal relevant parameters. This poster shows the preliminary results obtained for the Esseling Site. It has been observed that the common homogeneous approach is not sufficient to explain the observations obtained from the TRT and that parameter heterogeneity must be taken into account.

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.

Spatiotemporal mapping of ground water pollution in a Greek lignite basin, using geostatistics

Energy Technology Data Exchange (ETDEWEB)

Modis, K. [National Technical Univ. of Athens, Athens (Greece)

2010-07-01

An issue of significant interest in the mining industry in Greece is the occurrence of chemical pollutants in ground water. Ammonium, nitrites and nitrates concentrations have been monitored through an extensive sampling network in the Ptolemais lignite opencast mining area in Greece. Due to intensive mining efforts in the area, the surface topology is continuously altered, affecting the life span of the water boreholes and resulting in messy spatiotemporal distribution of data. This paper discussed the spatiotemporal mapping of ground water pollution in the Ptolemais lignite basin, using geostatistics. More specifically, the spatiotemporal distribution of ground water contamination was examined by the application of the bayesian maximum entropy theory which allows merging spatial and temporal estimations in a single model. The paper provided a description of the site and discussed the materials and methods, including samples and statistics; variography; and spatiotemporal mapping. It was concluded that in the case of the Ptolemais mining area, results revealed an underlying average yearly variation pattern of pollutant concentrations. Inspection of the produced spatiotemporal maps demonstrated a continuous increase in the risk of ammonium contamination, while risk for the other two pollutants appeared in hot spots. 18 refs., 1 tab., 7 figs.

Spatiotemporal mapping of ground water pollution in a Greek lignite basin, using geostatistics

International Nuclear Information System (INIS)

Modis, K.

2010-01-01

An issue of significant interest in the mining industry in Greece is the occurrence of chemical pollutants in ground water. Ammonium, nitrites and nitrates concentrations have been monitored through an extensive sampling network in the Ptolemais lignite opencast mining area in Greece. Due to intensive mining efforts in the area, the surface topology is continuously altered, affecting the life span of the water boreholes and resulting in messy spatiotemporal distribution of data. This paper discussed the spatiotemporal mapping of ground water pollution in the Ptolemais lignite basin, using geostatistics. More specifically, the spatiotemporal distribution of ground water contamination was examined by the application of the bayesian maximum entropy theory which allows merging spatial and temporal estimations in a single model. The paper provided a description of the site and discussed the materials and methods, including samples and statistics; variography; and spatiotemporal mapping. It was concluded that in the case of the Ptolemais mining area, results revealed an underlying average yearly variation pattern of pollutant concentrations. Inspection of the produced spatiotemporal maps demonstrated a continuous increase in the risk of ammonium contamination, while risk for the other two pollutants appeared in hot spots. 18 refs., 1 tab., 7 figs.

Bayesian Geostatistical Modeling of Malaria Indicator Survey Data in Angola

Science.gov (United States)

Gosoniu, Laura; Veta, Andre Mia; Vounatsou, Penelope

2010-01-01

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. PMID:20351775

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.

Spatial variability of soil pH based on GIS combined with geostatistics in Panzhihua tobacco area

International Nuclear Information System (INIS)

Du Wei; Wang Changquan; Li Bing; Li Qiquan; Du Qian; Hu Jianxin; Liu Chaoke

2012-01-01

GIS and geostatistics were utilized to study the spatial variability of soil pH in Panzhihua tobacco area. Results showed that pH values in this area ranged from 4.5 to 8.3, especially 5.5 to 6.5, and in few areas were lower than 5.0 or higher than 7.0 which can meet the need of high-quality tobacco production. The best fitting model of variogram was exponential model with the nugget/sill of soil pH in 13.61% indicating strong spatial correlation. The change process was 5.40 km and the coefficient of determination was 0.491. The spatial variability of soil pH was mainly caused by structural factors such as cane, topography and soil type. The soil pH in Panzhihua tobacco area also showed a increasing trend of northwest to southeast trend. The pH of some areas in Caochang, Gonghe and Yumen were lower, and in Dalongtan were slightly higher. (authors)

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. © 2012, The Author(s). Ground Water © 2012, National Ground Water Association.

Forward modeling of gravity data using geostatistically generated subsurface density variations

Science.gov (United States)

Phelps, Geoffrey

2016-01-01

Using geostatistical models of density variations in the subsurface, constrained by geologic data, forward models of gravity anomalies can be generated by discretizing the subsurface and calculating the cumulative effect of each cell (pixel). The results of such stochastically generated forward gravity anomalies can be compared with the observed gravity anomalies to find density models that match the observed data. These models have an advantage over forward gravity anomalies generated using polygonal bodies of homogeneous density because generating numerous realizations explores a larger region of the solution space. The stochastic modeling can be thought of as dividing the forward model into two components: that due to the shape of each geologic unit and that due to the heterogeneous distribution of density within each geologic unit. The modeling demonstrates that the internally heterogeneous distribution of density within each geologic unit can contribute significantly to the resulting calculated forward gravity anomaly. Furthermore, the stochastic models match observed statistical properties of geologic units, the solution space is more broadly explored by producing a suite of successful models, and the likelihood of a particular conceptual geologic model can be compared. The Vaca Fault near Travis Air Force Base, California, can be successfully modeled as a normal or strike-slip fault, with the normal fault model being slightly more probable. It can also be modeled as a reverse fault, although this structural geologic configuration is highly unlikely given the realizations we explored.

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

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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..

Estimating the number of cases of podoconiosis in Ethiopia using geostatistical methods [version 2; referees: 3 approved, 1 approved with reservations

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Kebede Deribe

2017-12-01

Full Text Available Background: In 2011, the World Health Organization recognized podoconiosis as one of the neglected tropical diseases. Nonetheless, the  magnitude of podoconiosis and the geographical distribution of the disease is poorly understood. Based on a nationwide mapping survey and geostatistical modelling, we predict the prevalence of podoconiosis and estimate the number of cases across Ethiopia. Methods: We used nationwide data collected in Ethiopia between 2008 and 2013. Data were available for 141,238 individuals from 1,442 communities in 775 districts from all nine regional states and two city administrations. We developed a geostatistical model of podoconiosis prevalence among adults (individuals aged 15 years or above, by combining environmental factors. The number of people with podoconiosis was then estimated using a gridded map of adult population density for 2015. Results: Podoconiosis is endemic in 345 districts in Ethiopia: 144 in Oromia, 128 in Southern Nations, Nationalities and People’s [SNNP], 64 in Amhara, 4 in Benishangul Gumuz, 4 in Tigray and 1 in Somali Regional State. Nationally, our estimates suggest that 1,537,963 adults (95% confidence intervals, 290,923-4,577,031 adults were living with podoconiosis in 2015. Three regions (SNNP, Oromia and Amhara contributed 99% of the cases. The highest proportion of individuals with podoconiosis resided in the SNNP (39%, while 32% and 29% of people with podoconiosis resided in Oromia and Amhara Regional States, respectively. Tigray and Benishangul Gumuz Regional States bore lower burdens, and in the remaining regions, podoconiosis was almost non-existent.  Conclusions: The estimates of podoconiosis cases presented here based upon the combination of currently available epidemiological data and a robust modelling approach clearly show that podoconiosis is highly endemic in Ethiopia. Given the presence of low cost prevention, and morbidity management and disability prevention services, it is

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

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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.

Improving imperfect data from health management information systems in Africa using space-time geostatistics.

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Peter W Gething

2006-06-01

Full Text Available Reliable and timely information on disease-specific treatment burdens within a health system is critical for the planning and monitoring of service provision. Health management information systems (HMIS exist to address this need at national scales across Africa but are failing to deliver adequate data because of widespread underreporting by health facilities. Faced with this inadequacy, vital public health decisions often rely on crudely adjusted regional and national estimates of treatment burdens.This study has taken the example of presumed malaria in outpatients within the largely incomplete Kenyan HMIS database and has defined a geostatistical modelling framework that can predict values for all data that are missing through space and time. The resulting complete set can then be used to define treatment burdens for presumed malaria at any level of spatial and temporal aggregation. Validation of the model has shown that these burdens are quantified to an acceptable level of accuracy at the district, provincial, and national scale.The modelling framework presented here provides, to our knowledge for the first time, reliable information from imperfect HMIS data to support evidence-based decision-making at national and sub-national levels.

Definition of radon prone areas in Friuli Venezia Giulia region, Italy, using geostatistical tools.

Science.gov (United States)

Cafaro, C; Bossew, P; Giovani, C; Garavaglia, M

2014-12-01

Studying the geographical distribution of indoor radon concentration, using geostatistical interpolation methods, has become common for predicting and estimating the risk to the population. Here we analyse the case of Friuli Venezia Giulia (FVG), the north easternmost region of Italy. Mean value and standard deviation are, respectively, 153 Bq/m(3) and 183 Bq/m(3). The geometric mean value is 100 Bq/m(3). Spatial datasets of indoor radon concentrations are usually affected by clustering and apparent non-stationarity issues, which can eventually yield arguable results. The clustering of the present dataset seems to be non preferential. Therefore the areal estimations are not expected to be affected. Conversely, nothing can be said on the non stationarity issues and its effects. After discussing the correlation of geology with indoor radon concentration It appears they are created by the same geologic features influencing the mean and median values, and can't be eliminated via a map-based approach. To tackle these problems, in this work we deal with multiple definitions of RPA, but only in quaternary areas of FVG, using extensive simulation techniques. Copyright © 2014 Elsevier Ltd. All rights reserved.

Characterisation of contaminated metals using an advanced statistical toolbox - Geostatistical characterisation of contaminated metals: methodology and illustrations

International Nuclear Information System (INIS)

Larsson, Arne; Lidar, Per; Desnoyers, Yvon

2014-01-01

Radiological characterisation plays an important role in the process to recycle contaminated or potentially contaminated metals. It is a platform for planning, identification of the extent and nature of contamination, assessing potential risk impacts, cost estimation, radiation protection, management of material arising from decommissioning as well as for the release of the materials as well as the disposal of the generated secondary waste as radioactive waste. Key issues in radiological characterisation are identification of objectives, development of a measurement and sampling strategy (probabilistic, judgmental or a combination thereof), knowledge management, traceability, recording and processing of obtained information. By applying advanced combination of statistical and geostatistical in the concept better performance can be achieved at a lower cost. This paper will describe the benefits with the usage of the available methods in the different stages of the characterisation, treatment and clearance processes aiming for reliable results in line with the data quality objectives. (authors)

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

International Nuclear Information System (INIS)

Park, Jinyong; Balasingham, P.; McKenna, Sean Andrew; Kulatilake, Pinnaduwa H. S. W.

2004-01-01

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

Geostatistical and GIS analysis of the spatial variability of alluvial gold content in Ngoura-Colomines area, Eastern Cameroon: Implications for the exploration of primary gold deposit

Science.gov (United States)

Takodjou Wambo, Jonas Didero; Ganno, Sylvestre; Djonthu Lahe, Yannick Sthopira; Kouankap Nono, Gus Djibril; Fossi, Donald Hermann; Tchouatcha, Milan Stafford; Nzenti, Jean Paul

2018-06-01

Linear and nonlinear geostatistic is commonly used in ore grade estimation and seldom used in Geographical Information System (GIS) technology. In this study, we suggest an approach based on geostatistic linear ordinary kriging (OK) and Geographical Information System (GIS) techniques to investigate the spatial distribution of alluvial gold content, mineralized and gangue layers thicknesses from 73 pits at the Ngoura-Colomines area with the aim to determine controlling factors for the spatial distribution of mineralization and delineate the most prospective area for primary gold mineralization. Gold content varies between 0.1 and 4.6 g/m3 and has been broadly grouped into three statistical classes. These classes have been spatially subdivided into nine zones using ordinary kriging model based on physical and topographical characteristics. Both mineralized and barren layer thicknesses show randomly spatial distribution, and there is no correlation between these parameters and the gold content. This approach has shown that the Ngoura-Colomines area is located in a large shear zone compatible with the Riedel fault system composed of P and P‧ fractures oriented NE-SW and NNE-SSW respectively; E-W trending R fractures and R‧ fractures with NW-SE trends that could have contributed significantly to the establishment of this gold mineralization. The combined OK model and GIS analysis have led to the delineation of Colomines, Tissongo, Madubal and Boutou villages as the most prospective areas for the exploration of primary gold deposit in the study area.

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.

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

International Nuclear Information System (INIS)

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

2016-01-01

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

Determining site-specific background level with geostatistics for remediation of heavy metals in neighborhood soils

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Tammy M. Milillo

2017-03-01

Full Text Available The choice of a relevant, uncontaminated site for the determination of site-specific background concentrations for pollutants is critical for planning remediation of a contaminated site. The guidelines used to arrive at concentration levels vary from state to state, complicating this process. The residential neighborhood of Hickory Woods in Buffalo, NY is an area where heavy metal concentrations and spatial distributions were measured to plan remediation. A novel geostatistics based decision making framework that relies on maps generated from indicator kriging (IK and indicator co-kriging (ICK of samples from the contaminated site itself is shown to be a viable alternative to the traditional method of choosing a reference site for remediation planning. GIS based IK and ICK, and map based analysis are performed on lead and arsenic surface and subsurface datasets to determine site-specific background concentration levels were determined to be 50 μg/g for lead and 10 μg/g for arsenic. With these results, a remediation plan was proposed which identified regions of interest and maps were created to effectively communicate the results to the environmental agencies, residents and other interested parties.

A combined geostatistical-optimization model for the optimal design of a groundwater quality monitoring network

Science.gov (United States)

Kolosionis, Konstantinos; Papadopoulou, Maria P.

2017-04-01

Monitoring networks provide essential information for water resources management especially in areas with significant groundwater exploitation due to extensive agricultural activities. In this work, a simulation-optimization framework is developed based on heuristic optimization methodologies and geostatistical modeling approaches to obtain an optimal design for a groundwater quality monitoring network. Groundwater quantity and quality data obtained from 43 existing observation locations at 3 different hydrological periods in Mires basin in Crete, Greece will be used in the proposed framework in terms of Regression Kriging to develop the spatial distribution of nitrates concentration in the aquifer of interest. Based on the existing groundwater quality mapping, the proposed optimization tool will determine a cost-effective observation wells network that contributes significant information to water managers and authorities. The elimination of observation wells that add little or no beneficial information to groundwater level and quality mapping of the area can be obtain using estimations uncertainty and statistical error metrics without effecting the assessment of the groundwater quality. Given the high maintenance cost of groundwater monitoring networks, the proposed tool could used by water regulators in the decision-making process to obtain a efficient network design that is essential.

Geostatistical methods in the assessment of the spatial variability of the quality of river water

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Krasowska Małgorzata

2017-01-01

Full Text Available The research was conducted in the agricultural catchment in north–eastern Poland. The aim of this study was to check how geostatistical analysis can be useful for the detection zones and forms of supply stream by water from different sources. The work was included the implementation of hydrochemical profiles. These profiles were made by measuring the electrical conductivity (EC values and temperature along the river. On the basis of these results, the authors calculated the coefficient of Moran I and performed semivariogram and found that the EC values are correlated on a stretch of about 140 m. This means that the spatial correlation between samples of water in the stream is readable over a distance of about 140 meters. Therefore it is believed that the degree of water mineralization on this section is shaped by water entering the river channel migration in different ways: through tributaries, leachate drainage and surface runoff. In the case of the analyzed catchment, the potential sources of pollution were drainage systems. Therefore, the spatial analysis allowed the identification pollution sources in a catchment, especially in drained agricultural catchments.

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.

Characterizing the spatial structure of endangered species habitat using geostatistical analysis of IKONOS imagery

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Wallace, C.S.A.; Marsh, S.E.

2005-01-01

Our study used geostatistics to extract measures that characterize the spatial structure of vegetated landscapes from satellite imagery for mapping endangered Sonoran pronghorn habitat. Fine spatial resolution IKONOS data provided information at the scale of individual trees or shrubs that permitted analysis of vegetation structure and pattern. We derived images of landscape structure by calculating local estimates of the nugget, sill, and range variogram parameters within 25 ?? 25-m image windows. These variogram parameters, which describe the spatial autocorrelation of the 1-m image pixels, are shown in previous studies to discriminate between different species-specific vegetation associations. We constructed two independent models of pronghorn landscape preference by coupling the derived measures with Sonoran pronghorn sighting data: a distribution-based model and a cluster-based model. The distribution-based model used the descriptive statistics for variogram measures at pronghorn sightings, whereas the cluster-based model used the distribution of pronghorn sightings within clusters of an unsupervised classification of derived images. Both models define similar landscapes, and validation results confirm they effectively predict the locations of an independent set of pronghorn sightings. Such information, although not a substitute for field-based knowledge of the landscape and associated ecological processes, can provide valuable reconnaissance information to guide natural resource management efforts. ?? 2005 Taylor & Francis Group Ltd.

Mapping of Aspergillus Section Nigri in Southern Europe and Israel based on geostatistical analysis.

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Battilani, P; Barbano, C; Marin, S; Sanchis, V; Kozakiewicz, Z; Magan, N

2006-09-01

Geostatistical analysis was applied to the incidence of Aspergillus Section Nigri and A. carbonarius in Southern Europe and Israel for the 3-year period 2001-2003 to facilitate identification of regions of high risk from contamination with these fungi and production of ochratoxin. The highest incidence of black aspergilli was normally observed at harvesting. At this grape growth stage, spatial variability of black aspergilli was significantly related to latitude and longitude, showing a positive West-East and North-South gradient. Predictive maps of infected berries incidence were drawn and showed the same trend in the 3 years, but incidence was highest in 2003, followed by 2001 and 2002. The highest incidence was always observed in Israel, Greece and Southern France, associated with the highest incidence of A. carbonarius. Southern Spain and Southern Italy also had relevant incidence of black aspergilli. The thermo-wetness maps for the 3 years showed a trend similar to the incidence of black aspergilli. The coldest and wettest year was 2002, while 2003 was the hottest and driest, particularly during August, with Israel being the hottest and driest country, followed by Greece and Southern Italy. This indicates that meteorological conditions can contribute to explain spatial distribution variation of black aspergilli within the Mediterranean basin.

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.

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

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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

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

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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

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.

A geostatistical approach to identify and mitigate agricultural nitrous oxide emission hotspots.

Science.gov (United States)

Turner, P A; Griffis, T J; Mulla, D J; Baker, J M; Venterea, R T

2016-12-01

Anthropogenic emissions of nitrous oxide (N 2 O), a trace gas with severe environmental costs, are greatest from agricultural soils amended with nitrogen (N) fertilizer. However, accurate N 2 O emission estimates at fine spatial scales are made difficult by their high variability, which represents a critical challenge for the management of N 2 O emissions. Here, static chamber measurements (n=60) and soil samples (n=129) were collected at approximately weekly intervals (n=6) for 42-d immediately following the application of N in a southern Minnesota cornfield (15.6-ha), typical of the systems prevalent throughout the U.S. Corn Belt. These data were integrated into a geostatistical model that resolved N 2 O emissions at a high spatial resolution (1-m). Field-scale N 2 O emissions exhibited a high degree of spatial variability, and were partitioned into three classes of emission strength: hotspots, intermediate, and coldspots. Rates of emission from hotspots were 2-fold greater than non-hotspot locations. Consequently, 36% of the field-scale emissions could be attributed to hotspots, despite representing only 21% of the total field area. Variations in elevation caused hotspots to develop in predictable locations, which were prone to nutrient and moisture accumulation caused by terrain focusing. Because these features are relatively static, our data and analyses indicate that targeted management of hotspots could efficiently reduce field-scale emissions by as much 17%, a significant benefit considering the deleterious effects of atmospheric N 2 O. Copyright © 2016 Elsevier B.V. All rights reserved.

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

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

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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.

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

Applications of stochastic models and geostatistical analyses to study sources and spatial patterns of soil heavy metals in a metalliferous industrial district of China

International Nuclear Information System (INIS)

Zhong, Buqing; Liang, Tao; Wang, Lingqing; Li, Kexin

2014-01-01

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

A new algorithm combining geostatistics with the surrogate data approach to increase the accuracy of comparisons of point radiation measurements with cloud measurements

Science.gov (United States)

Venema, V. K. C.; Lindau, R.; Varnai, T.; Simmer, C.

2009-04-01

Two main groups of statistical methods used in the Earth sciences are geostatistics and stochastic modelling. Geostatistical methods, such as various kriging algorithms, aim at estimating the mean value for every point as well as possible. In case of sparse measurements, such fields have less variability at small scales and a narrower distribution as the true field. This can lead to biases if a nonlinear process is simulated on such a kriged field. Stochastic modelling aims at reproducing the structure of the data. One of the stochastic modelling methods, the so-called surrogate data approach, replicates the value distribution and power spectrum of a certain data set. However, while stochastic methods reproduce the statistical properties of the data, the location of the measurement is not considered. Because radiative transfer through clouds is a highly nonlinear process it is essential to model the distribution (e.g. of optical depth, extinction, liquid water content or liquid water path) accurately as well as the correlations in the cloud field because of horizontal photon transport. This explains the success of surrogate cloud fields for use in 3D radiative transfer studies. However, up to now we could only achieve good results for the radiative properties averaged over the field, but not for a radiation measurement located at a certain position. Therefore we have developed a new algorithm that combines the accuracy of stochastic (surrogate) modelling with the positioning capabilities of kriging. In this way, we can automatically profit from the large geostatistical literature and software. The algorithm is tested on cloud fields from large eddy simulations (LES). On these clouds a measurement is simulated. From the pseudo-measurement we estimated the distribution and power spectrum. Furthermore, the pseudo-measurement is kriged to a field the size of the final surrogate cloud. The distribution, spectrum and the kriged field are the inputs to the algorithm. This

Massively Parallel Geostatistical Inversion of Coupled Processes in Heterogeneous Porous Media

Science.gov (United States)

Ngo, A.; Schwede, R. L.; Li, W.; Bastian, P.; Ippisch, O.; Cirpka, O. A.

2012-04-01

The quasi-linear geostatistical approach is an inversion scheme that can be used to estimate the spatial distribution of a heterogeneous hydraulic conductivity field. The estimated parameter field is considered to be a random variable that varies continuously in space, meets the measurements of dependent quantities (such as the hydraulic head, the concentration of a transported solute or its arrival time) and shows the required spatial correlation (described by certain variogram models). This is a method of conditioning a parameter field to observations. Upon discretization, this results in as many parameters as elements of the computational grid. For a full three dimensional representation of the heterogeneous subsurface it is hardly sufficient to work with resolutions (up to one million parameters) of the model domain that can be achieved on a serial computer. The forward problems to be solved within the inversion procedure consists of the elliptic steady-state groundwater flow equation and the formally elliptic but nearly hyperbolic steady-state advection-dominated solute transport equation in a heterogeneous porous medium. Both equations are discretized by Finite Element Methods (FEM) using fully scalable domain decomposition techniques. Whereas standard conforming FEM is sufficient for the flow equation, for the advection dominated transport equation, which rises well known numerical difficulties at sharp fronts or boundary layers, we use the streamline diffusion approach. The arising linear systems are solved using efficient iterative solvers with an AMG (algebraic multigrid) pre-conditioner. During each iteration step of the inversion scheme one needs to solve a multitude of forward and adjoint problems in order to calculate the sensitivities of each measurement and the related cross-covariance matrix of the unknown parameters and the observations. In order to reduce interprocess communications and to improve the scalability of the code on larger clusters

Mapping mean annual and monthly river discharges: geostatistical developments for incorporating river network dependencies

International Nuclear Information System (INIS)

Sauquet, Eric

2004-01-01

Regional hydrology is one topic that shows real improvement in partly due to new statistical development and computation facilities. Nevertheless theoretical difficulties for mapping river regime characteristics or recover these features at un gauged location remain because of the nature of the variable under study: river flows are related to a specific area that is defined by the drainage basin, are spatially organised by the river network with upstream-downstream dependencies. Estimations of hydrological descriptors are required for studying links with ecological processes at different spatial scale, from local site where biological or/and water quality data are available to large scale for sustainable development purposes. This presentation aims at describing a method for runoff pattern along the main river network. The approach dedicated to mean annual runoff is based on geostatistical interpolation procedures to which a constraint of water budget has been added. Expansion in Empirical Orthogonal Function has been considered in combination with kriging for interpolating mean monthly discharges. The methodologies are implemented within a Geographical Information System and illustrated by two study cases (two large basins in France). River flow regime descriptors are estimated for basins of more than 50km 2 . Opportunities of collaboration with a partition of France into hydro-eco regions derived from geology and climate considerations is discussed. (Author)

A multivariate geostatistical methodology to delineate areas of potential interest for future sedimentary gold exploration.

Science.gov (United States)

Goovaerts, P; Albuquerque, Teresa; Antunes, Margarida

2016-11-01

This paper describes a multivariate geostatistical methodology to delineate areas of potential interest for future sedimentary gold exploration, with an application to an abandoned sedimentary gold mining region in Portugal. The main challenge was the existence of only a dozen gold measurements confined to the grounds of the old gold mines, which precluded the application of traditional interpolation techniques, such as cokriging. The analysis could, however, capitalize on 376 stream sediment samples that were analyzed for twenty two elements. Gold (Au) was first predicted at all 376 locations using linear regression (R 2 =0.798) and four metals (Fe, As, Sn and W), which are known to be mostly associated with the local gold's paragenesis. One hundred realizations of the spatial distribution of gold content were generated using sequential indicator simulation and a soft indicator coding of regression estimates, to supplement the hard indicator coding of gold measurements. Each simulated map then underwent a local cluster analysis to identify significant aggregates of low or high values. The one hundred classified maps were processed to derive the most likely classification of each simulated node and the associated probability of occurrence. Examining the distribution of the hot-spots and cold-spots reveals a clear enrichment in Au along the Erges River downstream from the old sedimentary mineralization.

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.

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

Energy Technology Data Exchange (ETDEWEB)

El Sebai, T. [UMR Microbiologie et Geochimie des Sols, INRA/CMSE, 17 Rue Sully, BP 86510, 21065 Dijon Cedex (France); Lagacherie, B. [UMR Microbiologie et Geochimie des Sols, INRA/CMSE, 17 Rue Sully, BP 86510, 21065 Dijon Cedex (France); Soulas, G. [UMR Microbiologie et Geochimie des Sols, INRA/CMSE, 17 Rue Sully, BP 86510, 21065 Dijon Cedex (France); Martin-Laurent, F. [UMR Microbiologie et Geochimie des Sols, INRA/CMSE, 17 Rue Sully, BP 86510, 21065 Dijon Cedex (France)]. E-mail: fmartin@dijon.inra.fr

2007-02-15

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. - In field spatial variation of isoproturon mineralization mainly results from the spatial heterogeneity of soil pH and microbial C biomass.

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

International Nuclear Information System (INIS)

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

2007-01-01

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. - In field spatial variation of isoproturon mineralization mainly results from the spatial heterogeneity of soil pH and microbial C biomass

Bayesian geostatistical analysis and prediction of Rhodesian human African trypanosomiasis.

Directory of Open Access Journals (Sweden)

Nicola A Wardrop

2010-12-01

Full Text Available The persistent spread of Rhodesian human African trypanosomiasis (HAT in Uganda in recent years has increased concerns of a potential overlap with the Gambian form of the disease. Recent research has aimed to increase the evidence base for targeting control measures by focusing on the environmental and climatic factors that control the spatial distribution of the disease.One recent study used simple logistic regression methods to explore the relationship between prevalence of Rhodesian HAT and several social, environmental and climatic variables in two of the most recently affected districts of Uganda, and suggested the disease had spread into the study area due to the movement of infected, untreated livestock. Here we extend this study to account for spatial autocorrelation, incorporate uncertainty in input data and model parameters and undertake predictive mapping for risk of high HAT prevalence in future.Using a spatial analysis in which a generalised linear geostatistical model is used in a Bayesian framework to account explicitly for spatial autocorrelation and incorporate uncertainty in input data and model parameters we are able to demonstrate a more rigorous analytical approach, potentially resulting in more accurate parameter and significance estimates and increased predictive accuracy, thereby allowing an assessment of the validity of the livestock movement hypothesis given more robust parameter estimation and appropriate assessment of covariate effects.Analysis strongly supports the theory that Rhodesian HAT was imported to the study area via the movement of untreated, infected livestock from endemic areas. The confounding effect of health care accessibility on the spatial distribution of Rhodesian HAT and the linkages between the disease's distribution and minimum land surface temperature have also been confirmed via the application of these methods.Predictive mapping indicates an increased risk of high HAT prevalence in the future

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

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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

Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping.

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Hampton, Kristen H; Serre, Marc L; Gesink, Dionne C; Pilcher, Christopher D; Miller, William C

2011-10-06

Disease maps of crude rates from routinely collected health data indexed at a small geographical resolution pose specific statistical problems due to the sparse nature of the data. Spatial smoothers allow areas to borrow strength from neighboring regions to produce a more stable estimate of the areal value. Geostatistical smoothers are able to quantify the uncertainty in smoothed rate estimates without a high computational burden. In this paper, we introduce a uniform model extension of Bayesian Maximum Entropy (UMBME) and compare its performance to that of Poisson kriging in measures of smoothing strength and estimation accuracy as applied to simulated data and the real data example of HIV infection in North Carolina. The aim is to produce more reliable maps of disease rates in small areas to improve identification of spatial trends at the local level. In all data environments, Poisson kriging exhibited greater smoothing strength than UMBME. With the simulated data where the true latent rate of infection was known, Poisson kriging resulted in greater estimation accuracy with data that displayed low spatial autocorrelation, while UMBME provided more accurate estimators with data that displayed higher spatial autocorrelation. With the HIV data, UMBME performed slightly better than Poisson kriging in cross-validatory predictive checks, with both models performing better than the observed data model with no smoothing. Smoothing methods have different advantages depending upon both internal model assumptions that affect smoothing strength and external data environments, such as spatial correlation of the observed data. Further model comparisons in different data environments are required to provide public health practitioners with guidelines needed in choosing the most appropriate smoothing method for their particular health dataset.

Training-Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network

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Laloy, Eric; Hérault, Romain; Jacques, Diederik; Linde, Niklas

2018-01-01

Probabilistic inversion within a multiple-point statistics framework is often computationally prohibitive for high-dimensional problems. To partly address this, we introduce and evaluate a new training-image based inversion approach for complex geologic media. Our approach relies on a deep neural network of the generative adversarial network (GAN) type. After training using a training image (TI), our proposed spatial GAN (SGAN) can quickly generate 2-D and 3-D unconditional realizations. A key characteristic of our SGAN is that it defines a (very) low-dimensional parameterization, thereby allowing for efficient probabilistic inversion using state-of-the-art Markov chain Monte Carlo (MCMC) methods. In addition, available direct conditioning data can be incorporated within the inversion. Several 2-D and 3-D categorical TIs are first used to analyze the performance of our SGAN for unconditional geostatistical simulation. Training our deep network can take several hours. After training, realizations containing a few millions of pixels/voxels can be produced in a matter of seconds. This makes it especially useful for simulating many thousands of realizations (e.g., for MCMC inversion) as the relative cost of the training per realization diminishes with the considered number of realizations. Synthetic inversion case studies involving 2-D steady state flow and 3-D transient hydraulic tomography with and without direct conditioning data are used to illustrate the effectiveness of our proposed SGAN-based inversion. For the 2-D case, the inversion rapidly explores the posterior model distribution. For the 3-D case, the inversion recovers model realizations that fit the data close to the target level and visually resemble the true model well.

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.

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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.

Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm

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Asma Shaheen

2018-03-01

Full Text Available In third world countries, industries mainly cause environmental contamination due to lack of environmental policies or oversight during their implementation. The Sheikhupura industrial zone, which includes industries such as tanneries, leather, chemical, textiles, and colour and dyes, contributes massive amounts of untreated effluents that are released directly into drains and used for the irrigation of crops and vegetables. This practice causes not only soil contamination with an excessive amount of heavy metals, but is also considered a source of toxicity in the food chain, i.e., bioaccumulation in plants and ultimately in human body organs. The objective of this research study was to assess the spatial distribution of the heavy metals chromium (Cr, cadmium (Cd, and lead (Pb, at three depths of soil using geostatistics and the selection of significant contributing variables to soil contamination using the Random Forest (RF function of the Boruta Algorithm. A total of 60 sampling locations were selected in the study area to collect soil samples (180 samples at three depths (0–15 cm, 15–30 cm, and 60–90 cm. The soil samples were analysed for their physico-chemical properties, i.e., soil saturation, electrical conductivity (EC, organic matter (OM, pH, phosphorus (P, potassium (K, and Cr, Cd, and Pb using standard laboratory procedures. The data were analysed with comprehensive statistics and geostatistical techniques. The correlation coefficient matrix between the heavy metals and the physico-chemical properties revealed that electrical conductivity (EC had a significant (p ≤ 0.05 negative correlation with Cr, Cd, and Pb. The RF function of the Boruta Algorithm employed soil depth as a classifier and ranked the significant soil contamination parameters (Cr, Cd, Pb, EC, and P in relation to depth. The mobility factor indicated the leachate percentage of heavy metals at different vertical depths of soil. The spatial distribution pattern of

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

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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.

Predicting polycyclic aromatic hydrocarbons using a mass fraction approach in a geostatistical framework across North Carolina.

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Reyes, Jeanette M; Hubbard, Heidi F; Stiegel, Matthew A; Pleil, Joachim D; Serre, Marc L

2018-01-09

Currently in the United States there are no regulatory standards for ambient concentrations of polycyclic aromatic hydrocarbons (PAHs), a class of organic compounds with known carcinogenic species. As such, monitoring data are not routinely collected resulting in limited exposure mapping and epidemiologic studies. This work develops the log-mass fraction (LMF) Bayesian maximum entropy (BME) geostatistical prediction method used to predict the concentration of nine particle-bound PAHs across the US state of North Carolina. The LMF method develops a relationship between a relatively small number of collocated PAH and fine Particulate Matter (PM2.5) samples collected in 2005 and applies that relationship to a larger number of locations where PM2.5 is routinely monitored to more broadly estimate PAH concentrations across the state. Cross validation and mapping results indicate that by incorporating both PAH and PM2.5 data, the LMF BME method reduces mean squared error by 28.4% and produces more realistic spatial gradients compared to the traditional kriging approach based solely on observed PAH data. The LMF BME method efficiently creates PAH predictions in a PAH data sparse and PM2.5 data rich setting, opening the door for more expansive epidemiologic exposure assessments of ambient PAH.

Delineating Facies Spatial Distribution by Integrating Ensemble Data Assimilation and Indicator Geostatistics with Level Set Transformation.

Energy Technology Data Exchange (ETDEWEB)

Hammond, Glenn Edward; Song, Xuehang; Ye, Ming; Dai, Zhenxue; Zachara, John; Chen, Xingyuan

2017-03-01

A new approach is developed to delineate the spatial distribution of discrete facies (geological units that have unique distributions of hydraulic, physical, and/or chemical properties) conditioned not only on direct data (measurements directly related to facies properties, e.g., grain size distribution obtained from borehole samples) but also on indirect data (observations indirectly related to facies distribution, e.g., hydraulic head and tracer concentration). Our method integrates for the first time ensemble data assimilation with traditional transition probability-based geostatistics. The concept of level set is introduced to build shape parameterization that allows transformation between discrete facies indicators and continuous random variables. The spatial structure of different facies is simulated by indicator models using conditioning points selected adaptively during the iterative process of data assimilation. To evaluate the new method, a two-dimensional semi-synthetic example is designed to estimate the spatial distribution and permeability of two distinct facies from transient head data induced by pumping tests. The example demonstrates that our new method adequately captures the spatial pattern of facies distribution by imposing spatial continuity through conditioning points. The new method also reproduces the overall response in hydraulic head field with better accuracy compared to data assimilation with no constraints on spatial continuity on facies.

A Reduced-Order Successive Linear Estimator for Geostatistical Inversion and its Application in Hydraulic Tomography

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Zha, Yuanyuan; Yeh, Tian-Chyi J.; Illman, Walter A.; Zeng, Wenzhi; Zhang, Yonggen; Sun, Fangqiang; Shi, Liangsheng

2018-03-01

Hydraulic tomography (HT) is a recently developed technology for characterizing high-resolution, site-specific heterogeneity using hydraulic data (nd) from a series of cross-hole pumping tests. To properly account for the subsurface heterogeneity and to flexibly incorporate additional information, geostatistical inverse models, which permit a large number of spatially correlated unknowns (ny), are frequently used to interpret the collected data. However, the memory storage requirements for the covariance of the unknowns (ny × ny) in these models are prodigious for large-scale 3-D problems. Moreover, the sensitivity evaluation is often computationally intensive using traditional difference method (ny forward runs). Although employment of the adjoint method can reduce the cost to nd forward runs, the adjoint model requires intrusive coding effort. In order to resolve these issues, this paper presents a Reduced-Order Successive Linear Estimator (ROSLE) for analyzing HT data. This new estimator approximates the covariance of the unknowns using Karhunen-Loeve Expansion (KLE) truncated to nkl order, and it calculates the directional sensitivities (in the directions of nkl eigenvectors) to form the covariance and cross-covariance used in the Successive Linear Estimator (SLE). In addition, the covariance of unknowns is updated every iteration by updating the eigenvalues and eigenfunctions. The computational advantages of the proposed algorithm are demonstrated through numerical experiments and a 3-D transient HT analysis of data from a highly heterogeneous field site.

Assessment of groundwater and soil quality degradation using multivariate and geostatistical analyses, Dakhla Oasis, Egypt

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Masoud, Alaa A.; El-Horiny, Mohamed M.; Atwia, Mohamed G.; Gemail, Khaled S.; Koike, Katsuaki

2018-06-01

Salinization of groundwater and soil resources has long been a serious environmental hazard in arid regions. This study was conducted to investigate and document the factors controlling such salinization and their inter-relationships in the Dakhla Oasis (Egypt). To accomplish this, 60 groundwater samples and 31 soil samples were collected in February 2014. Factor analysis (FA) and hierarchical cluster analysis (HCA) were integrated with geostatistical analyses to characterize the chemical properties of groundwater and soil and their spatial patterns, identify the factors controlling the pattern variability, and clarify the salinization mechanism. Groundwater quality standards revealed emergence of salinization (av. 885.8 mg/L) and extreme occurrences of Fe2+ (av. 17.22 mg/L) and Mn2+ (av. 2.38 mg/L). Soils were highly salt-affected (av. 15.2 dS m-1) and slightly alkaline (av. pH = 7.7). Evaporation and ion-exchange processes governed the evolution of two main water types: Na-Cl (52%) and Ca-Mg-Cl (47%), respectively. Salinization leads the chemical variability of both resources. Distinctive patterns of slight salinization marked the northern part and intense salinization marked the middle and southern parts. Congruence in the resources clusters confirmed common geology, soil types, and urban and agricultural practices. Minimizing the environmental and socioeconomic impacts of the resources salinization urges the need for better understanding of the hydrochemical characteristics and prediction of quality changes.

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

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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.

Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping

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Pilcher Christopher D

2011-10-01

Full Text Available Abstract Background Disease maps of crude rates from routinely collected health data indexed at a small geographical resolution pose specific statistical problems due to the sparse nature of the data. Spatial smoothers allow areas to borrow strength from neighboring regions to produce a more stable estimate of the areal value. Geostatistical smoothers are able to quantify the uncertainty in smoothed rate estimates without a high computational burden. In this paper, we introduce a uniform model extension of Bayesian Maximum Entropy (UMBME and compare its performance to that of Poisson kriging in measures of smoothing strength and estimation accuracy as applied to simulated data and the real data example of HIV infection in North Carolina. The aim is to produce more reliable maps of disease rates in small areas to improve identification of spatial trends at the local level. Results In all data environments, Poisson kriging exhibited greater smoothing strength than UMBME. With the simulated data where the true latent rate of infection was known, Poisson kriging resulted in greater estimation accuracy with data that displayed low spatial autocorrelation, while UMBME provided more accurate estimators with data that displayed higher spatial autocorrelation. With the HIV data, UMBME performed slightly better than Poisson kriging in cross-validatory predictive checks, with both models performing better than the observed data model with no smoothing. Conclusions Smoothing methods have different advantages depending upon both internal model assumptions that affect smoothing strength and external data environments, such as spatial correlation of the observed data. Further model comparisons in different data environments are required to provide public health practitioners with guidelines needed in choosing the most appropriate smoothing method for their particular health dataset.

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.

Diagnostic techniques applied in geostatistics for agricultural data analysis Técnicas de diagnóstico utilizadas em geoestatística para análise de dados agrícolas

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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

Caracterização do solo de cobertura de aterros encerrados com ferramentas (geoestatísticas Characterization of soil covers in closed landfill sites with (geostatistical tools

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Alessandro Samuel-Rosa

2011-06-01

Full Text Available Inúmeros trabalhos abordam a elaboração de estratégias amostrais e a aplicação de ferramentas (geoestatísticas no estudo de atributos do solo. Entretanto, são escassos os trabalhos envolvendo a aplicação desta abordagem no monitoramento de solos construídos sobre aterros encerrados de resíduos sólidos urbanos. Este estudo mostra que a densidade amostral necessária para tornar possível o uso da geoestatística em tais casos, elevaria os custos operacionais. A melhor alternativa é a utilização dos métodos de estatística multivariada (análise de componentes principais e de agrupamento para definição de zonas homogêneas de manejo. Os atributos que melhor explicam a estrutura da variabilidade do solo construído são o teor de areia (ou argila, a saturação por bases e o pH, todos relacionados com a contaminação do solo com chorume e o adequado desenvolvimento da vegetação.Several studies address the development of sampling strategies and implementation of (geostatistical tools in the study of soil properties. However, there is a lack of studies in the application of such approach to monitor soil covers in closed landfill sites of urban solid waste. This study shows that the sampling density needed to make possible the use of geostatistics in such cases would raise operational costs. The best alternative is the use of multivariate statistics methods (principal components and cluster analysis to define homogeneous management zones. The soil attributes that best explain the structure of soil variability are sand (or clay content, base saturation and pH, all related with soil contamination by leachate and with the proper development of vegetation.

Definition of radon prone areas in Friuli Venezia Giulia region, Italy, using geostatistical tools

International Nuclear Information System (INIS)

Cafaro, C.; Bossew, P.; Giovani, C.; Garavaglia, M.

2014-01-01

Studying the geographical distribution of indoor radon concentration, using geostatistical interpolation methods, has become common for predicting and estimating the risk to the population. Here we analyse the case of Friuli Venezia Giulia (FVG), the north easternmost region of Italy. Mean value and standard deviation are, respectively, 153 Bq/m 3 and 183 Bq/m 3 . The geometric mean value is 100 Bq/m 3 . Spatial datasets of indoor radon concentrations are usually affected by clustering and apparent non-stationarity issues, which can eventually yield arguable results. The clustering of the present dataset seems to be non preferential. Therefore the areal estimations are not expected to be affected. Conversely, nothing can be said on the non stationarity issues and its effects. After discussing the correlation of geology with indoor radon concentration It appears they are created by the same geologic features influencing the mean and median values, and can't be eliminated via a map-based approach. To tackle these problems, in this work we deal with multiple definitions of RPA, but only in quaternary areas of FVG, using extensive simulation techniques. - Highlights: • The data are clustered in a preferential way, but natural clustering renders preferentiality undetectable. • Different soil classes lead to different variograms, then the database is divided to improve predictions. • The geological classes do not improve the quality of prediction more than a quadratic drift and yield arguable results. • Simulation conditioned by kriging are used to solve the change of support problem

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

Reservoir Characterization using geostatistical and numerical modeling in GIS with noble gas geochemistry

Science.gov (United States)

Vasquez, D. A.; Swift, J. N.; Tan, S.; Darrah, T. H.

2013-12-01

The integration of precise geochemical analyses with quantitative engineering modeling into an interactive GIS system allows for a sophisticated and efficient method of reservoir engineering and characterization. Geographic Information Systems (GIS) is utilized as an advanced technique for oil field reservoir analysis by combining field engineering and geological/geochemical spatial datasets with the available systematic modeling and mapping methods to integrate the information into a spatially correlated first-hand approach in defining surface and subsurface characteristics. Three key methods of analysis include: 1) Geostatistical modeling to create a static and volumetric 3-dimensional representation of the geological body, 2) Numerical modeling to develop a dynamic and interactive 2-dimensional model of fluid flow across the reservoir and 3) Noble gas geochemistry to further define the physical conditions, components and history of the geologic system. Results thus far include using engineering algorithms for interpolating electrical well log properties across the field (spontaneous potential, resistivity) yielding a highly accurate and high-resolution 3D model of rock properties. Results so far also include using numerical finite difference methods (crank-nicholson) to solve for equations describing the distribution of pressure across field yielding a 2D simulation model of fluid flow across reservoir. Ongoing noble gas geochemistry results will also include determination of the source, thermal maturity and the extent/style of fluid migration (connectivity, continuity and directionality). Future work will include developing an inverse engineering algorithm to model for permeability, porosity and water saturation.This combination of new and efficient technological and analytical capabilities is geared to provide a better understanding of the field geology and hydrocarbon dynamics system with applications to determine the presence of hydrocarbon pay zones (or

Improving Classification of Airborne Laser Scanning Echoes in the Forest-Tundra Ecotone Using Geostatistical and Statistical Measures

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Nadja Stumberg

2014-05-01

Full Text Available The vegetation in the forest-tundra ecotone zone is expected to be highly affected by climate change and requires effective monitoring techniques. Airborne laser scanning (ALS has been proposed as a tool for the detection of small pioneer trees for such vast areas using laser height and intensity data. The main objective of the present study was to assess a possible improvement in the performance of classifying tree and nontree laser echoes from high-density ALS data. The data were collected along a 1000 km long transect stretching from southern to northern Norway. Different geostatistical and statistical measures derived from laser height and intensity values were used to extent and potentially improve more simple models ignoring the spatial context. Generalised linear models (GLM and support vector machines (SVM were employed as classification methods. Total accuracies and Cohen’s kappa coefficients were calculated and compared to those of simpler models from a previous study. For both classification methods, all models revealed total accuracies similar to the results of the simpler models. Concerning classification performance, however, the comparison of the kappa coefficients indicated a significant improvement for some models both using GLM and SVM, with classification accuracies >94%.

Determination of geostatistically representative sampling locations in Porsuk Dam Reservoir (Turkey)

Science.gov (United States)

Aksoy, A.; Yenilmez, F.; Duzgun, S.

2013-12-01

Several factors such as wind action, bathymetry and shape of a lake/reservoir, inflows, outflows, point and diffuse pollution sources result in spatial and temporal variations in water quality of lakes and reservoirs. The guides by the United Nations Environment Programme and the World Health Organization to design and implement water quality monitoring programs suggest that even a single monitoring station near the center or at the deepest part of a lake will be sufficient to observe long-term trends if there is good horizontal mixing. In stratified water bodies, several samples can be required. According to the guide of sampling and analysis under the Turkish Water Pollution Control Regulation, a minimum of five sampling locations should be employed to characterize the water quality in a reservoir or a lake. The European Union Water Framework Directive (2000/60/EC) states to select a sufficient number of monitoring sites to assess the magnitude and impact of point and diffuse sources and hydromorphological pressures in designing a monitoring program. Although existing regulations and guidelines include frameworks for the determination of sampling locations in surface waters, most of them do not specify a procedure in establishment of monitoring aims with representative sampling locations in lakes and reservoirs. In this study, geostatistical tools are used to determine the representative sampling locations in the Porsuk Dam Reservoir (PDR). Kernel density estimation and kriging were used in combination to select the representative sampling locations. Dissolved oxygen and specific conductivity were measured at 81 points. Sixteen of them were used for validation. In selection of the representative sampling locations, care was given to keep similar spatial structure in distributions of measured parameters. A procedure was proposed for that purpose. Results indicated that spatial structure was lost under 30 sampling points. This was as a result of varying water

Geostatistical approach for assessing soil volumes requiring remediation: validation using lead-polluted soils underlying a former smelting works.

Science.gov (United States)

Demougeot-Renard, Helene; De Fouquet, Chantal

2004-10-01

Assessing the volume of soil requiring remediation and the accuracy of this assessment constitutes an essential step in polluted site management. If this remediation volume is not properly assessed, misclassification may lead both to environmental risks (polluted soils may not be remediated) and financial risks (unexpected discovery of polluted soils may generate additional remediation costs). To minimize such risks, this paper proposes a geostatistical methodology based on stochastic simulations that allows the remediation volume and the uncertainty to be assessed using investigation data. The methodology thoroughly reproduces the conditions in which the soils are classified and extracted at the remediation stage. The validity of the approach is tested by applying it on the data collected during the investigation phase of a former lead smelting works and by comparing the results with the volume that has actually been remediated. This real remediated volume was composed of all the remediation units that were classified as polluted after systematic sampling and analysis during clean-up stage. The volume estimated from the 75 samples collected during site investigation slightly overestimates (5.3% relative error) the remediated volume deduced from 212 remediation units. Furthermore, the real volume falls within the range of uncertainty predicted using the proposed methodology.

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

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Namysłowska-Wilczyńska Barbara

2016-09-01

Full Text Available 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.

Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models.

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Scholte, Ronaldo G C; Schur, Nadine; Bavia, Maria E; Carvalho, Edgar M; Chammartin, Frédérique; Utzinger, Jürg; Vounatsou, Penelope

2013-11-01

Soil-transmitted helminths (Ascaris lumbricoides, Trichuris trichiura and hookworm) negatively impact the health and wellbeing of hundreds of millions of people, particularly in tropical and subtropical countries, including Brazil. Reliable maps of the spatial distribution and estimates of the number of infected people are required for the control and eventual elimination of soil-transmitted helminthiasis. We used advanced Bayesian geostatistical modelling, coupled with geographical information systems and remote sensing to visualize the distribution of the three soil-transmitted helminth species in Brazil. Remotely sensed climatic and environmental data, along with socioeconomic variables from readily available databases were employed as predictors. Our models provided mean prevalence estimates for A. lumbricoides, T. trichiura and hookworm of 15.6%, 10.1% and 2.5%, respectively. By considering infection risk and population numbers at the unit of the municipality, we estimate that 29.7 million Brazilians are infected with A. lumbricoides, 19.2 million with T. trichiura and 4.7 million with hookworm. Our model-based maps identified important risk factors related to the transmission of soiltransmitted helminths and confirm that environmental variables are closely associated with indices of poverty. Our smoothed risk maps, including uncertainty, highlight areas where soil-transmitted helminthiasis control interventions are most urgently required, namely in the North and along most of the coastal areas of Brazil. We believe that our predictive risk maps are useful for disease control managers for prioritising control interventions and for providing a tool for more efficient surveillance-response mechanisms.

Identification of the Hydrogeochemical Processes in Groundwater Using Classic Integrated Geochemical Methods and Geostatistical Techniques, in Amol-Babol Plain, Iran

Science.gov (United States)

Sheikhy Narany, Tahoora; Ramli, Mohammad Firuz; Aris, Ahmad Zaharin; Sulaiman, Wan Nor Azmin; Juahir, Hafizan; Fakharian, Kazem

2014-01-01

Hydrogeochemical investigations had been carried out at the Amol-Babol Plain in the north of Iran. Geochemical processes and factors controlling the groundwater chemistry are identified based on the combination of classic geochemical methods with geographic information system (GIS) and geostatistical techniques. The results of the ionic ratios and Gibbs plots show that water rock interaction mechanisms, followed by cation exchange, and dissolution of carbonate and silicate minerals have influenced the groundwater chemistry in the study area. The hydrogeochemical characteristics of groundwater show a shift from low mineralized Ca-HCO3, Ca-Na-HCO3, and Ca-Cl water types to high mineralized Na-Cl water type. Three classes, namely, C1, C2, and C3, have been classified using cluster analysis. The spatial distribution maps of Na+/Cl−, Mg2+/Ca2+, and Cl−/HCO3 − ratios and electrical conductivity values indicate that the carbonate and weathering of silicate minerals played a significant role in the groundwater chemistry on the southern and western sides of the plain. However, salinization process had increased due to the influence of the evaporation-precipitation process towards the north-eastern side of the study area. PMID:24523640

Assessment of ground-water flow and chemical transport in a tidally influenced aquifer using geostatistical filtering and hydrocarbon fingerprinting

International Nuclear Information System (INIS)

Marquis, S.A. Jr.; Smith, E.A.

1994-01-01

Traditional environmental investigations at tidally influenced hazardous waste sites such as marine fuel storage terminals have generally failed to characterize ground-water flow and chemical transport because they have been based on only a cursory knowledge of plume geometry, chemicals encountered, and hydrogeologic setting and synoptic ground-water level measurement. Single-time observations cannot be used to accurately determine flow direction and gradient in tidally fluctuating aquifers since these measurements delineate hydraulic head at only one point in time during a tidal cycle, not the net effect of the fluctuations. In this study, a more rigorous approach was used to characterize flow and chemical transport in a tidally influenced aquifer at a marine fuel storage terminal using: (1) ground-water-level monitoring over three tidal cycles (72 hours), (2) geostatistical filtering of ground-water-level data using 25-hour and 71-hour filtering methods, and (3) hydrocarbon fingerprinting analysis. The results from the study indicate that naphtha released from one of the on-site naphtha tanks has been the predominant contributor to the hydrocarbon plume both on-site and downgradient off-site and that net ground-water and hydrocarbon movement has been to the southeast away from the tank since 1989

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

International Nuclear Information System (INIS)

Wingle, W.L.; Poeter, E.P.; McKenna, S.A.

1999-01-01

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. (Copyright (c) 1999 Elsevier Science B.V., Amsterdam. All rights reserved.)

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)

Statistics and geostatistics: Kriging and use of hemivariogram functions in the structural investigation of uranium deposits

International Nuclear Information System (INIS)

Lucero Michaut, H.N.

1980-01-01

After presenting some general conceptual considerations regarding the theory of regionalized variables, the paper deals with specific applications of the intrinsic dispersion law to the determination, description and quantification of structures. It then briefly describes two uranium deposits in Cordoba province, the study of which yielded the basic data and parameters for compiling the geostatistical results presented. Before taking up the matter of structural interpretations, it refers briefly to the mathematical relationship between the number of sampling points available and the number of directions that can be investigated by the variogram method and also emphasizes the need for quantifying regionalization concepts on the basis of a table of absolute dimensionalities. In the case of the ''Rodolfo'' deposit it presents and comments on the hemivariograms for concentrations, thicknesses and accumulations, drawing attention at the same time to the existence of significant nest-like phenomena (gigogne structures). In this connection there is also a discussion of the case of iterative lenticular mineralization on a natural and a simulated model. The ''Schlagintweit'' deposit is dealt with in the same way, with descriptions and evaluations of the subjacent structures revealed by the hemivariographic analysis of grades, mineralization thicknesses and accumulations. This is followed by some considerations on the possibility of applying Krige and Matheron correctors in the moderation of anomalous mineralized thicknesses. In conclusion, the paper presents a ''range ellipse'' for grades; this is designed to supplement the grid of sampling points for the ''Rodolfo'' deposit by means of Matheronian kriging techniques. (author)

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

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)

Geostatistics as a tool to improve the natural background level definition: An application in groundwater.

Science.gov (United States)

Dalla Libera, Nico; Fabbri, Paolo; Mason, Leonardo; Piccinini, Leonardo; Pola, Marco

2017-11-15

The Natural Background Level (NBL), suggested by UE BRIDGE project, is suited for spatially distributed datasets providing a regional value that could be higher than the Threshold Value (TV) set by every country. In hydro-geochemically dis-homogeneous areas, the use of a unique regional NBL, higher than TV, could arise problems to distinguish between natural occurrences and anthropogenic contaminant sources. Hence, the goal of this study is to improve the NBL definition employing a geostatistical approach, which reconstructs the contaminant spatial structure accounting geochemical and hydrogeological relationships. This integrated mapping is fundamental to evaluate the contaminant's distribution impact on the NBL, giving indications to improve it. We decided to test this method on the Drainage Basin of Venice Lagoon (DBVL, NE Italy), where the existing NBL is seven times higher than the TV. This area is notoriously affected by naturally occurring arsenic contamination. An available geochemical dataset collected by 50 piezometers was used to reconstruct the spatial distribution of arsenic in the densely populated area of the DBVL. A cokriging approach was applied exploiting the geochemical relationships among As, Fe and NH4+. The obtained spatial predictions of arsenic concentrations were divided into three different zones: i) areas with an As concentration lower than the TV, ii) areas with an As concentration between the TV and the median of the values higher than the TV, and iii) areas with an As concentration higher than the median. Following the BRIDGE suggestions, where enough samples were available, the 90th percentile for each zone was calculated to obtain a local NBL (LNBL). Differently from the original NBL, this local value gives more detailed water quality information accounting the hydrogeological and geochemical setting, and contaminant spatial variation. Hence, the LNBL could give more indications about the distinction between natural occurrence and

A Combined Approach of Sensor Data Fusion and Multivariate Geostatistics for Delineation of Homogeneous Zones in an Agricultural Field

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Annamaria Castrignanò

2017-12-01

Full Text Available To assess spatial variability at the very fine scale required by Precision Agriculture, different proximal and remote sensors have been used. They provide large amounts and different types of data which need to be combined. An integrated approach, using multivariate geostatistical data-fusion techniques and multi-source geophysical sensor data to determine simple summary scale-dependent indices, is described here. These indices can be used to delineate management zones to be submitted to differential management. Such a data fusion approach with geophysical sensors was applied in a soil of an agronomic field cropped with tomato. The synthetic regionalized factors determined, contributed to split the 3D edaphic environment into two main horizontal structures with different hydraulic properties and to disclose two main horizons in the 0–1.0-m depth with a discontinuity probably occurring between 0.40 m and 0.70 m. Comparing this partition with the soil properties measured with a shallow sampling, it was possible to verify the coherence in the topsoil between the dielectric properties and other properties more directly related to agronomic management. These results confirm the advantages of using proximal sensing as a preliminary step in the application of site-specific management. Combining disparate spatial data (data fusion is not at all a naive problem and novel and powerful methods need to be developed.

A Combined Approach of Sensor Data Fusion and Multivariate Geostatistics for Delineation of Homogeneous Zones in an Agricultural Field.

Science.gov (United States)

Castrignanò, Annamaria; Buttafuoco, Gabriele; Quarto, Ruggiero; Vitti, Carolina; Langella, Giuliano; Terribile, Fabio; Venezia, Accursio

2017-12-03

To assess spatial variability at the very fine scale required by Precision Agriculture, different proximal and remote sensors have been used. They provide large amounts and different types of data which need to be combined. An integrated approach, using multivariate geostatistical data-fusion techniques and multi-source geophysical sensor data to determine simple summary scale-dependent indices, is described here. These indices can be used to delineate management zones to be submitted to differential management. Such a data fusion approach with geophysical sensors was applied in a soil of an agronomic field cropped with tomato. The synthetic regionalized factors determined, contributed to split the 3D edaphic environment into two main horizontal structures with different hydraulic properties and to disclose two main horizons in the 0-1.0-m depth with a discontinuity probably occurring between 0.40 m and 0.70 m. Comparing this partition with the soil properties measured with a shallow sampling, it was possible to verify the coherence in the topsoil between the dielectric properties and other properties more directly related to agronomic management. These results confirm the advantages of using proximal sensing as a preliminary step in the application of site-specific management. Combining disparate spatial data (data fusion) is not at all a naive problem and novel and powerful methods need to be developed.

GEOSTATISTICAL BASED SUSCEPTIBILITY MAPPING OF SOIL EROSION AND OPTIMIZATION OF ITS CAUSATIVE FACTORS: A CONCEPTUAL FRAMEWORK

Directory of Open Access Journals (Sweden)

ABDULKADIR T. SHOLAGBERU

2017-11-01

Full Text Available Soil erosion hazard is the second biggest environmental challenges after population growth causing land degradation, desertification and water deterioration. Its impacts on watersheds include loss of soil nutrients, reduced reservoir capacity through siltation which may lead to flood risk, landslide, high water turbidity, etc. These problems become more pronounced in human altered mountainous areas through intensive agricultural activities, deforestation and increased urbanization among others. However, due to challenging nature of soil erosion management, there is great interest in assessing its spatial distribution and susceptibility levels. This study is thus intend to review the recent literatures and develop a novel framework for soil erosion susceptibility mapping using geostatistical based support vector machine (SVM, remote sensing and GIS techniques. The conceptual framework is to bridge the identified knowledge gaps in the area of causative factors’ (CFs selection. In this research, RUSLE model, field studies and the existing soil erosion maps for the study area will be integrated for the development of inventory map. Spatial data such as Landsat 8, digital soil and geological maps, digital elevation model and hydrological data shall be processed for the extraction of erosion CFs. GISbased SVM techniques will be adopted for the establishment of spatial relationships between soil erosion and its CFs, and subsequently for the development of erosion susceptibility maps. The results of this study include evaluation of predictive capability of GIS-based SVM in soil erosion mapping and identification of the most influential CFs for erosion susceptibility assessment. This study will serve as a guide to watershed planners and to alleviate soil erosion challenges and its related hazards.

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.

Geostatistical Modelling of the Travertine Formation Associated with the Alicun de las Torres Thermal System by Using Electrical Tomography and Porosity Data

International Nuclear Information System (INIS)

Prado Perez, A. J.; Aracil, E.; Perez del Villar, L.

2010-01-01

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 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 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.

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

Lattice Boltzmann Simulations of Fluid Flow in Continental Carbonate Reservoir Rocks and in Upscaled Rock Models Generated with Multiple-Point Geostatistics

Directory of Open Access Journals (Sweden)

J. Soete

2017-01-01

Full Text Available Microcomputed tomography (μCT and Lattice Boltzmann Method (LBM simulations were applied to continental carbonates to quantify fluid flow. Fluid flow characteristics in these complex carbonates with multiscale pore networks are unique and the applied method allows studying their heterogeneity and anisotropy. 3D pore network models were introduced to single-phase flow simulations in Palabos, a software tool for particle-based modelling of classic computational fluid dynamics. In addition, permeability simulations were also performed on rock models generated with multiple-point geostatistics (MPS. This allowed assessing the applicability of MPS in upscaling high-resolution porosity patterns into large rock models that exceed the volume limitations of the μCT. Porosity and tortuosity control fluid flow in these porous media. Micro- and mesopores influence flow properties at larger scales in continental carbonates. Upscaling with MPS is therefore necessary to overcome volume-resolution problems of CT scanning equipment. The presented LBM-MPS workflow is applicable to other lithologies, comprising different pore types, shapes, and pore networks altogether. The lack of straightforward porosity-permeability relationships in complex carbonates highlights the necessity for a 3D approach. 3D fluid flow studies provide the best understanding of flow through porous media, which is of crucial importance in reservoir modelling.

Verification of the geostatistical inference code INFERENS, Version 1.1, and demonstration using data from Finnsjoen

International Nuclear Information System (INIS)

Geier, J.

1993-06-01

This report describes preliminary verification and demonstration of the geostatistical inference code, INFERENS Version 1.1. This code performs regularization of packer test conductivities, and iterative generalized least-squares estimation (IGLSE) of nested covariance models and spatial trends for the regularized data. Cross-validation is used to assess the quality of the estimated models in terms of statistics for the kriging errors. The code includes a capability to generate synthetic datasets for a given configuration of packer tests; this capability can be used for verification exercises and numerical experiments to aid in the design of packer testing programs. The report presents the results of a set of verification test cases. The test cases were designed to test the ability of INFERENS 1.1 to estimate the parameters of a variety of covariance models, with or without trends. This was done using synthetic datasets. This report also describes an application of INFERENS 1.1 to the dataset from the Finnsjoen site. The results are roughly similar to those obtained previously by Norman (1992a) using INFERENS 1.0, for the comparable cases. The actual numerical results are different, which may be due to changes in the fitting algorithms, and differences in how the lag pairs are divided into lag classes. The demonstrations confirm the result previously obtained by Norman, that the fitted horizontally isotropic models are less good, in terms of their cross-validation statistics, than the corresponding isotropic models. The use of nested covariance models is demonstrated to give visually improved fits to the sample semivariograms, at both short and long lag distances. However, despite the good match to the semivariograms, the nested models obtained are not better than the simple models, in terms of cross-validation statistics

A Novel Approach of Understanding and Incorporating Error of Chemical Transport Models into a Geostatistical Framework

Science.gov (United States)

Reyes, J.; Vizuete, W.; Serre, M. L.; Xu, Y.

2015-12-01

The EPA employs a vast monitoring network to measure ambient PM2.5 concentrations across the United States with one of its goals being to quantify exposure within the population. However, there are several areas of the country with sparse monitoring spatially and temporally. One means to fill in these monitoring gaps is to use PM2.5 modeled estimates from Chemical Transport Models (CTMs) specifically the Community Multi-scale Air Quality (CMAQ) model. CMAQ is able to provide complete spatial coverage but is subject to systematic and random error due to model uncertainty. Due to the deterministic nature of CMAQ, often these uncertainties are not quantified. Much effort is employed to quantify the efficacy of these models through different metrics of model performance. Currently evaluation is specific to only locations with observed data. Multiyear studies across the United States are challenging because the error and model performance of CMAQ are not uniform over such large space/time domains. Error changes regionally and temporally. Because of the complex mix of species that constitute PM2.5, CMAQ error is also a function of increasing PM2.5 concentration. To address this issue we introduce a model performance evaluation for PM2.5 CMAQ that is regionalized and non-linear. This model performance evaluation leads to error quantification for each CMAQ grid. Areas and time periods of error being better qualified. The regionalized error correction approach is non-linear and is therefore more flexible at characterizing model performance than approaches that rely on linearity assumptions and assume homoscedasticity of CMAQ predictions errors. Corrected CMAQ data are then incorporated into the modern geostatistical framework of Bayesian Maximum Entropy (BME). Through cross validation it is shown that incorporating error-corrected CMAQ data leads to more accurate estimates than just using observed data by themselves.

Quantifying aggregated uncertainty in Plasmodium falciparum malaria prevalence and populations at risk via efficient space-time geostatistical joint simulation.

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Gething, Peter W; Patil, Anand P; Hay, Simon I

2010-04-01

Risk maps estimating the spatial distribution of infectious diseases are required to guide public health policy from local to global scales. The advent of model-based geostatistics (MBG) has allowed these maps to be generated in a formal statistical framework, providing robust metrics of map uncertainty that enhances their utility for decision-makers. In many settings, decision-makers require spatially aggregated measures over large regions such as the mean prevalence within a country or administrative region, or national populations living under different levels of risk. Existing MBG mapping approaches provide suitable metrics of local uncertainty--the fidelity of predictions at each mapped pixel--but have not been adapted for measuring uncertainty over large areas, due largely to a series of fundamental computational constraints. Here the authors present a new efficient approximating algorithm that can generate for the first time the necessary joint simulation of prevalence values across the very large prediction spaces needed for global scale mapping. This new approach is implemented in conjunction with an established model for P. falciparum allowing robust estimates of mean prevalence at any specified level of spatial aggregation. The model is used to provide estimates of national populations at risk under three policy-relevant prevalence thresholds, along with accompanying model-based measures of uncertainty. By overcoming previously unchallenged computational barriers, this study illustrates how MBG approaches, already at the forefront of infectious disease mapping, can be extended to provide large-scale aggregate measures appropriate for decision-makers.

A geostatistical analysis of the association between armed conflicts and Plasmodium falciparum malaria in Africa, 1997-2010.

Science.gov (United States)

Sedda, Luigi; Qi, Qiuyin; Tatem, Andrew J

2015-12-16

The absence of conflict in a country has been cited as a crucial factor affecting the operational feasibility of achieving malaria control and elimination, yet mixed evidence exists on the influence that conflicts have had on malaria transmission. Over the past two decades, Africa has seen substantial numbers of armed conflicts of varying length and scale, creating conditions that can disrupt control efforts and impact malaria transmission. However, very few studies have quantitatively assessed the associations between conflicts and malaria transmission, particularly in a consistent way across multiple countries. In this analysis an explicit geostatistical, autoregressive, mixed model is employed to quantitatively assess the association between conflicts and variations in Plasmodium falciparum parasite prevalence across a 13-year period in sub-Saharan Africa. Analyses of geolocated, malaria prevalence survey variations against armed conflict data in general showed a wide, but short-lived impact of conflict events geographically. The number of countries with decreased P. falciparum parasite prevalence (17) is larger than the number of countries with increased transmission (12), and notably, some of the countries with the highest transmission pre-conflict were still found with lower transmission post-conflict. For four countries, there were no significant changes in parasite prevalence. Finally, distance from conflicts, duration of conflicts, violence of conflict, and number of conflicts were significant components in the model explaining the changes in P. falciparum parasite rate. The results suggest that the maintenance of intervention coverage and provision of healthcare in conflict situations to protect vulnerable populations can maintain gains in even the most difficult of circumstances, and that conflict does not represent a substantial barrier to elimination goals.

Development of A Bayesian Geostatistical Data Assimilation Method and Application to the Hanford 300 Area

Science.gov (United States)

Murakami, Haruko

Probabilistic risk assessment of groundwater contamination requires us to incorporate large and diverse datasets at the site into the stochastic modeling of flow and transport for prediction. In quantifying the uncertainty in our predictions, we must not only combine the best estimates of the parameters based on each dataset, but also integrate the uncertainty associated with each dataset caused by measurement errors and limited number of measurements. This dissertation presents a Bayesian geostatistical data assimilation method that integrates various types of field data for characterizing heterogeneous hydrological properties. It quantifies the parameter uncertainty as a posterior distribution conditioned on all the datasets, which can be directly used in stochastic simulations to compute possible outcomes of flow and transport processes. The goal of this framework is to remove the discontinuity between data analysis and prediction. Such a direct connection between data and prediction also makes it possible to evaluate the worth of each dataset or combined worth of multiple datasets. The synthetic studies described here confirm that the data assimilation method introduced in this dissertation successfully captures the true parameter values and predicted values within the posterior distribution. The shape of the inferred posterior distributions from the method indicates the importance of estimating the entire distribution in fully accounting for parameter uncertainty. The method is then applied to integrate multiple types of datasets at the Hanford 300 Area for characterizing a three-dimensional heterogeneous hydraulic conductivity field. Comparing the results based on the different numbers or combinations of datasets shows that increasing data do not always contribute in a straightforward way to improving the posterior distribution: increasing numbers of the same data type would not necessarily be beneficial above a certain number, and also the combined effect of

Geostatistical characterization of the Callovo-Oxfordian clay variability: from conventional and high resolution log data

International Nuclear Information System (INIS)

Lefranc, Marie

2007-01-01

Andra (National Radioactive Waste Management Agency) has conducted studies in its Meuse/Haute-Marne Underground Research Laboratory located at a depth of about 490 m in a 155-million-year-old argillaceous rock: the Callovo-Oxfordian argillite. The purpose of the present work is to obtain as much information as possible from high-resolution log data and to optimize their analysis to specify and characterize space-time variations of the argillites from the Meuse/Haute-Marne site and subsequently predict the evolution of argillite properties on a 250 km 2 zone around the underground laboratory (transposition zone). The aim is to outline a methodology to transform depth intervals into geological time intervals and thus to quantify precisely the sedimentation rate variation, estimate duration; for example the duration of bio-stratigraphical units or of hiatuses. The latter point is particularly important because a continuous time recording is often assumed in geological modelling. The spatial variations can be studied on various scales. First, well-to-well correlations are established between seven wells at different scales. Relative variations of the thickness are observed locally. Second, FMI (Full-bore Formation Micro-Imager, Schlumberger) data are studied in detail to extract as much information as possible. For example, the analysis of FMI images reveals a clear carbonate - clay inter-bedding which displays cycles. Third, geostatistical tools are used to study these cycles. The vario-graphic analysis of conventional log data shows one metre cycles. With FMI data, smaller periods can be detected. Variogram modelling and factorial kriging analysis suggest that three spatial periods exist. They vary vertically and laterally in the boreholes but cycle ratios are stable and similar to orbital-cycle ratios (Milankovitch cycles). The three periods correspond to eccentricity, obliquity and precession. Since the duration of these orbital cycles is known, depth intervals can

Geochemical mapping in polluted floodplains using handheld XRF, geophysical imaging, and geostatistics

Science.gov (United States)

Hošek, Michal; Matys Grygar, Tomáš; Popelka, Jan; Kiss, Timea; Elznicová, Jitka; Faměra, Martin

2017-04-01

units. Those findings must, however, be checked by sediment examination and analysis in selected points. We processed the crucial characteristics obtained by geochemical mapping, namely depth of maximum pollution, amount of contamination, and lithology (Al/Si and Zr/Rb ratios), using geostatistics. Moreover, some parts of floodplain were dated by optically stimulated luminescence (OSL) which revealed, that recycling of top decimetres of floodplain fine fill (silts) in Boreček site has proceeded relatively recently (in decades and centuries) as compared to deeper lying coarser (sandy) strata (millennia). The results of geochemical mapping show complexity of pollution hotspots and need of their integrated interpretation. Key words: Dipole electromagneting profilling, electric resistivity tomography, floodplain contamination, geochemical mapping

Survey and Zoning of Soil Physical and Chemical Properties Using Geostatistical Methods in GIS (Case Study: Miankangi Region in Sistan

Directory of Open Access Journals (Sweden)

M. Hashemi

2017-02-01

Full Text Available Introduction: In order to provide a database, it is essential having access to accurate information on soil spatial variation for soil sustainable management such as proper application of fertilizers. Spatial variations in soil properties are common but it is important for understanding these changes, particularly in agricultural lands for careful planning and land management. Materials and Methods: To this end, in winter 1391, 189 undisturbed soil samples (0-30 cm depth in a regular lattice with a spacing of 500 m were gathered from the surface of Miankangi land, Sistan plain, and their physical and chemical properties were studied. The land area of the region is about 4,500 hectares; the average elevation of studied area is 489.2 meters above sea level with different land uses. Soil texture was measured by the hydrometer methods (11, Also EC and pH (39, calcium carbonate equivalent (37 and the saturation percentage of soils were determined. Kriging, Co-Kriging, Inverse Distance Weighting and Local Polynomial Interpolation techniques were evaluated to produce a soil characteristics map of the study area zoning and to select the best geostatistical methods. Cross-validation techniques and Root Mean Square Error (RMSE were used. Results and Discussion: Normalized test results showed that all of the soil properties except calcium carbonate and soil clay content had normal distribution. In addition, the results of correlation test showed that the soil saturation percentage was positively correlated with silt content (r=0.43 and p

Time-lapse analysis of methane quantity in Mary Lee group of coal seams using filter-based multiple-point geostatistical simulation

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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.

Quantifying the exposure of humans and the environment to oil pollution in the Niger Delta using advanced geostatistical techniques.

Science.gov (United States)

Obida, Christopher B; Alan Blackburn, G; Duncan Whyatt, J; Semple, Kirk T

2018-02-01

The Niger Delta is one of the largest oil producing regions of the world. Large numbers and volumes of oil spills have been reported in this region. What has not been quantified is the putative exposure of humans and/or the environment to this hydrocarbon pollution. In this novel study, advanced geostatistical techniques were applied to an extensive database of oil spill incidents from 2007 to 2015. The aims were to (i) identify and analyse spill hotspots along the oil pipeline network and (ii) estimate the exposure of the hydrocarbon pollution to the human population and the environment within the Niger Delta. Over the study period almost 90millionlitres of oil were released. Approximately 29% of the human population living in proximity to the pipeline network has been potentially exposed to oil contamination, of which 565,000 people live within high or very high spill intensity sectors. Over 1000km 2 of land has been contaminated by oil pollution, with broadleaved forest, mangroves and agricultural land the most heavily impacted land cover types. Proximity to the coast, roads and cities are the strongest spatial factors contributing to spill occurrence, which largely determine the accessibility of sites for pipeline sabotage and oil theft. Overall, the findings demonstrate the high levels of environmental and human exposure to hydrocarbon pollutants in the Niger Delta. These results provide evidence with which to spatially target interventions to reduce future spill incidents and mitigate the impacts of previous spills on human communities and ecosystem health. Copyright © 2017 Elsevier Ltd. All rights reserved.

Geostatistical Model-Based Estimates of Schistosomiasis Prevalence among Individuals Aged ≤20 Years in West Africa

Science.gov (United States)

Schur, Nadine; Hürlimann, Eveline; Garba, Amadou; Traoré, Mamadou S.; Ndir, Omar; Ratard, Raoult C.; Tchuem Tchuenté, Louis-Albert; Kristensen, Thomas K.; Utzinger, Jürg; Vounatsou, Penelope

2011-01-01

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/Significance We

A Geostatistical Toolset for Reconstructing Louisiana's Coastal Stratigraphy using Subsurface Boring and Cone Penetrometer Test Data

Science.gov (United States)

Li, A.; Tsai, F. T. C.; Jafari, N.; Chen, Q. J.; Bentley, S. J.

2017-12-01

A vast area of river deltaic wetlands stretches across southern Louisiana coast. The wetlands are suffering from a high rate of land loss, which increasingly threats coastal community and energy infrastructure. A regional stratigraphic framework of the delta plain is now imperative to answer scientific questions (such as how the delta plain grows and decays?) and to provide information to coastal protection and restoration projects (such as marsh creation and construction of levees and floodwalls). Through years, subsurface investigations in Louisiana have been conducted by state and federal agencies (Louisiana Department of Natural Resources, United States Geological Survey, United States Army Corps of Engineers, etc.), research institutes (Louisiana Geological Survey, LSU Coastal Studies Institute, etc.), engineering firms, and oil-gas companies. This has resulted in the availability of various types of data, including geological, geotechnical, and geophysical data. However, it is challenging to integrate different types of data and construct three-dimensional stratigraphy models in regional scale. In this study, a set of geostatistical methods were used to tackle this problem. An ordinary kriging method was used to regionalize continuous data, such as grain size, water content, liquid limit, plasticity index, and cone penetrometer tests (CPTs). Indicator kriging and multiple indicator kriging methods were used to regionalize categorized data, such as soil classification. A compositional kriging method was used to regionalize compositional data, such as soil composition (fractions of sand, silt and clay). Stratigraphy models were constructed for three cases in the coastal zone: (1) Inner Harbor Navigation Canal (IHNC) area: soil classification and soil behavior type (SBT) stratigraphies were constructed using ordinary kriging; (2) Middle Barataria Bay area: a soil classification stratigraphy was constructed using multiple indicator kriging; (3) Lower Barataria

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

Integration of DAS (distributed acoustic sensing) vertical seismic profile and geostatistically modeled lithology data to characterize an enhanced geothermal system.

Science.gov (United States)

Cronin, S. P.; Trainor Guitton, W.; Team, P.; Pare, A.; Jreij, S.; Powers, H.

2017-12-01

In March 2016, a 4-week field data acquisition took place at Brady's Natural Lab (BNL), an enhanced geothermal system (EGS) in Fallan, NV. During these 4 weeks, a vibe truck executed 6,633 sweeps, recorded by nodal seismometers, horizontal distributed acoustic sensing (DAS) cable, and 400 meters of vertical DAS cable. DAS provides lower signal to noise ratio than traditional geophones but better spatial resolution. The analysis of DAS VSP included Fourier transform, and filtering to remove all up-going energy. Thus, allowing for accurate first arrival picking. We present an example of the Gradual Deformation Method (GDM) using DAS VSP and lithological data to produce a distribution of valid velocity models of BNL. GDM generates continuous perturbations of prior model realizations seeking the best match to the data (i.e. minimize the misfit). Prior model realizations honoring the lithological data were created using sequential Gaussian simulation, a commonly used noniterative geostatistical method. Unlike least-squares-based methods of inversion, GDM readily incorporates a priori information, such as a variogram calculated from well-based lithology information. Additionally, by producing a distribution of models, as opposed to one optimal model, GDM allows for uncertainty quantification. This project aims at assessing the integrated technologies ability to monitor changes in the water table (possibly to one meter resolution) by exploiting the dependence of seismic wave velocities on water saturation of the subsurface. This project, which was funded in part by the National Science Foundation, is a part of the PoroTomo project, funded by a grant from the U.S. Department of Energy.

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.

Risk mapping of clonorchiasis in the People's Republic of China: A systematic review and Bayesian geostatistical analysis.

Directory of Open Access Journals (Sweden)

Ying-Si Lai

2017-03-01

Full Text Available Clonorchiasis, one of the most important food-borne trematodiases, affects more than 12 million people in the People's Republic of China (P.R. China. Spatially explicit risk estimates of Clonorchis sinensis infection are needed in order to target control interventions.Georeferenced survey data pertaining to infection prevalence of C. sinensis in P.R. China from 2000 onwards were obtained via a systematic review in PubMed, ISI Web of Science, Chinese National Knowledge Internet, and Wanfang Data from January 1, 2000 until January 10, 2016, with no restriction of language or study design. Additional disease data were provided by the National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention in Shanghai. Environmental and socioeconomic proxies were extracted from remote-sensing and other data sources. Bayesian variable selection was carried out to identify the most important predictors of C. sinensis risk. Geostatistical models were applied to quantify the association between infection risk and the predictors of the disease, and to predict the risk of infection across P.R. China at high spatial resolution (over a grid with grid cell size of 5×5 km.We obtained clonorchiasis survey data at 633 unique locations in P.R. China. We observed that the risk of C. sinensis infection increased over time, particularly from 2005 onwards. We estimate that around 14.8 million (95% Bayesian credible interval 13.8-15.8 million people in P.R. China were infected with C. sinensis in 2010. Highly endemic areas (≥ 20% were concentrated in southern and northeastern parts of the country. The provinces with the highest risk of infection and the largest number of infected people were Guangdong, Guangxi, and Heilongjiang.Our results provide spatially relevant information for guiding clonorchiasis control interventions in P.R. China. The trend toward higher risk of C. sinensis infection in the recent past urges the Chinese government to

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.

IN SITU NON-INVASIVE SOIL CARBON ANALYSIS: SAMPLE SIZE AND GEOSTATISTICAL CONSIDERATIONS.

Energy Technology Data Exchange (ETDEWEB)

WIELOPOLSKI, L.

2005-04-01

I discuss a new approach for quantitative carbon analysis in soil based on INS. Although this INS method is not simple, it offers critical advantages not available with other newly emerging modalities. The key advantages of the INS system include the following: (1) It is a non-destructive method, i.e., no samples of any kind are taken. A neutron generator placed above the ground irradiates the soil, stimulating carbon characteristic gamma-ray emission that is counted by a detection system also placed above the ground. (2) The INS system can undertake multielemental analysis, so expanding its usefulness. (3) It can be used either in static or scanning modes. (4) The volume sampled by the INS method is large with a large footprint; when operating in a scanning mode, the sampled volume is continuous. (5) Except for a moderate initial cost of about $100,000 for the system, no additional expenses are required for its operation over two to three years after which a NG has to be replenished with a new tube at an approximate cost of $10,000, this regardless of the number of sites analyzed. In light of these characteristics, the INS system appears invaluable for monitoring changes in the carbon content in the field. For this purpose no calibration is required; by establishing a carbon index, changes in carbon yield can be followed with time in exactly the same location, thus giving a percent change. On the other hand, with calibration, it can be used to determine the carbon stock in the ground, thus estimating the soil's carbon inventory. However, this requires revising the standard practices for deciding upon the number of sites required to attain a given confidence level, in particular for the purposes of upward scaling. Then, geostatistical considerations should be incorporated in considering properly the averaging effects of the large volumes sampled by the INS system that would require revising standard practices in the field for determining the number of spots to

Stochastic hydrogeology: what professionals really need?

Science.gov (United States)

Renard, Philippe

2007-01-01

Quantitative hydrogeology celebrated its 150th anniversary in 2006. Geostatistics is younger but has had a very large impact in hydrogeology. Today, geostatistics is used routinely to interpolate deterministically most of the parameters that are required to analyze a problem or make a quantitative analysis. In a small number of cases, geostatistics is combined with deterministic approaches to forecast uncertainty. At a more academic level, geostatistics is used extensively to study physical processes in heterogeneous aquifers. Yet, there is an important gap between the academic use and the routine applications of geostatistics. The reasons for this gap are diverse. These include aspects related to the hydrogeology consulting market, technical reasons such as the lack of widely available software, but also a number of misconceptions. A change in this situation requires acting at different levels. First, regulators must be convinced of the benefit of using geostatistics. Second, the economic potential of the approach must be emphasized to customers. Third, the relevance of the theories needs to be increased. Last, but not least, software, data sets, and computing infrastructure such as grid computing need to be widely available.

Regional soil erosion assessment based on a sample survey and geostatistics

Science.gov (United States)

Yin, Shuiqing; Zhu, Zhengyuan; Wang, Li; Liu, Baoyuan; Xie, Yun; Wang, Guannan; Li, Yishan

2018-03-01

Soil erosion is one of the most significant environmental problems in China. From 2010 to 2012, the fourth national census for soil erosion sampled 32 364 PSUs (Primary Sampling Units, small watersheds) with the areas of 0.2-3 km2. Land use and soil erosion controlling factors including rainfall erosivity, soil erodibility, slope length, slope steepness, biological practice, engineering practice, and tillage practice for the PSUs were surveyed, and the soil loss rate for each land use in the PSUs was estimated using an empirical model, the Chinese Soil Loss Equation (CSLE). Though the information collected from the sample units can be aggregated to estimate soil erosion conditions on a large scale; the problem of estimating soil erosion condition on a regional scale has not been addressed well. The aim of this study is to introduce a new model-based regional soil erosion assessment method combining a sample survey and geostatistics. We compared seven spatial interpolation models based on the bivariate penalized spline over triangulation (BPST) method to generate a regional soil erosion assessment from the PSUs. Shaanxi Province (3116 PSUs) in China was selected for the comparison and assessment as it is one of the areas with the most serious erosion problem. Ten-fold cross-validation based on the PSU data showed the model assisted by the land use, rainfall erosivity factor (R), soil erodibility factor (K), slope steepness factor (S), and slope length factor (L) derived from a 1 : 10 000 topography map is the best one, with the model efficiency coefficient (ME) being 0.75 and the MSE being 55.8 % of that for the model assisted by the land use alone. Among four erosion factors as the covariates, the S factor contributed the most information, followed by K and L factors, and R factor made almost no contribution to the spatial estimation of soil loss. The LS factor derived from 30 or 90 m Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data

Application of multiple-point geostatistics to simulate the effect of small scale aquifer heterogeneity on the efficiency of Aquifer Thermal Energy Storage (ATES)

Science.gov (United States)

Possemiers, Mathias; Huysmans, Marijke; Batelaan, Okke

2015-04-01

Adequate aquifer characterization and simulation using heat transport models are indispensible for determining the optimal design for Aquifer Thermal Energy Storage (ATES) systems and wells. Recent model studies indicate that meter scale heterogeneities in the hydraulic conductivity field introduce a considerable uncertainty in the distribution of thermal energy around an ATES system and can lead to a reduction in the thermal recoverability. In this paper, the influence of centimeter scale clay drapes on the efficiency of a doublet ATES system and the distribution of the thermal energy around the ATES wells are quantified. Multiple-point geostatistical simulation of edge properties is used to incorporate the clay drapes in the models. The results show that clay drapes have an influence both on the distribution of thermal energy in the subsurface and on the efficiency of the ATES system. The distribution of the thermal energy is determined by the strike of the clay drapes, with the major axis of anisotropy parallel to the clay drape strike. The clay drapes have a negative impact (3.3 - 3.6%) on the energy output in the models without a hydraulic gradient. In the models with a hydraulic gradient, however, the presence of clay drapes has a positive influence (1.6 - 10.2%) on the energy output of the ATES system. It is concluded that it is important to incorporate small scale heterogeneities in heat transport models to get a better estimate on ATES efficiency and distribution of thermal energy.

Application of multiple-point geostatistics to simulate the effect of small-scale aquifer heterogeneity on the efficiency of aquifer thermal energy storage

Science.gov (United States)

Possemiers, Mathias; Huysmans, Marijke; Batelaan, Okke

2015-08-01

Adequate aquifer characterization and simulation using heat transport models are indispensible for determining the optimal design for aquifer thermal energy storage (ATES) systems and wells. Recent model studies indicate that meter-scale heterogeneities in the hydraulic conductivity field introduce a considerable uncertainty in the distribution of thermal energy around an ATES system and can lead to a reduction in the thermal recoverability. In a study site in Bierbeek, Belgium, the influence of centimeter-scale clay drapes on the efficiency of a doublet ATES system and the distribution of the thermal energy around the ATES wells are quantified. Multiple-point geostatistical simulation of edge properties is used to incorporate the clay drapes in the models. The results show that clay drapes have an influence both on the distribution of thermal energy in the subsurface and on the efficiency of the ATES system. The distribution of the thermal energy is determined by the strike of the clay drapes, with the major axis of anisotropy parallel to the clay drape strike. The clay drapes have a negative impact (3.3-3.6 %) on the energy output in the models without a hydraulic gradient. In the models with a hydraulic gradient, however, the presence of clay drapes has a positive influence (1.6-10.2 %) on the energy output of the ATES system. It is concluded that it is important to incorporate small-scale heterogeneities in heat transport models to get a better estimate on ATES efficiency and distribution of thermal energy.

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%.

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.

A comparison of geostatistically based inverse techniques for use in performance assessment analysis at the Waste Isolation Pilot Plant Site: Results from Test Case No. 1

International Nuclear Information System (INIS)

Zimmerman, D.A.; Gallegos, D.P.

1993-10-01

The groundwater flow pathway in the Culebra Dolomite aquifer at the Waste Isolation Pilot Plant (WIPP) has been identified as a potentially important pathway for radionuclide migration to the accessible environment. Consequently, uncertainties in the models used to describe flow and transport in the Culebra need to be addressed. A ''Geostatistics Test Problem'' is being developed to evaluate a number of inverse techniques that may be used for flow calculations in the WIPP performance assessment (PA). The Test Problem is actually a series of test cases, each being developed as a highly complex synthetic data set; the intent is for the ensemble of these data sets to span the range of possible conceptual models of groundwater flow at the WIPP site. The Test Problem analysis approach is to use a comparison of the probabilistic groundwater travel time (GWTT) estimates produced by each technique as the basis for the evaluation. Participants are given observations of head and transmissivity (possibly including measurement error) or other information such as drawdowns from pumping wells, and are asked to develop stochastic models of groundwater flow for the synthetic system. Cumulative distribution functions (CDFs) of groundwater flow (computed via particle tracking) are constructed using the head and transmissivity data generated through the application of each technique; one semi-analytical method generates the CDFs of groundwater flow directly. This paper describes the results from Test Case No. 1

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

Regional soil erosion assessment based on a sample survey and geostatistics

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S. Yin

2018-03-01

Full Text Available Soil erosion is one of the most significant environmental problems in China. From 2010 to 2012, the fourth national census for soil erosion sampled 32 364 PSUs (Primary Sampling Units, small watersheds with the areas of 0.2–3 km2. Land use and soil erosion controlling factors including rainfall erosivity, soil erodibility, slope length, slope steepness, biological practice, engineering practice, and tillage practice for the PSUs were surveyed, and the soil loss rate for each land use in the PSUs was estimated using an empirical model, the Chinese Soil Loss Equation (CSLE. Though the information collected from the sample units can be aggregated to estimate soil erosion conditions on a large scale; the problem of estimating soil erosion condition on a regional scale has not been addressed well. The aim of this study is to introduce a new model-based regional soil erosion assessment method combining a sample survey and geostatistics. We compared seven spatial interpolation models based on the bivariate penalized spline over triangulation (BPST method to generate a regional soil erosion assessment from the PSUs. Shaanxi Province (3116 PSUs in China was selected for the comparison and assessment as it is one of the areas with the most serious erosion problem. Ten-fold cross-validation based on the PSU data showed the model assisted by the land use, rainfall erosivity factor (R, soil erodibility factor (K, slope steepness factor (S, and slope length factor (L derived from a 1 : 10 000 topography map is the best one, with the model efficiency coefficient (ME being 0.75 and the MSE being 55.8 % of that for the model assisted by the land use alone. Among four erosion factors as the covariates, the S factor contributed the most information, followed by K and L factors, and R factor made almost no contribution to the spatial estimation of soil loss. The LS factor derived from 30 or 90 m Shuttle Radar Topography Mission

Mapping the annual exceedance frequencies of the PM10 air quality standard - Comparing kriging to a generalized linear spatial model

CSIR Research Space (South Africa)

Khuluse, S

2013-11-01

Full Text Available . Monestiez P., Dubroca L., Bonnin E., Durbec J.-P., Guinet C. (2004). Comparison of model based geostatistical methods in ecology: Application to fin whale distribution in northwestern Mediterranean sea. In proceedings of Geostatistics Banff, Leuangthong...

PENDEKATAN GEOSTATISTIKA DALAM PENDUGAAN KELIMPAHAN IKAN DEMERSAL DENGAN METODE SWEPT AREA DI PERAIRAN UTARA JAWA TENGAH

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Moh. Natsir

2016-03-01

This research is intended to apply geostatistical analysis in fish abundance estimation in the north Java waters. Geostatistical is a series of methods to examine one or more spatially distributed variables through structure analysis of the data. Trawl data obtained using the bottom trawl operated by Bawal Putih vessel. Data processing includes the standardization of catch, geographic position transformation to UTM format, variogram model fitting and abundance prediction using the model. Analysis of trawl data structure was done by using geostatistical analysis, estimation results of the experimental semi-variogram were then used to infer the characteristics of demersal fish abundance in the north of central Java waters. Results of structural analysis and models fitting using geostatistical analysis showed that the most suitable model with all data used were spherical model with different parameters from each model. The models are then used to estimate the value of fish abundance on the points that there is no abundance information through kriging interpolation process. Results of cross-validation of the estimated abundance using kriging with actual values shows that R2 values varied for each data set. Geostatistical prediction results showed smaller coefficients of variation compare to arithmetic calculations.

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)

Spatial and temporal distribution of soil-transmitted helminth infection in sub-Saharan Africa: a systematic review and geostatistical meta-analysis.

Science.gov (United States)

Karagiannis-Voules, Dimitrios-Alexios; Biedermann, Patricia; Ekpo, Uwem F; Garba, Amadou; Langer, Erika; Mathieu, Els; Midzi, Nicholas; Mwinzi, Pauline; Polderman, Anton M; Raso, Giovanna; Sacko, Moussa; Talla, Idrissa; Tchuenté, Louis-Albert Tchuem; Touré, Seydou; Winkler, Mirko S; Utzinger, Jürg; Vounatsou, Penelope

2015-01-01

Interest is growing in predictive risk mapping for neglected tropical diseases (NTDs), particularly to scale up preventive chemotherapy, surveillance, and elimination efforts. Soil-transmitted helminths (hookworm, Ascaris lumbricoides, and Trichuris trichiura) are the most widespread NTDs, but broad geographical analyses are scarce. We aimed to predict the spatial and temporal distribution of soil-transmitted helminth infections, including the number of infected people and treatment needs, across sub-Saharan Africa. We systematically searched PubMed, Web of Knowledge, and African Journal Online from inception to Dec 31, 2013, without language restrictions, to identify georeferenced surveys. We extracted data from household surveys on sources of drinking water, sanitation, and women's level of education. Bayesian geostatistical models were used to align the data in space and estimate risk of with hookworm, A lumbricoides, and T trichiura over a grid of roughly 1 million pixels at a spatial resolution of 5 × 5 km. We calculated anthelmintic treatment needs on the basis of WHO guidelines (treatment of all school-aged children once per year where prevalence in this population is 20-50% or twice per year if prevalence is greater than 50%). We identified 459 relevant survey reports that referenced 6040 unique locations. We estimate that the prevalence of hookworm, A lumbricoides, and T trichiura among school-aged children from 2000 onwards was 16·5%, 6·6%, and 4·4%. These estimates are between 52% and 74% lower than those in surveys done before 2000, and have become similar to values for the entire communities. We estimated that 126 million doses of anthelmintic treatments are required per year. Patterns of soil-transmitted helminth infection in sub-Saharan Africa have changed and the prevalence of infection has declined substantially in this millennium, probably due to socioeconomic development and large-scale deworming programmes. The global control strategy

Information for decision making from imperfect national data: tracking major changes in health care use in Kenya using geostatistics

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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

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)

Applications of stochastic models and geostatistical analyses to study sources and spatial patterns of soil heavy metals in a metalliferous industrial district of China.

Science.gov (United States)

Zhong, Buqing; Liang, Tao; 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. Copyright © 2014 Elsevier B.V. All rights reserved.

Geostatistical screening of flood events in the groundwater levels of the diverted inner delta of the Danube River: implications for river bed clogging

Science.gov (United States)

Trásy, Balázs; Garamhegyi, Tamás; Laczkó-Dobos, Péter; Kovács, József; Hatvani, István Gábor

2018-04-01

The efficient operation of shallow groundwater (SGW) monitoring networks is crucial to water supply, in-land water protection, agriculture and nature conservation. In the present study, the spatial representativity of such a monitoring network in an area that has been thoroughly impacted by anthropogenic activity (river diversion/damming) is assessed, namely the Szigetköz adjacent to the River Danube. The main aims were to assess the spatial representativity of the SGW monitoring network in different discharge scenarios, and investigate the directional characteristics of this representativity, i.e. establish whether geostatistical anisotropy is present, and investigate how this changes with flooding. After the subtraction of a spatial trend from the time series of 85 shallow groundwater monitoring wells tracking flood events from 2006, 2009 and 2013, variography was conducted on the residuals, and the degree of anisotropy was assessed to explore the spatial autocorrelation structure of the network. Since the raw data proved to be insufficient, an interpolated grid was derived, and the final results were scaled to be representative of the original raw data. It was found that during floods the main direction of the spatial variance of the shallow groundwater monitoring wells alters, from perpendicular to the river to parallel with it for over a period of about two week. However, witht the passing of the flood, this returns to its original orientation in 2 months. It is likely that this process is related first to the fast removal of clogged riverbed strata by the flood, then to their slower replacement. In addition, the study highlights the importance of assessing the direction of the spatial autocorrelation structure of shallow groundwater monitoring networks, especially if the aim is to derive interpolated maps for the further investigation or modeling of flow.

The geostatistics of the metal concentrations in sediments from the eastern Brazilian continental shelf in areas of gas and oil production

Science.gov (United States)

Aguiar, Jose Edvar; de Lacerda, Luiz Drude; Miguens, Flavio Costa; Marins, Rozane Valente

2014-04-01

Geostatistical techniques were used to evaluate the differences in the geochemistry of metals in the marine sediments along the Eastern Brazilian continental margin along the states of Ceará and Rio Grande do Norte (Northeastern sector) and Espírito Santo (Southeastern sector). The concentrations of Al, Fe, Mn, Ba, Cd, Cu, Cr, Ni, Pb, V, Hg, and Zn were obtained from acid digestion and quantified using flame atomic absorption spectrometry (AAS), inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma atomic emission spectrometry (ICP-AES). The metals showed a similar order of concentration: Al > Fe > Ba > Mn > V > Ni > Pb > Cr > Zn > Cu, in both the Ceará; and Rio Grande do Norte shelf regions but different in the Espírito Santo shelf (Fe > Al > Mn > Ba > Zn > V > Cr > Ni > Pb > Cu. The concentrations of Hg and Cd were below the detection limit in all areas. A multivariate analysis revealed that the metals of siliciclastic origin on the continental shelf of Ceará are carried by Al. In addition, a large portion of metal deposits is connected to the iron and manganese oxides on the continental margin of Rio Grande do Norte. The metals from the continental supply on the coast of Espírito Santo (Cu, Ni, Ba, and Mn) are associated with Al; whereas Cr, Pb, V, and Zn are associated with iron in this southern area. Geochemical evaluations are needed to distinguish the origin and mineralogical differences of marine sediments within the regions. Scanning electron microscopy/energy dispersive spectrometry (SEM/EDS) applied to the sediments from the coast of Ceará showed the morphological diversity of sediment grains: biological fragments, multifaceted particles, aggregates, and crystals occurred in the three regions analyzed. Among these grains, calcite, Mg-calcite, and aragonite were predominant in the northeastern sector, whereas silicates and other minerals were predominant the southeastern sector. Mg, K, Ti, and Zr as well as the

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

Bayesian Geostatistical Design

DEFF Research Database (Denmark)

Diggle, Peter; Lophaven, Søren Nymand

2006-01-01

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...

Apcom 87. Proceedings of the twentieth international symposium on the application of computers and mathematics in the mineral industries. v. 3

International Nuclear Information System (INIS)

Lemmer, I.C.; Schaum, H.; Camisani-Calzolari, F.A.G.M.

1987-01-01

APCOM symposia provide a medium of exchange of technical expertise and experience for practitioners in the general field of applications of computers, operations research, mathematical and geostatistical techniques in the mineral industries. Contributors represent mine and plant personnel, academic and government or semi-government representatives, and the topics covered range from mining and metallurgical techniques and planning to financial analysis, project evaluation, information systems, computer graphics, geostatistics, etc. For the 20th APCOM, the Proceedings have been divided into the three broad categories of mining, metallurgy and geostatistics, and are grouped accordingly into the three published volumes. The theme of this Symposium - 'special emphasis on the practical application of computers to implement the many powerful theoretical techniques in the workplace' - was chosen to underline the practical needs of industry. Contributions from both local and foreing participants demonstrate how established geostatistics and related disciplines have become. Apart from the more classic applications of estimation and simulation, the papers deal with optimisation of drill sites and spacing, identification of geological patterns, determination of potential prospecting targets and the processing of geochemical and geophysical data. New and more unorthodox applications such as the use of expert systems, the enhancement of remotely sensed data and production forecasting are also dealt with. The papers represent the state of the art in the workplace. Reflecting back on the 10th Symposium, it is clear that the field of geostatistics has flourished in the last 15 years and will go from strength to strength in the future

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

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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

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

Comments on ''Use of conditional simulation in nuclear waste site performance assessment'' by Carol Gotway

International Nuclear Information System (INIS)

Downing, D.J.

1993-01-01

This paper discusses Carol Gotway's paper, ''The Use of Conditional Simulation in Nuclear Waste Site Performance Assessment.'' The paper centers on the use of conditional simulation and the use of geostatistical methods to simulate an entire field of values for subsequent use in a complex computer model. The issues of sampling designs for geostatistics, semivariogram estimation and anisotropy, turning bands method for random field generation, and estimation of the comulative distribution function are brought out

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

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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

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

Application of computers and operations research in the mineral industry. Proceedings of the 30th international symposium

Energy Technology Data Exchange (ETDEWEB)

Bandopadhyay, S. (ed.)

2002-07-01

Papers are presented under the following topics: emerging technologies/E-commerce/Internet; geostatistical resource estimation; openpit mine planning and design; information technology implementation; remote sensing/GIS/GPS applications; geostatistical orebody mining; strategies in mine planning and design; simulation and modeling of mining systems; evaluation of mining systems and optimization through artificial intelligence (AI) and neural networks; process control and optimization; mine data visualization and applications; investment planning and risk evaluation; and occupational risk monitoring and assessment.

Integration of vertical and in-seam horizontal well production analyses with stochastic geostatistical algorithms to estimate pre-mining methane drainage efficiency from coal seams: Blue Creek seam, Alabama.

Science.gov (United States)

Karacan, C Özgen

2013-07-30

Coal seam degasification and its efficiency are directly related to the safety of coal mining. Degasification activities in the Black Warrior basin started in the early 1980s by using vertical boreholes. Although the Blue Creek seam, which is part of the Mary Lee coal group, has been the main seam of interest for coal mining, vertical wellbores have also been completed in the Pratt, Mary Lee, and Black Creek coal groups of the Upper Pottsville formation to degasify multiple seams. Currently, the Blue Creek seam is further degasified 2-3 years in advance of mining using in-seam horizontal boreholes to ensure safe mining. The studied location in this work is located between Tuscaloosa and Jefferson counties in Alabama and was degasified using 81 vertical boreholes, some of which are still active. When the current long mine expanded its operation into this area in 2009, horizontal boreholes were also drilled in advance of mining for further degasification of only the Blue Creek seam to ensure a safe and a productive operation. This paper presents an integrated study and a methodology to combine history matching results from vertical boreholes with production modeling of horizontal boreholes using geostatistical simulation to evaluate spatial effectiveness of in-seam boreholes in reducing gas-in-place (GIP). Results in this study showed that in-seam wells' boreholes had an estimated effective drainage area of 2050 acres with cumulative production of 604 MMscf methane during ~2 years of operation. With horizontal borehole production, GIP in the Blue Creek seam decreased from an average of 1.52 MMscf to 1.23 MMscf per acre. It was also shown that effective gas flow capacity, which was independently modeled using vertical borehole data, affected horizontal borehole production. GIP and effective gas flow capacity of coal seam gas were also used to predict remaining gas potential for the Blue Creek seam.

Geostatistical analysis of groundwater chemistry in Japan. Evaluation of the base case groundwater data set

Energy Technology Data Exchange (ETDEWEB)

Salter, P.F.; Apted, M.J. [Monitor Scientific LLC, Denver, CO (United States); Sasamoto, Hiroshi; Yui, Mikazu

1999-05-01

The groundwater chemistry is one of important geological environment for performance assessment of high level radioactive disposal system. This report describes the results of geostatistical analysis of groundwater chemistry in Japan. Over 15,000 separate groundwater analyses have been collected of deep Japanese groundwaters for the purpose of evaluating the range of geochemical conditions for geological radioactive waste repositories in Japan. The significance to issues such as radioelement solubility limits, sorption, corrosion of overpack, behavior of compacted clay buffers, and many other factors involved in safety assessment. It is important therefore, that a small, but representative set of groundwater types be identified so that defensible models and data for generic repository performance assessment can be established. Principal component analysis (PCA) is used to categorize representative deep groundwater types from this extensive data set. PCA is a multi-variate statistical analysis technique, similar to factor analysis or eigenvector analysis, designed to provide the best possible resolution of the variability within multi-variate data sets. PCA allows the graphical inspection of the most important similarities (clustering) and differences among samples, based on simultaneous consideration of all variables in the dataset, in a low dimensionality plot. It also allows the analyst to determine the reasons behind any pattern that is observed. In this study, PCA has been aided by hierarchical cluster analysis (HCA), in which statistical indices of similarity among multiple samples are used to distinguish distinct clusters of samples. HCA allows the natural, a priori, grouping of data into clusters showing similar attributes and is graphically represented in a dendrogram Pirouette is the multivariate statistical software package used to conduct the PCA and HCA for the Japanese groundwater dataset. An audit of the initial 15,000 sample dataset on the basis of

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.

Geostatistics for Large Datasets

KAUST Repository

Sun, Ying; Li, Bo; Genton, Marc G.

2011-01-01

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.

Spatial uncertainty of a geoid undulation model in Guayaquil, Ecuador

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Chicaiza E.G.

2017-06-01

Full Text Available Geostatistics is a discipline that deals with the statistical analysis of regionalized variables. In this case study, geostatistics is used to estimate geoid undulation in the rural area of Guayaquil town in Ecuador. The geostatistical approach was chosen because the estimation error of prediction map is getting. Open source statistical software R and mainly geoR, gstat and RGeostats libraries were used. Exploratory data analysis (EDA, trend and structural analysis were carried out. An automatic model fitting by Iterative Least Squares and other fitting procedures were employed to fit the variogram. Finally, Kriging using gravity anomaly of Bouguer as external drift and Universal Kriging were used to get a detailed map of geoid undulation. The estimation uncertainty was reached in the interval [-0.5; +0.5] m for errors and a maximum estimation standard deviation of 2 mm in relation with the method of interpolation applied. The error distribution of the geoid undulation map obtained in this study provides a better result than Earth gravitational models publicly available for the study area according the comparison with independent validation points. The main goal of this paper is to confirm the feasibility to use geoid undulations from Global Navigation Satellite Systems and leveling field measurements and geostatistical techniques methods in order to use them in high-accuracy engineering projects.

A GIS Tool for evaluating and improving NEXRAD and its application in distributed hydrologic modeling

Science.gov (United States)

Zhang, X.; Srinivasan, R.

2008-12-01

In this study, a user friendly GIS tool was developed for evaluating and improving NEXRAD using raingauge data. This GIS tool can automatically read in raingauge and NEXRAD data, evaluate the accuracy of NEXRAD for each time unit, implement several geostatistical methods to improve the accuracy of NEXRAD through raingauge data, and output spatial precipitation map for distributed hydrologic model. The geostatistical methods incorporated in this tool include Simple Kriging with varying local means, Kriging with External Drift, Regression Kriging, Co-Kriging, and a new geostatistical method that was newly developed by Li et al. (2008). This tool was applied in two test watersheds at hourly and daily temporal scale. The preliminary cross-validation results show that incorporating raingauge data to calibrate NEXRAD can pronouncedly change the spatial pattern of NEXRAD and improve its accuracy. Using different geostatistical methods, the GIS tool was applied to produce long term precipitation input for a distributed hydrologic model - Soil and Water Assessment Tool (SWAT). Animated video was generated to vividly illustrate the effect of using different precipitation input data on distributed hydrologic modeling. Currently, this GIS tool is developed as an extension of SWAT, which is used as water quantity and quality modeling tool by USDA and EPA. The flexible module based design of this tool also makes it easy to be adapted for other hydrologic models for hydrological modeling and water resources management.

Spatial uncertainty of a geoid undulation model in Guayaquil, Ecuador

Science.gov (United States)

Chicaiza, E. G.; Leiva, C. A.; Arranz, J. J.; Buenańo, X. E.

2017-06-01

Geostatistics is a discipline that deals with the statistical analysis of regionalized variables. In this case study, geostatistics is used to estimate geoid undulation in the rural area of Guayaquil town in Ecuador. The geostatistical approach was chosen because the estimation error of prediction map is getting. Open source statistical software R and mainly geoR, gstat and RGeostats libraries were used. Exploratory data analysis (EDA), trend and structural analysis were carried out. An automatic model fitting by Iterative Least Squares and other fitting procedures were employed to fit the variogram. Finally, Kriging using gravity anomaly of Bouguer as external drift and Universal Kriging were used to get a detailed map of geoid undulation. The estimation uncertainty was reached in the interval [-0.5; +0.5] m for errors and a maximum estimation standard deviation of 2 mm in relation with the method of interpolation applied. The error distribution of the geoid undulation map obtained in this study provides a better result than Earth gravitational models publicly available for the study area according the comparison with independent validation points. The main goal of this paper is to confirm the feasibility to use geoid undulations from Global Navigation Satellite Systems and leveling field measurements and geostatistical techniques methods in order to use them in high-accuracy engineering projects.

Theoretical background and user's manual for the computer code on groundwater flow and radionuclide transport calculation in porous rock

International Nuclear Information System (INIS)

Shirakawa, Toshihiko; Hatanaka, Koichiro

2001-11-01

In order to document a basic manual about input data, output data, execution of computer code on groundwater flow and radionuclide transport calculation in heterogeneous porous rock, we investigated the theoretical background about geostatistical computer codes and the user's manual for the computer code on groundwater flow and radionuclide transport which calculates water flow in three dimension, the path of moving radionuclide, and one dimensional radionuclide migration. In this report, based on above investigation we describe the geostatistical background about simulating heterogeneous permeability field. And we describe construction of files, input and output data, a example of calculating of the programs which simulates heterogeneous permeability field, and calculates groundwater flow and radionuclide transport. Therefore, we can document a manual by investigating the theoretical background about geostatistical computer codes and the user's manual for the computer code on groundwater flow and radionuclide transport calculation. And we can model heterogeneous porous rock and analyze groundwater flow and radionuclide transport by utilizing the information from this report. (author)

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.

Application of covariance clouds for estimating the anisotropy ellipsoid eigenvectors, with case study in uranium deposit

International Nuclear Information System (INIS)

Jamali Esfahlan, D.; Madani, H.; Tahmaseb Nazemi, M. T.; Mahdavi, F.; Ghaderi, M. R.; Najafi, M.

2010-01-01

Various methods of Kriging and nonlinear geostatistical methods considered as acceptable methods for resource and reserve estimations have characters such as the least estimation variance in their nature, and accurate results in the acceptable confidence levels range could be achieved if the required parameters for the estimation are determined accurately. If the determined parameters don't have the sufficient accuracy, 3-D geostatistical estimations will not be reliable any more, and by this, all the quantitative parameters of the mineral deposit (e.g. grade-tonnage variations) will be misinterpreted. One of the most significant parameters for 3-D geostatistical estimation is the anisotropy ellipsoid. The anisotropy ellipsoid is important for geostatistical estimations because it determines the samples in different directions required for accomplishing the estimation. The aim of this paper is to illustrate a more simple and time preserving analytical method that can apply geophysical or geochemical analysis data from the core-length of boreholes for modeling the anisotropy ellipsoid. By this method which is based on the distribution of covariance clouds in a 3-D sampling space of a deposit, quantities, ratios, azimuth and plunge of the major-axis, semi-major axis and the minor-axis determine the ore-grade continuity within the deposit and finally the anisotropy ellipsoid of the deposit will be constructed. A case study of an uranium deposit is also analytically discussed for illustrating the application of this method.

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

International Nuclear Information System (INIS)

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

2007-01-01

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 flow

Time-Lapse Analysis of Methane Quantity in the Mary Lee Group of Coal Seams Using Filter-Based Multiple-Point Geostatistical Simulation.

Science.gov (United States)

Karacan, C Özgen; Olea, Ricardo A

2013-08-01

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. The systematic approach presented in this paper is the first time in literature that history matching, TIs of GIPs and filter simulations are used for degasification performance evaluation and for assessing GIP for mining safety. Results from this study showed that using production history matching of coalbed methane wells to determine time-lapsed reservoir data could be used to compute spatial GIP and representative GIP TIs generated through Voronoi decomposition

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

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

Science.gov (United States)

Goovaerts, Pierre

2006-01-01

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 maps is the common biased

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.

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

Time-REferenced data Kriging (TREK): mapping hydrological statistics given their time of reference

Science.gov (United States)

Porcheron, Delphine; Leblois, Etienne; Sauquet, Eric

2016-04-01

A major issue in water sciences is to predict runoff parameters at ungauged sites. Estimates can be obtained by various methods. Among them, geostatistical approaches provide interpolation methods that consequently use explicit assumptions on the variable of interest. Geostatistical techniques have been applied to precipitation and temperature fields and later extended to estimate runoff features considered as basin-support variates along the river network (e.g. Gottschalk, 1993; Sauquet et al., 2000; Skoien et al., 2006; Gottschalk et al., 2011). To obtain robust estimations, the first step is to collect a relevant dataset. Sauquet et al. (2000) and Sauquet (2006) suggest including a large number of catchments with long and common observation periods to ensure both reliability and temporal consistency in runoff estimates. However most observation networks evolve with time. Several choices are thus possible to define an optimal reference period maximizing either spatial or temporal overlap. However, the constraints usually lead to discard a significant number of stations. Time-REferenced data Kriging method (TREK) has been developed to overcome this issue. Here is proposed a method of geostatistical estimation considering the temporal support over which a hydrological statistic has been estimated. This allows attenuating the loss of data previously caused by the application of a strict reference period. The time reference remains for the targeted map itself. The weights depend on the observation period of the data included in the dataset and how near this is to the target period. In this presentation, the concepts of TREK will be introduced and thereafter illustrated to map mean annual runoff in France. References Gottschalk, L., 1993, Correlation and covariance of runoff. Stochastic Hydrology and Hydraulics 7(2), 85-101. Sauquet, E., Gottschalk, L. and Leblois, E., 2000, Mapping average annual runoff: a hierarchical approach applying a stochastic interpolation

Three-dimensional imaging of aquifer and aquitard heterogeneity via transient hydraulic tomography at a highly heterogeneous field site

Science.gov (United States)

Zhao, Zhanfeng; Illman, Walter A.

2018-04-01

Previous studies have shown that geostatistics-based transient hydraulic tomography (THT) is robust for subsurface heterogeneity characterization through the joint inverse modeling of multiple pumping tests. However, the hydraulic conductivity (K) and specific storage (Ss) estimates can be smooth or even erroneous for areas where pumping/observation densities are low. This renders the imaging of interlayer and intralayer heterogeneity of highly contrasting materials including their unit boundaries difficult. In this study, we further test the performance of THT by utilizing existing and newly collected pumping test data of longer durations that showed drawdown responses in both aquifer and aquitard units at a field site underlain by a highly heterogeneous glaciofluvial deposit. The robust performance of the THT is highlighted through the comparison of different degrees of model parameterization including: (1) the effective parameter approach; (2) the geological zonation approach relying on borehole logs; and (3) the geostatistical inversion approach considering different prior information (with/without geological data). Results reveal that the simultaneous analysis of eight pumping tests with the geostatistical inverse model yields the best results in terms of model calibration and validation. We also find that the joint interpretation of long-term drawdown data from aquifer and aquitard units is necessary in mapping their full heterogeneous patterns including intralayer variabilities. Moreover, as geological data are included as prior information in the geostatistics-based THT analysis, the estimated K values increasingly reflect the vertical distribution patterns of permeameter-estimated K in both aquifer and aquitard units. Finally, the comparison of various THT approaches reveals that differences in the estimated K and Ss tomograms result in significantly different transient drawdown predictions at observation ports.

Environmental Modeling Center

Data.gov (United States)

Federal Laboratory Consortium — The Environmental Modeling Center provides the computational tools to perform geostatistical analysis, to model ground water and atmospheric releases for comparison...

Ore reserve estimation: a summary of principles and methods

International Nuclear Information System (INIS)

Marques, J.P.M.

1985-01-01

The mining industry has experienced substantial improvements with the increasing utilization of computerized and electronic devices throughout the last few years. In the ore reserve estimation field the main methods have undergone recent advances in order to improve their overall efficiency. This paper presents the three main groups of ore reserve estimation methods presently used worldwide: Conventional, Statistical and Geostatistical, and elaborates a detaited description and comparative analysis of each. The Conventional Methods are the oldest, less complex and most employed ones. The Geostatistical Methods are the most recent precise and more complex ones. The Statistical Methods are intermediate to the others in complexity, diffusion and chronological order. (D.J.M.) [pt

Surge of Bering Glacier and Bagley Ice Field: Parameterization of surge characteristics based on automated analysis of crevasse image data and laser altimeter data

Science.gov (United States)

Stachura, M.; Herzfeld, U. C.; McDonald, B.; Weltman, A.; Hale, G.; Trantow, T.

2012-12-01

The dynamical processes that occur during the surge of a large, complex glacier system are far from being understood. The aim of this paper is to derive a parameterization of surge characteristics that captures the principle processes and can serve as the basis for a dynamic surge model. Innovative mathematical methods are introduced that facilitate derivation of such a parameterization from remote-sensing observations. Methods include automated geostatistical characterization and connectionist-geostatistical classification of dynamic provinces and deformation states, using the vehicle of crevasse patterns. These methods are applied to analyze satellite and airborne image and laser altimeter data collected during the current surge of Bering Glacier and Bagley Ice Field, Alaska.

Estimation of Leakage Potential of Selected Sites in Interstate and Tri-State Canals Using Geostatistical Analysis of Selected Capacitively Coupled Resistivity Profiles, Western Nebraska, 2004

Science.gov (United States)

Vrabel, Joseph; Teeple, Andrew; Kress, Wade H.

2009-01-01

With increasing demands for reliable water supplies and availability estimates, groundwater flow models often are developed to enhance understanding of surface-water and groundwater systems. Specific hydraulic variables must be known or calibrated for the groundwater-flow model to accurately simulate current or future conditions. Surface geophysical surveys, along with selected test-hole information, can provide an integrated framework for quantifying hydrogeologic conditions within a defined area. In 2004, the U.S. Geological Survey, in cooperation with the North Platte Natural Resources District, performed a surface geophysical survey using a capacitively coupled resistivity technique to map the lithology within the top 8 meters of the near-surface for 110 kilometers of the Interstate and Tri-State Canals in western Nebraska and eastern Wyoming. Assuming that leakage between the surface-water and groundwater systems is affected primarily by the sediment directly underlying the canal bed, leakage potential was estimated from the simple vertical mean of inverse-model resistivity values for depth levels with geometrically increasing layer thickness with depth which resulted in mean-resistivity values biased towards the surface. This method generally produced reliable results, but an improved analysis method was needed to account for situations where confining units, composed of less permeable material, underlie units with greater permeability. In this report, prepared by the U.S. Geological Survey in cooperation with the North Platte Natural Resources District, the authors use geostatistical analysis to develop the minimum-unadjusted method to compute a relative leakage potential based on the minimum resistivity value in a vertical column of the resistivity model. The minimum-unadjusted method considers the effects of homogeneous confining units. The minimum-adjusted method also is developed to incorporate the effect of local lithologic heterogeneity on water

Nonpoint source solute transport normal to aquifer bedding in heterogeneous, Markov chain random fields

Science.gov (United States)

Zhang, Hua; Harter, Thomas; Sivakumar, Bellie

2006-06-01

Facies-based geostatistical models have become important tools for analyzing flow and mass transport processes in heterogeneous aquifers. Yet little is known about the relationship between these latter processes and the parameters of facies-based geostatistical models. In this study, we examine the transport of a nonpoint source solute normal (perpendicular) to the major bedding plane of an alluvial aquifer medium that contains multiple geologic facies, including interconnected, high-conductivity (coarse textured) facies. We also evaluate the dependence of the transport behavior on the parameters of the constitutive facies model. A facies-based Markov chain geostatistical model is used to quantify the spatial variability of the aquifer system's hydrostratigraphy. It is integrated with a groundwater flow model and a random walk particle transport model to estimate the solute traveltime probability density function (pdf) for solute flux from the water table to the bottom boundary (the production horizon) of the aquifer. The cases examined include two-, three-, and four-facies models, with mean length anisotropy ratios for horizontal to vertical facies, ek, from 25:1 to 300:1 and with a wide range of facies volume proportions (e.g., from 5 to 95% coarse-textured facies). Predictions of traveltime pdfs are found to be significantly affected by the number of hydrostratigraphic facies identified in the aquifer. Those predictions of traveltime pdfs also are affected by the proportions of coarse-textured sediments, the mean length of the facies (particularly the ratio of length to thickness of coarse materials), and, to a lesser degree, the juxtapositional preference among the hydrostratigraphic facies. In transport normal to the sedimentary bedding plane, traveltime is not lognormally distributed as is often assumed. Also, macrodispersive behavior (variance of the traveltime) is found not to be a unique function of the conductivity variance. For the parameter range

Uso da geoestatística para avaliar a captação automática dos níveis de pressão sonora em instalações de creche para suínos Geostatistics to evaluate the automatic acquisiton of sound pressure levels in pig nursery facilities

Directory of Open Access Journals (Sweden)

Giselle Borges

2010-06-01

Full Text Available Este trabalho teve o objetivo de estudar a influência da distribuição de decibelímetros na captação automática dos níveis de pressão sonora, em ambiente de produção intensiva de suínos. O experimento foi conduzido em sala do setor de creche de uma granja comercial de suínos situada no município de Monte Mor, Estado de São Paulo. A sala foi dividida em dez quadrantes idênticos, e os decibelímetros foram instalados no centro geométrico de cada quadrante. Utilizou-se a geoestatística para avaliar a dependência espacial entre os decibelímetros e para predizer os níveis de pressão sonora em locais onde estes não foram instalados. Os dados foram analisados pela correlação entre os decibelímetros e por intermédio da geoestatística, que possibilitou afirmar que não houve dependência espacial entre os pontos de registro dos níveis de pressão sonora. Por intermédio da interpolação dos pontos de captura utilizando o processo de krigagem, foi possível predizer os níveis de pressão sonora nos locais onde não havia decibelímetros no interior da instalação. Verificou-se homogeneidade de propagação dos níveis de pressão sonora no interior da instalação, concluindo que, para o ambiente avaliado, o uso de somente um equipamento para o registro automático dos níveis de pressão sonora é suficiente.The objective of this work was to study the influence of decibelimeters distribution in the automatic acquisition of sound pressure levels in pig nursery facilities. The experiment was conducted in a nursering room of a commercial swine's facility situated in the city of Monte Mor, State of São Paulo, Brazil. The room was divided in ten identical quadrants and the geometrical center of each quadrant was installed the decibelimeters. Geostatistics was used to evaluate the spatial dependence among the decibelimeters and to predict sound pressure levels in the places that they were not installed. Data were analyzed by the

Application of integrated reservoir management and reservoir characterization to optimize infill drilling. Quarterly progress report, June 13, 1995--September 12, 1995

Energy Technology Data Exchange (ETDEWEB)

Pande, P.K.

1995-09-12

At this stage of the reservoir characterization research, the main emphasis is on the geostatistics and reservoir simulation. Progress is reported on geological analysis, reservoir simulation, and reservoir management.

A Comparative Analysis of Extracted Heights from Topographic ...

African Journals Online (AJOL)

Abebrese

1 Department of Geomatic Engineering, KNUST, Ghana. 2 Building ... investigate numerical techniques that could improve the solution to the Thompson's polynomial. ..... Geo-EAS (Geostatistical Environment Assessment Software) Las Vegas.

A Computer Program for Practical Semivariogram Modeling and Ordinary Kriging: A Case Study of Porosity Distribution in an Oil Field

Directory of Open Access Journals (Sweden)

Mert Bayram Ali

2017-12-01

Full Text Available In this study, firstly, a practical and educational geostatistical program (JeoStat was developed, and then example analysis of porosity parameter distribution, using oilfield data, was presented.

Prediction of spatial distribution for some land use allometric ...

African Journals Online (AJOL)

Prediction of spatial distribution for some land use allometric characteristics in land use planning models with geostatistic and Geographical Information System (GIS) (Case study: Boein and Miandasht, Isfahan Province, Iran)

Ultrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data: A Digital Soil Mapping Case Study

Science.gov (United States)

Lamb, David W.; Mengersen, Kerrie

2016-01-01

Modern soil mapping is characterised by the need to interpolate point referenced (geostatistical) observations and the availability of large numbers of environmental characteristics for consideration as covariates to aid this interpolation. Modelling tasks of this nature also occur in other fields such as biogeography and environmental science. This analysis employs the Least Angle Regression (LAR) algorithm for fitting Least Absolute Shrinkage and Selection Operator (LASSO) penalized Multiple Linear Regressions models. This analysis demonstrates the efficiency of the LAR algorithm at selecting covariates to aid the interpolation of geostatistical soil carbon observations. Where an exhaustive search of the models that could be constructed from 800 potential covariate terms and 60 observations would be prohibitively demanding, LASSO variable selection is accomplished with trivial computational investment. PMID:27603135

Interest rates mapping

Science.gov (United States)

Kanevski, M.; Maignan, M.; Pozdnoukhov, A.; Timonin, V.

2008-06-01

The present study deals with the analysis and mapping of Swiss franc interest rates. Interest rates depend on time and maturity, defining term structure of the interest rate curves (IRC). In the present study IRC are considered in a two-dimensional feature space-time and maturity. Exploratory data analysis includes a variety of tools widely used in econophysics and geostatistics. Geostatistical models and machine learning algorithms (multilayer perceptron and Support Vector Machines) were applied to produce interest rate maps. IR maps can be used for the visualisation and pattern perception purposes, to develop and to explore economical hypotheses, to produce dynamic asset-liability simulations and for financial risk assessments. The feasibility of an application of interest rates mapping approach for the IRC forecasting is considered as well.

Geostatistics and Analysis of Spatial Data

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg

2007-01-01

, 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...

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.

Geostatistics and cost-effective environmental remediation

International Nuclear Information System (INIS)

Rautman, C.A.

1996-01-01

Numerous sites within the U.S. Department of Energy (DOE) complex have been contaminated with various radioactive and hazardous materials by defense-related activities during the post-World War II era. The perception is that characterization and remediation of these contaminated sites will be too costly using currently available technology. Consequently, the DOE Office of Technology Development has funded development of a number of alternative processes for characterizing and remediating these sites. The former Feed-Materials Processing Center near Fernald, Ohio (USA), was selected for demonstrating several innovative technologies. Contamination at the Fernald site consists principally of particulate uranium and derivative compounds in surficial soil. A field-characterization demonstration program was conducted during the summer of 1994 specifically to demonstrate the relative economic performance of seven proposed advanced-characterization tools for measuring uranium activity of in-situ soils. These innovative measurement technologies are principally radiation detectors of varied designs. Four industry-standard measurement technologies, including conventional, regulatory-agency-accepted soil sampling followed by laboratory geochemical analysis, were also demonstrated during the program for comparative purposes. A risk-based economic-decision model has been used to evaluate the performance of these alternative characterization tools. The decision model computes the dollar value of an objective function for each of the different characterization approaches. The methodology not only can assist site operators to choose among engineering alternatives for site characterization and/or remediation, but also can provide an objective and quantitative basis for decisions with respect to the completeness of site characterization

Cross-covariance functions for multivariate geostatistics

KAUST Repository

Genton, Marc G.; Kleiber, William

2015-01-01

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.

Geostatistics applied in mine production planning

Energy Technology Data Exchange (ETDEWEB)

Zerdin, F.; Markic, S.; Subelj, A. [NTF, Ljubljana (Slovenia)

1998-07-01

Long-term plan for lignite mine Velenje has been made by using a technical 'parametrisation' method where the basis for periodic, middle-term and short-term plans as well as for scheduling has already been fixed. Plans are presented by grade (heating value in GJ), quantity of ROM coal in tons and quantity of excavated material in cubic metre for each sequence of the seam or whole seam as well as for all working seams in the mines as a whole which correspond to a defined time period. Grade planning aims at achieving uniform product quality to meet contractual obligations. 17 refs., 10 figs.

Journal of Earth System Science | Indian Academy of Sciences

Indian Academy of Sciences (India)

Natural radioactivity; seasonal climate; soil; geostatistics ... national authorities because of the harmful effects of radiation exposure on human health. ... study showed a prominent control of radio-emission measurements by seasonal changes.

5. Surveys for Schistosomiasis and Soil Transmitted Helminths in ...

African Journals Online (AJOL)

Esem

Occurrence of dual infection with S. ..... considered and properly integrated when planning and implementing further ... geostatistical model based risk estimates of schistosomiasis. ... Shiff, C.J. Diagnosis of Schistosoma mansoni without the ...

Spatial distribution of Aedes aegypti (Diptera: Culicidae in the rural area of two municipalities of Cundinamarca, Colombia

Directory of Open Access Journals (Sweden)

Laura Cabezas

2017-03-01

Conclusion: This study shows the importance of geostatistics for the surveillance of vector-borne diseases and the analysis of time and space dynamics of vector insects and of diseases transmitted by them.

Soil penetration resistance analysis by multivariate and geostatistical methods Análisis de la resistencia a la penetración del suelo mediante métodos geoestadísticos y multivariados

Directory of Open Access Journals (Sweden)

Cecilia Medina

2012-02-01

Full Text Available The penetration resistance (PR is a soil attribute that allows identifies areas with restrictions due to compaction, which results in mechanical impedance for root growth and reduced crop yield. The aim of this study was to characterize the PR of an agricultural soil by geostatistical and multivariate analysis. Sampling was done randomly in 90 points up to 0.60 m depth. It was determined spatial distribution models of PR, and defined areas with mechanical impedance for roots growth. The PR showed a random distribution to 0.55 and 0.60 m depth. PR in other depths analyzed showed spatial dependence, with adjustments to exponential and spherical models. The cluster analysis that considered sampling points allowed establishing areas with compaction problem identified in the maps by kriging interpolation. The analysis with main components identified three soil layers, where the middle layer showed the highest values of PR.La resistencia a la penetración (RP es un atributo del suelo que permite identificar zonas con restricciones debido a la compactación, que se traduce en impedancia mecánica para el desarrollo de las raíces y en una menor productividad de los cultivos. El objetivo del presente trabajo fue caracterizar la RP de un suelo agrícola, mediante análisis geoestadístico y multivariado. El muestreo se realizó de manera aleatoria en 90 puntos, hasta una profundidad de 0,60 m. Se determinaron los modelos de distribución espacial de la RP y se delimitaron áreas con problemas de impedancia mecánica de las raíces. La RP presentó distribución aleatoria a 0,55 y 0,60 m de profundidad. La RP en las otras profundidades analizadas mostraron dependencia espacial, con ajustes a modelos exponenciales y esféricos. El análisis jerárquico que consideró puntos de muestreo, permitió establecer zonas con problemas de compactación, identificadas en los mapas obtenidos mediante interpolación por kriging. El análisis de componentes principales

GLOBAL JOURNAL OF AGRICULTURAL SCIENCES ISSN 1596-2903

African Journals Online (AJOL)

Ada Global

This paper applies statistical and geostatistical procedures to analyse the spatial distribution of several soil properties and .... curve as the functional diagram of the semi-variance. ( ) ... indicating that the dispersion of data points in each data.

Infill sampling criteria to locate extremes

CSIR Research Space (South Africa)

Watson, AG

1995-07-01

Full Text Available Three problem-dependent meanings for engineering ''extremes'' are motivated, established, and translated into formal geostatistical (model-based) criteria for designing infill sample networks. (I) Locate an area within the domain of interest where a...

Spatiotemporal Dynamics of Whitefly Bemisia tabaci (Hemiptera: Aleyrodidae) in Commercial Watermelon Crops.

Science.gov (United States)

Lima, Carlos H O; Sarmento, Renato A; Galdino, Tarcísio V S; Pereira, Poliana S; Silva, Joedna; Souza, Danival J; Dos Santos, Gil R; Costa, Thiago L; Picanço, Marcelo C

2018-04-16

Spatiotemporal dynamics studies of crop pests enable the determination of the colonization pattern and dispersion of these insects in the landscape. Geostatistics is an efficient tool for these studies: to determine the spatial distribution pattern of the pest in the crops and to make maps that represent this situation. Analysis of these maps across the development of plants can be used as a tool in precision agriculture programs. Watermelon, Citrullus lanatus (Thunb.) Matsum. and Nakai (Cucurbitales: Cucurbitaceae), is the second most consumed fruit in the world, and the whitefly Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is one of the most important pests of this crop. Thus, the objective of this work was to determine the spatiotemporal distribution of B. tabaci in commercial watermelon crops using geostatistics. For 2 yr, we monitored adult whitefly densities in eight watermelon crops in a tropical climate region. The location of the samples and other crops in the landscape was georeferenced. Experimental data were submitted to geostatistical analysis. The colonization of B. tabaci had two patterns. In the first, the colonization started at the outermost parts of the crop. In the second, the insects occupied the whole area of the crop since the beginning of cultivation. The maximum distance between sites of watermelon crops in which spatial dependence of B. tabaci densities was observed was 19.69 m. The adult B. tabaci densities in the eight watermelon fields were positively correlated with rainfall and relative humidity, whereas wind speed negatively affected whiteflies population.

Statistical Estimation of Heterogeneities: A New Frontier in Well Testing

Science.gov (United States)

Neuman, S. P.; Guadagnini, A.; Illman, W. A.; Riva, M.; Vesselinov, V. V.

2001-12-01

Well-testing methods have traditionally relied on analytical solutions of groundwater flow equations in relatively simple domains, consisting of one or at most a few units having uniform hydraulic properties. Recently, attention has been shifting toward methods and solutions that would allow one to characterize subsurface heterogeneities in greater detail. On one hand, geostatistical inverse methods are being used to assess the spatial variability of parameters, such as permeability and porosity, on the basis of multiple cross-hole pressure interference tests. On the other hand, analytical solutions are being developed to describe the mean and variance (first and second statistical moments) of flow to a well in a randomly heterogeneous medium. Geostatistical inverse interpretation of cross-hole tests yields a smoothed but detailed "tomographic" image of how parameters actually vary in three-dimensional space, together with corresponding measures of estimation uncertainty. Moment solutions may soon allow one to interpret well tests in terms of statistical parameters such as the mean and variance of log permeability, its spatial autocorrelation and statistical anisotropy. The idea of geostatistical cross-hole tomography is illustrated through pneumatic injection tests conducted in unsaturated fractured tuff at the Apache Leap Research Site near Superior, Arizona. The idea of using moment equations to interpret well-tests statistically is illustrated through a recently developed three-dimensional solution for steady state flow to a well in a bounded, randomly heterogeneous, statistically anisotropic aquifer.

Bayesian Spatial Modelling with R-INLA

Directory of Open Access Journals (Sweden)

Finn Lindgren

2015-02-01

Full Text Available The principles behind the interface to continuous domain spatial models in the R- INLA software package for R are described. The integrated nested Laplace approximation (INLA approach proposed by Rue, Martino, and Chopin (2009 is a computationally effective alternative to MCMC for Bayesian inference. INLA is designed for latent Gaussian models, a very wide and flexible class of models ranging from (generalized linear mixed to spatial and spatio-temporal models. Combined with the stochastic partial differential equation approach (SPDE, Lindgren, Rue, and Lindstrm 2011, one can accommodate all kinds of geographically referenced data, including areal and geostatistical ones, as well as spatial point process data. The implementation interface covers stationary spatial mod- els, non-stationary spatial models, and also spatio-temporal models, and is applicable in epidemiology, ecology, environmental risk assessment, as well as general geostatistics.

Improving Chemical EOR Simulations and Reducing the Subsurface Uncertainty Using Downscaling Conditioned to Tracer Data

KAUST Repository

Torrealba, Victor A.; Hoteit, Hussein; Chawathe, Adwait

2017-01-01

and thermodynamic phase split, the impact of grid downscaling on CEOR simulations is not well understood. In this work, we introduce a geostatistical downscaling method conditioned to tracer data to refine a coarse history-matched WF model. This downscaling process

kriging method of study of the groundwater quality used for irrigation

African Journals Online (AJOL)

Boufekane A, Saighi O

2016-05-01

May 1, 2016 ... its risks by using the geostatistical approach. 2. .... one of the most important tools for quantifying spatial correlation between data points. ... In this study, two types of models (gaussian and spherical) were used to determine the ...

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

COMPARISON OF SPATIAL INTERPOLATION METHODS FOR WHEAT WATER REQUIREMENT AND ITS TEMPORAL DISTRIBUTION IN HAMEDAN PROVINCE (IRAN

Directory of Open Access Journals (Sweden)

M. H. Nazarifar

2014-01-01

Full Text Available Water is the main constraint for production of agricultural crops. The temporal and spatial variations in water requirement for agriculture products are limiting factors in the study of optimum use of water resources in regional planning and management. However, due to unfavorable distribution and density of meteorological stations, it is not possible to monitor the regional variations precisely. Therefore, there is a need to estimate the evapotranspiration of crops at places where meteorological data are not available and then extend the findings from points of measurements to regional scale. Geostatistical methods are among those methods that can be used for estimation of evapotranspiration at regional scale. The present study attempts to investigate different geostatistical methods for temporal and spatial estimation of water requirements for wheat crop in different periods. The study employs the data provided by 16 synoptic and climatology meteorological stations in Hamadan province in Iran. Evapotranspiration for each month and for the growth period were determined using Penman-Mantis and Torrent-White methods for different water periods based on Standardized Precipitation Index (SPI. Among the available geostatistical methods, three methods: Kriging Method, Cokriging Method, and inverse weighted distance were selected, and analyzed, using GS+ software. Analysis and selection of the suitable geostatistical method were performed based on two measures, namely Mean Absolute Error (MAE and Mean Bias Error (MBE. The findings suggest that, in general, during the drought period, Kriging method is the proper one for estimating water requirements for the six months: January, February, April, May, August, and December. However, weighted moving average is a better estimation method for the months March, June, September, and October. In addition, Kriging is the best method for July. In normal conditions, Kriging is suitable for April, August, December

Scalable Learning for Geostatistics and Speaker Recognition

Science.gov (United States)

2011-01-01

Device Architecture (CUDA)[63], a parallel programming model that leverages the parallel compute engine in NVIDIA GPUs to solve general purpose...validation. 3.1 Geospatial data reconstruction Sensors deployed on satellites are often used to collect enviromental data where a direct measurement is...same decision as training a model on B and testing on A, which is desirable in many recognition engines . We shall address this in the next chapter. The

Prediction of spatial soil property information from ancillary sensor data using ordinary linear regression: Model derivations, residual assumptions and model validation tests

Science.gov (United States)

Geospatial measurements of ancillary sensor data, such as bulk soil electrical conductivity or remotely sensed imagery data, are commonly used to characterize spatial variation in soil or crop properties. Geostatistical techniques like kriging with external drift or regression kriging are often use...

Acoustic Determination of Near-Surface Soil Properties

Science.gov (United States)

2008-12-01

requiring geostatistical analysis, while nearby others are spatially independent. In studies involving many different soil properties and chemistry ...Am 116(6), p. 3354-3369. Kravchenko, N., C.W. Boast, D.G. Bullock, 1991. Fractal analysis of soil spatial variability. Agronomy Journal 91

Large-scale inverse model analyses employing fast randomized data reduction

Science.gov (United States)

Lin, Youzuo; Le, Ellen B.; O'Malley, Daniel; Vesselinov, Velimir V.; Bui-Thanh, Tan

2017-08-01

When the number of observations is large, it is computationally challenging to apply classical inverse modeling techniques. We have developed a new computationally efficient technique for solving inverse problems with a large number of observations (e.g., on the order of 107 or greater). Our method, which we call the randomized geostatistical approach (RGA), is built upon the principal component geostatistical approach (PCGA). We employ a data reduction technique combined with the PCGA to improve the computational efficiency and reduce the memory usage. Specifically, we employ a randomized numerical linear algebra technique based on a so-called "sketching" matrix to effectively reduce the dimension of the observations without losing the information content needed for the inverse analysis. In this way, the computational and memory costs for RGA scale with the information content rather than the size of the calibration data. Our algorithm is coded in Julia and implemented in the MADS open-source high-performance computational framework (http://mads.lanl.gov). We apply our new inverse modeling method to invert for a synthetic transmissivity field. Compared to a standard geostatistical approach (GA), our method is more efficient when the number of observations is large. Most importantly, our method is capable of solving larger inverse problems than the standard GA and PCGA approaches. Therefore, our new model inversion method is a powerful tool for solving large-scale inverse problems. The method can be applied in any field and is not limited to hydrogeological applications such as the characterization of aquifer heterogeneity.

The Application of Artificial Neural Networks to Ore Reserve Estimation at Choghart Iron Ore Deposit

Directory of Open Access Journals (Sweden)

Seyyed Ali Nezamolhosseini

2017-01-01

Full Text Available Geo-statistical methods for reserve estimation are difficult to use when stationary conditions are not satisfied. Artificial Neural Networks (ANNs provide an alternative to geo-statistical techniques while considerably reducing the processing time required for development and application. In this paper the ANNs was applied to the Choghart iron ore deposit in Yazd province of Iran. Initially, an optimum Multi Layer Perceptron (MLP was constructed to estimate the Fe grade within orebody using the whole ore data of the deposit. Sensitivity analysis was applied for a number of hidden layers and neurons, different types of activation functions and learning rules. Optimal architectures for iron grade estimation were 3-20-10-1. In order to improve the network performance, the deposit was divided into four homogenous zones. Subsequently, all sensitivity analyses were carried out on each zone.  Finally, a different optimum network was trained and Fe was estimated separately for each zone. Comparison of correlation coefficient (R and least mean squared error (MSE showed that the ANNs performed on four homogenous zones were far better than the nets applied to the overall ore body. Therefore, these optimized neural networks were used to estimate the distribution of iron grades and the iron resource in Choghart deposit. As a result of applying ANNs, the tonnage of ore for Choghart deposit is approximately estimated at 135.8 million tones with average grade of Fe at 56.14 percent. Results of reserve estimation using ANNs showed a good agreement with the geo-statistical methods applied to this ore body in another work.

Sequential Gaussian co-simulation of rate decline parameters of longwall gob gas ventholes

Science.gov (United States)

Karacan, C. Özgen; Olea, Ricardo A.

2013-01-01

Gob gas ventholes (GGVs) are used to control methane inflows into a longwall mining operation by capturing the gas within the overlying fractured strata before it enters the work environment. Using geostatistical co-simulation techniques, this paper maps the parameters of their rate decline behaviors across the study area, a longwall mine in the Northern Appalachian basin. Geostatistical gas-in-place (GIP) simulations were performed, using data from 64 exploration boreholes, and GIP data were mapped within the fractured zone of the study area. In addition, methane flowrates monitored from 10 GGVs were analyzed using decline curve analyses (DCA) techniques to determine parameters of decline rates. Surface elevation showed the most influence on methane production from GGVs and thus was used to investigate its relation with DCA parameters using correlation techniques on normal-scored data. Geostatistical analysis was pursued using sequential Gaussian co-simulation with surface elevation as the secondary variable and with DCA parameters as the primary variables. The primary DCA variables were effective percentage decline rate, rate at production start, rate at the beginning of forecast period, and production end duration. Co-simulation results were presented to visualize decline parameters at an area-wide scale. Wells located at lower elevations, i.e., at the bottom of valleys, tend to perform better in terms of their rate declines compared to those at higher elevations. These results were used to calculate drainage radii of GGVs using GIP realizations. The calculated drainage radii are close to ones predicted by pressure transient tests.

Hybridization of the probability perturbation method with gradient information

DEFF Research Database (Denmark)

Johansen, Kent; Caers, J.; Suzuki, S.

2007-01-01

Geostatistically based history-matching methods make it possible to devise history-matching strategies that will honor geologic knowledge about the reservoir. However, the performance of these methods is known to be impeded by slow convergence rates resulting from the stochastic nature of the alg...

Petrographic studies on a newly discovered Indo-Arabian stone anchor from the Gulf of Kachchh, Gujarat: Implications for source area

Digital Repository Service at National Institute of Oceanography (India)

Tripati, S.; Mudholkar, A.; Khedekar, V.

of soil properties in agricultural farm and its application in pre- dicting surface map of hydraulic property. Curr. Sci., 2008, 95, 937–945. 21. Davis, B. M., Uses and abuses of cross-validation in geostatistics. Math. Geol., 1987, 19, 241–248. 22...

Uses of GIS for Homeland Security and Emergency Management for Higher Education Institutions

Science.gov (United States)

Murchison, Stuart B.

2010-01-01

Geographic information systems (GIS) are a major component of the geospatial sciences, which are also composed of geostatistical analysis, remote sensing, and global positional satellite systems. These systems can be integrated into GIS for georeferencing, pattern analysis, visualization, and understanding spatial concepts that transcend…

Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution

NARCIS (Netherlands)

Kilibarda, M.; Hengl, T.; Heuvelink, G.B.M.; Graler, B.; Pebesma, E.; Tadic, M.P.; Bajat, B.

2014-01-01

Combined Global Surface Summary of Day and European Climate Assessment and Dataset daily meteorological data sets (around 9000 stations) were used to build spatio-temporal geostatistical models and predict daily air temperature at ground resolution of 1km for the global land mass. Predictions in

Quantitative geomorphology with geographical information systems (GIS) for evolving societies and science

Science.gov (United States)

Gomez, C.; Oguchi, T.; Evans, I. S.

2016-05-01

Based on the two sessions on spatial analysis, GIS and geostatistics convened by T. Oguchi, I. Evans and C. Gomez at the 2013 International Association of Geomorphology in Paris, the conveners have edited two special issues on the topic: volume 242 and the present one.

A comparison of spatial rainfall estimation techniques: A case study ...

African Journals Online (AJOL)

Two geostatistical interpolation techniques (kriging and cokriging) were evaluated against inverse distance weighted (IDW) and global polynomial interpolation (GPI). Of the four spatial interpolators, kriging and cokriging produced results with the least root mean square error (RMSE). A digital elevation model (DEM) was ...

Journal of Earth System Science | Indian Academy of Sciences

Indian Academy of Sciences (India)

Linear trends of anomalously high gold values in the Florida Canyon gold deposit, Nevada have been identified using a combination of contour maps of gold (Au) concentration developed with a geographic information system (GIS) and variogram maps created using a geostatistical analysis package. These linear trends ...

Enhanced computational methods for quantifying the effect of geographic and environmental isolation on genetic differentiation

NARCIS (Netherlands)

Botta, Filippo; Eriksen, Casper; Fontaine, Michael Christophe; Guillot, Gilles

2015-01-01

In a recent paper, Bradburd et al. (2013) proposed a model to quantify the relative effect ofgeographic and environmental distance on genetic differentiation. Here, we enhance this method in several ways. 1. We modify the covariance model so as to fit better with mainstream geostatistical models and

Planning schistosomiasis control: investigation of alternative sampling strategies for Schistosoma mansoni to target mass drug administration of praziquantel in East Africa.

Science.gov (United States)

Sturrock, Hugh J W; Gething, Pete W; Ashton, Ruth A; Kolaczinski, Jan H; Kabatereine, Narcis B; Brooker, Simon

2011-09-01

In schistosomiasis control, there is a need to geographically target treatment to populations at high risk of morbidity. This paper evaluates alternative sampling strategies for surveys of Schistosoma mansoni to target mass drug administration in Kenya and Ethiopia. Two main designs are considered: lot quality assurance sampling (LQAS) of children from all schools; and a geostatistical design that samples a subset of schools and uses semi-variogram analysis and spatial interpolation to predict prevalence in the remaining unsurveyed schools. Computerized simulations are used to investigate the performance of sampling strategies in correctly classifying schools according to treatment needs and their cost-effectiveness in identifying high prevalence schools. LQAS performs better than geostatistical sampling in correctly classifying schools, but at a cost with a higher cost per high prevalence school correctly classified. It is suggested that the optimal surveying strategy for S. mansoni needs to take into account the goals of the control programme and the financial and drug resources available.

Combined SEM/AVS and attenuation of concentration models for the assessment of bioavailability and mobility of metals in sediments of Sepetiba Bay (SE Brazil).

Science.gov (United States)

Ribeiro, Andreza Portella; Figueiredo, Ana Maria Graciano; dos Santos, José Osman; Dantas, Elizabeth; Cotrim, Marycel Elena Barboza; Figueira, Rubens Cesar Lopes; Silva Filho, Emmanoel V; Wasserman, Julio Cesar

2013-03-15

This study proposes a new methodology to study contamination, bioavailability and mobility of metals (Cd, Cu, Ni, Pb, and Zn) using chemical and geostatistics approaches in marine sediments of Sepetiba Bay (SE Brazil). The chemical model of SEM (simultaneously extracted metals)/AVS (acid volatile sulfides) ratio uses a technique of cold acid extraction of metals to evaluate their bioavailability, and the geostatistical model of attenuation of concentrations estimates the mobility of metals. By coupling the two it was observed that Sepetiba Port, the urban area of Sepetiba and the riverine discharges may constitute potential sources of metals to Sepetiba Bay. The metals are concentrated in the NE area of the bay, where they tend to have their lowest mobility, as shown by the attenuation model, and are not bioavailable, as they tend to associate with sulfide and organic matter originated in the mangrove forests of nearby Guaratiba area. Copyright © 2013 Elsevier Ltd. All rights reserved.

Uncertainty estimation of the mass discharge from a contaminated site using a fully Bayesian framework

DEFF Research Database (Denmark)

Troldborg, Mads; Nowak, W.; Binning, Philip John

2010-01-01

with an uncertain geostatistical model and iii) measurement uncertainty. The method is tested on a TCE contaminated site for which four different conceptual models were set up. The mass discharge and the associated uncertainty are hereby determined. It is discussed which of the conceptual models is most likely...

a review of geothermal mapping techniques using remotely sensed

African Journals Online (AJOL)

Aliyu et al.

estimate land surface temperature and heat fluxes are also applied to aid thermal .... minimize the effect of temperature variations resulting from diurnal heating effects of the ... Models such as Kriging with External Drift (KED) together with geo-statistical ..... overcoming the limitations of cloud and thick vegetation in revealing ...

Spatio-temporal interpolation of soil water, temperature, and electrical conductivity in 3D + T

NARCIS (Netherlands)

Gasch, C.K.; Hengl, Tom; Gräler, Benedikt; Meyer, Hanna; Magney, T.S.; Brown, D.J.

2015-01-01

The paper describes a framework for modeling dynamic soil properties in 3-dimensions and time (3D + T) using soil data collected with automated sensor networks as a case study. Two approaches to geostatistical modeling and spatio-temporal predictions are described: (1) 3D + T predictive modeling

Journal of Earth System Science | Indian Academy of Sciences

Indian Academy of Sciences (India)

Accuracies of Real-Time Kinematic Global Positioning (RTK-GPS) system and Total Station (TS) were investigated in GIS environment. In geostatistical evaluations, Kriging method was used with spherical, exponential, and Gaussian models. The survey results demonstrated that an area of 3.5 ha or smaller can be best ...

Learning Kriging by an instructive program.

Science.gov (United States)

Cuador, José

2016-04-01

There are three types of problem classification: the deterministic, the approximated and the stochastic problems. First, in the deterministic problems the law of the phenomenon and the data are known in the entire domain and for each instant of time. In the approximated problems, the law of the phenomenon behavior is unknown but the data can be known in the entire domain and for each instant of time. In the stochastic problems much of the law and the data are unknown in the domain, so in this case the spatial behavior of the data can only be explained with probabilistic laws. This is the most important reason why the students of geo-sciences careers and others related careers need to take courses in advance estimation methods. A good example of this situation is the estimation grades in ore mineral deposit for which the Geostatistics was formalized by G. Matheron in 1962 [6]. Geostatistics is defined as the application of the theory of Random Function to the recognition and estimation of natural phenomenon [4]. Nowadays, Geostatistics is widely used in several fields of earth sciences, for example: Mining, Oil exploration, Environment, Agricultural, Forest and others [3]. It provides a wide variety of tools for spatial data analysis and allows analysing models which are subjected to degrees of uncertainty with the rigor of mathematics and formal statistical analysis [9]. Adequate models for the Kriging interpolator has been developed according to the data behavior; however there are two key steps in applying this interpolator properly: the semivariogram determination and the Kriging neighborhood selection. The main objective of this paper is to present these two elements using an instructive program.

Apcom 87. Proceedings of the twentieth international symposium on the application of computers and mathematics in the mineral industries V. 2

International Nuclear Information System (INIS)

King, R.P.; Barker, I.J.

1987-01-01

APCOM symposia provide a medium of exchange of technical expertise and experience for practitioners in the general field of applications of computers, operations research, mathematical and geostatistical techniques in the mineral industries. Contributors represent mine and plant personnel, academic and government or semi-government representatives, and the topics covered range from mining and metallurgical techniques and planning to financial analysis, project valuation, information systems, computer graphics, geostatistics, etc. For the 20th APCOM, the Proceedings have been divided into the three broad categories of mining, metallurgy and geostatistics, and are grouped accordingly into the three published volumes. APCOM '87, the twentieth in this series of international meetings, places major emphasis on the practical application of computers in the workplace to implement the theoretical techniques. Modelling, simulation and process control still seem to attract the major research efforts. Modelling of milling and flotation have a long history but are still active areas for research. Pyrometallurgy, gold recovery, separation processes with distributed partition functions like screening and gravity separation, and electrowinning, are also attracting attention. Computeraided design (CAD) is becoming a lot more usable and available throughout the industry. Process control is at the door of a new era, with the combination of sensor technology, reliable on-plant computers and viable theoretical techniques now becoming available as never before. CAD and process control have also given new impetus to the need to develop and test computer-based models of a wider range of unit operations. APCOM remains the most important forum for the discussion of new developments in the application of computer methods and mathematical techniques in the metallurgical industry

Inhomogeneities detection in annual precipitation time series in Portugal using direct sequential simulation

Science.gov (United States)

Caineta, Júlio; Ribeiro, Sara; Costa, Ana Cristina; Henriques, Roberto; Soares, Amílcar

2014-05-01

Climate data homogenisation is of major importance in monitoring climate change, the validation of weather forecasting, general circulation and regional atmospheric models, modelling of erosion, drought monitoring, among other studies of hydrological and environmental impacts. This happens because non-climate factors can cause time series discontinuities which may hide the true climatic signal and patterns, thus potentially bias the conclusions of those studies. In the last two decades, many methods have been developed to identify and remove these inhomogeneities. One of those is based on geostatistical simulation (DSS - direct sequential simulation), where local probability density functions (pdf) are calculated at candidate monitoring stations, using spatial and temporal neighbouring observations, and then are used for detection of inhomogeneities. This approach has been previously applied to detect inhomogeneities in four precipitation series (wet day count) from a network with 66 monitoring stations located in the southern region of Portugal (1980-2001). This study revealed promising results and the potential advantages of geostatistical techniques for inhomogeneities detection in climate time series. This work extends the case study presented before and investigates the application of the geostatistical stochastic approach to ten precipitation series that were previously classified as inhomogeneous by one of six absolute homogeneity tests (Mann-Kendall test, Wald-Wolfowitz runs test, Von Neumann ratio test, Standard normal homogeneity test (SNHT) for a single break, Pettit test, and Buishand range test). Moreover, a sensibility analysis is implemented to investigate the number of simulated realisations that should be used to accurately infer the local pdfs. Accordingly, the number of simulations per iteration is increased from 50 to 500, which resulted in a more representative local pdf. A set of default and recommended settings is provided, which will help

Variogram based and Multiple - Point Statistical simulation of shallow aquifer structures in the Upper Salzach valley, Austria

Science.gov (United States)

Jandrisevits, Carmen; Marschallinger, Robert

2014-05-01

Quarternary sediments in overdeepened alpine valleys and basins in the Eastern Alps bear substantial groundwater resources. The associated aquifer systems are generally geometrically complex with highly variable hydraulic properties. 3D geological models provide predictions of both geometry and properties of the subsurface required for subsequent modelling of groundwater flow and transport. In hydrology, geostatistical Kriging and Kriging based conditional simulations are widely used to predict the spatial distribution of hydrofacies. In the course of investigating the shallow aquifer structures in the Zell basin in the Upper Salzach valley (Salzburg, Austria), a benchmark of available geostatistical modelling and simulation methods was performed: traditional variogram based geostatistical methods, i.e. Indicator Kriging, Sequential Indicator Simulation and Sequential Indicator Co - Simulation were used as well as Multiple Point Statistics. The ~ 6 km2 investigation area is sampled by 56 drillings with depths of 5 to 50 m; in addition, there are 2 geophysical sections with lengths of 2 km and depths of 50 m. Due to clustered drilling sites, indicator Kriging models failed to consistently model the spatial variability of hydrofacies. Using classical variogram based geostatistical simulation (SIS), equally probable realizations were generated with differences among the realizations providing an uncertainty measure. The yielded models are unstructured from a geological point - they do not portray the shapes and lateral extensions of associated sedimentary units. Since variograms consider only two - point spatial correlations, they are unable to capture the spatial variability of complex geological structures. The Multiple Point Statistics approach overcomes these limitations of two point statistics as it uses a Training image instead of variograms. The 3D Training Image can be seen as a reference facies model where geological knowledge about depositional

Natural and artificial radionuclides in our environment

International Nuclear Information System (INIS)

Neu, Alfred; Bayer, Anton; Steinkopff, Thomas

2010-01-01

The conference proceedings cover contributions and posters concerning the following topics: emission control and monitoring, environmental radioactivity and radiation exposure; radon in the environment; radioecology and modeling: radioecology and tracer for environmental processes, modeling in environmental monitoring, environmental and geostatistics; physical metrology and radiochemical methods; regulations, regulatory standards and quality assurance.

Analysis of water content in salt deposits: its application to radioactive waste storage

International Nuclear Information System (INIS)

Cuevas Muller, C. de la.

1993-01-01

The salt deposits as radioactive storage medium are analyzed. This report studies the physical-chemical characteristics of water into salts deposits, its implications for the safety of the repository, and the transport water release mechanism. The last part analyzes the geochemical numerical data of correlation analysis, geostatistics analysis and interpretation of statistical data

Network analysis reveals multiscale controls on streamwater chemistry

Science.gov (United States)

Kevin J. McGuire; Christian E. Torgersen; Gene E. Likens; Donald C. Buso; Winsor H. Lowe; Scott W. Bailey

2014-01-01

By coupling synoptic data from a basin-wide assessment of streamwater chemistry with network-based geostatistical analysis, we show that spatial processes differentially affect biogeochemical condition and pattern across a headwater stream network. We analyzed a high-resolution dataset consisting of 664 water samples collected every 100 m throughout 32 tributaries in...

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...

Assessment of heterogeneous geological environment using geostatistical techniques

International Nuclear Information System (INIS)

Toida, Masaru; Suyama, Yasuhiro; Shiogama, Yukihiro; Atsumi, Hiroyuki; Abe, Yasunori; Furuichi, Mitsuaki

2003-02-01

'Geoscientific' research at Tono are developing site investigation and assessment techniques in geological environment. One of their important themes is to establish rational methodology to reduce uncertainties associated with the understanding of geological environment, which often exhibits significant heterogeneity. Purpose of this study is to identify and evaluate uncertainties associated with the understanding of geological environment. Because it is useful to guide designing effective site investigation techniques to reduce the uncertainty. For this, a methodology of the uncertainty analysis concerning the heterogeneous geological environment has been developed. In this report the methodology has also been tested through an exercise attempted in Tono area to demonstrate its applicability. This report summarizes as follows: 1) The exercise shows that the methodology considered 'variability' and 'ignorance' can demonstrate its applicability at three-dimensional case. 2) The exercise shows that the methodology can identity and evaluate uncertainties concerning ground water flow associated with performance assessment. 3) Based on sensitivity analyses, it is possible for the methodology to support designs of the following stage investigations to reduce the uncertainties efficiently. (author)

Uncertainty Estimate in Resources Assessment: A Geostatistical Contribution

International Nuclear Information System (INIS)

Souza, Luis Eduardo de; Costa, Joao Felipe C. L.; Koppe, Jair C.

2004-01-01

For many decades the mining industry regarded resources/reserves estimation and classification as a mere calculation requiring basic mathematical and geological knowledge. Most methods were based on geometrical procedures and spatial data distribution. Therefore, uncertainty associated with tonnages and grades either were ignored or mishandled, although various mining codes require a measure of confidence in the values reported. Traditional methods fail in reporting the level of confidence in the quantities and grades. Conversely, kriging is known to provide the best estimate and its associated variance. Among kriging methods, Ordinary Kriging (OK) probably is the most widely used one for mineral resource/reserve estimation, mainly because of its robustness and its facility in uncertainty assessment by using the kriging variance. It also is known that OK variance is unable to recognize local data variability, an important issue when heterogeneous mineral deposits with higher and poorer grade zones are being evaluated. Alternatively, stochastic simulation are used to build local or global uncertainty about a geological attribute respecting its statistical moments. This study investigates methods capable of incorporating uncertainty to the estimates of resources and reserves via OK and sequential gaussian and sequential indicator simulation The results showed that for the type of mineralization studied all methods classified the tonnages similarly. The methods are illustrated using an exploration drill hole data sets from a large Brazilian coal deposit

Preliminary Groundwater Simulations To Compare Different Reconstruction Methods of 3-d Alluvial Heterogeneity

Science.gov (United States)

Teles, V.; de Marsily, G.; Delay, F.; Perrier, E.

Alluvial floodplains are extremely heterogeneous aquifers, whose three-dimensional structures are quite difficult to model. In general, when representing such structures, the medium heterogeneity is modeled with classical geostatistical or Boolean meth- ods. Another approach, still in its infancy, is called the genetic method because it simulates the generation of the medium by reproducing sedimentary processes. We developed a new genetic model to obtain a realistic three-dimensional image of allu- vial media. It does not simulate the hydrodynamics of sedimentation but uses semi- empirical and statistical rules to roughly reproduce fluvial deposition and erosion. The main processes, either at the stream scale or at the plain scale, are modeled by simple rules applied to "sediment" entities or to conceptual "erosion" entities. The model was applied to a several kilometer long portion of the Aube River floodplain (France) and reproduced the deposition and erosion cycles that occurred during the inferred climate periods (15 000 BP to present). A three-dimensional image of the aquifer was gener- ated, by extrapolating the two-dimensional information collected on a cross-section of the floodplain. Unlike geostatistical methods, this extrapolation does not use a statis- tical spatial analysis of the data, but a genetic analysis, which leads to a more realistic structure. Groundwater flow and transport simulations in the alluvium were carried out with a three-dimensional flow code or simulator (MODFLOW), using different rep- resentations of the alluvial reservoir of the Aube River floodplain: first an equivalent homogeneous medium, and then different heterogeneous media built either with the traditional geostatistical approach simulating the permeability distribution, or with the new genetic model presented here simulating sediment facies. In the latter case, each deposited entity of a given lithology was assigned a constant hydraulic conductivity value. Results of these

The spatial distribution of Schistosoma mansoni infection in four regions of western Côte d’Ivoire

Directory of Open Access Journals (Sweden)

Rufin K. Assaré

2015-06-01

Full Text Available Schistosomiasis poses a considerable public health burden in sub- Saharan Africa and a sound understanding of the spatial distribution facilitates to better target control interventions. The objectives of this study were i to assess the prevalence of Schistosoma mansoni among school-aged children in four regions of western Côte d’Ivoire; ii to determine demographic, climatic and environmental factors that influence the distribution of S. mansoni; and iii to map and predict the distribution of S. mansoni in non-sampled locations. Parasitological surveys were carried out in 264 schools from June to December 2011. In each school, we aimed to examine 50 children for S. mansoni infection using duplicate Kato-Katz thick smears. Schools were georeferenced using a hand-held global positioning system receiver. Demographic data were obtained from readily available school lists, while climatic and environmental data were extracted from open-access remote sensing databases. Multivariable, binary non-spatial models and a Bayesian geostatistical logistic regression model were used to identify demographic, climatic and environmental risk factors for S. mansoni infection. Risk maps were developed based on observed S. mansoni prevalences and using Bayesian geostatistical models to predict prevalences at non-sampled locations. Overall, 12,462 children provided a sufficiently large stool sample to perform at least one Kato-Katz thick smear. The observed overall prevalence of S. mansoni infection was 39.9%, ranging from 0 to 100% at the unit of the school. Bayesian geostatistical analysis revealed that age, sex, altitude and difference between land surface temperature at day and night were significantly associated with S. mansoni infection. The S. mansoni risk map presented here is being been used by the national schistosomiasis control programme for spatial targeting of praziquantel and other interventions.

Evaluation of soil characterization technologies using a stochastic, value-of-information approach

International Nuclear Information System (INIS)

Kaplan, P.G.

1993-01-01

The US Department of Energy has initiated an integrated demonstration program to develop and compare new technologies for the characterization of uranium-contaminated soils. As part of this effort, a performance-assessment task was funded in February, 1993 to evaluate the field tested technologies. Performance assessment can be cleaned as the analysis that evaluates a system's, or technology's, ability to meet the criteria specified for performance. Four new technologies were field tested at the Fernald Environmental Management Restoration Co. in Ohio. In the next section, the goals of this performance assessment task are discussed. The following section discusses issues that must be resolved if the goals are to be successfully met. The author concludes with a discussion of the potential benefits to performance assessment of the approach taken. This paper is intended to be the first of a series of documentation that describes the work. Also in this proceedings is a paper on the field demonstration at the Fernald site and a description of the technologies (Tidwell et al, 1993) and a paper on the application of advanced geostatistical techniques (Rautman, 1993). The overall approach is to simply demonstrate the applicability of concepts that are well described in the literature but are not routinely applied to problems in environmental remediation, restoration, and waste management. The basic geostatistical concepts are documented in Clark (1979) and in Issaks and Srivastava (1989). Advanced concepts and applications, along with software, are discussed in Deutsch and Journel (1992). Integration of geostatistical modeling with a decision-analytic framework is discussed in Freeze et al (1992). Information-theoretic and probabilistic concepts are borrowed from the work of Shannon (1948), Jaynes (1957), and Harr (1987). The author sees the task as one of introducing and applying robust methodologies with demonstrated applicability in other fields to the problem at hand

Complementary programs for stochastic analysis of radionuclide transport

International Nuclear Information System (INIS)

Gomez Hernandez, J.J.

1993-01-01

The present programs will permit to analyze the risks using parametric and non parametric technic. The programs are presented in two groups: 1) variable estimation through indicator krigeaje and variable estimation by Cokrigeaje 2) variable simulation with multi gassiness stochastic model and non gassiness. This report includes new programs for the non parametric geostatistics

Characterization of heterogeneous reservoirs: sentinels method and quantification of uncertainties; Caracterisation des reservoirs heterogenes: methode des sentinelles et quantification des incertitudes

Energy Technology Data Exchange (ETDEWEB)

Mezghani, M.

1999-02-11

The aim of this thesis is to propose a new inversion method to allow both an improved reservoir characterization and a management of uncertainties. In this approach, the identification of the permeability distribution is conducted using the sentinel method in order to match the pressure data. This approach, based on optimal control theory, can be seen as an alternative of least-squares method. Here, we prove the existence of exact sentinels under regularity hypothesis. From a numerical point of view, we consider regularized sentinels. We suggest a novel approach to update the penalization coefficient in order to improve numerical robustness. Moreover, the flexibility of the sentinel method enables to develop a way to treat noisy pressure data. To deal with geostatistical modelling of permeability distribution, we propose to link the pilot point method with sentinels to reach the identification of permeability. We particularly focus on the optimal location of pilot points. Finally, we present an original method, based on adjoint state computations, to quantify the dynamic data contribution to the characterisation of a calibrated geostatistical model. (author) 67 refs.

Application of indicator kriging to the complementary use of bioindicators at three trophic levels

International Nuclear Information System (INIS)

Figueira, Rui; Tavares, Paula C.; Palma, Luis; Beja, Pedro; Sergio, Cecilia

2009-01-01

The use of biological indicators is widespread in environmental monitoring, although it has long been recognised that each bioindicator is generally associated with a range of potential limitations and shortcomings. To circumvent this problem, this study adopted the complementary use of bioindicators representing different trophic levels and providing different type of information, in an innovative approach to integrate knowledge and to estimate the overall health state of ecosystems. The approach is illustrated using mercury contamination in primary producers (mosses), primary consumers (domestic pigeons and red-legged partridges) and top predators (Bonelli's eagles) in southern Portugal. Indicator kriging geostatistics was used to identify the areas where mercury concentration was higher than the median for each species, and to produce an index that combines mercury contamination across trophic levels. Spatial patterns of mercury contamination were consistent across species. The combined index provided a new level of information useful in incorporating measures of overall environmental contamination into pollution studies. - Mercury levels in bioindicators at three trophic levels were combined using geostatistics to build an integrated environmental contamination index.

Method for determining the footprint area of air temperature and relative humidity - doi: 10.4025/actascitechnol.v35i2.11791

Directory of Open Access Journals (Sweden)

Elis Dener Lima Alves

2013-04-01

Full Text Available Despite the numerous studies in the area of urban climatology, there is still a relevant gap in this area corresponding to the demarcation of the footprint area on a variable. Various authors arbitrarily delimit this area without a prior study, which leads to significant errors in the results. In recent years, a variety of models to estimate the footprint area was presented mainly with stochastic and analytical approaches, usually expensive. Thus this article aimed to develop a methodology based on geostatistics for inference of the footprint area for temperature and relative humidity. By using geostatistics it was possible to observe that the radius of footprint had a temporal variation (between times and days and spatial variation (between points, pointing out the great importance in assessing the footprint area. However, for a better analysis of this method we suggest to model the anisotropy in future studies, because the footprint area behaves like an ellipse with different radii at different directions. And for this, it is necessary to collect data with a regular distribution within a mesh.  

Granulometric analysis at Lampulo Fishing Port (LFP) substrate, Banda Aceh, Indonesia

Science.gov (United States)

Purnawan, S.; Setiawan, I.; Haridhi, H. A.; Irham, M.

2018-01-01

The study of sediment granulometry was completed at Lampulo fishing port (LFP). The LFP is a main fishing port in Aceh Province, Indonesia, located at 5°34’35” N; 95°19’23” E. The purpose of the research is to study and construct the environment condition of the bottom substrate. The data was taken by incorporating coring method at 10 stations using purposive random sampling. The wet sieve method was used to analyze the grain size for geostatistical analysis. The geostatistical parameters analysis in this study is classified as mean, sorting, skewness and kurtosis. The result informs that the types of sediments are sand, sandy clay and clayey sand for all stations. Station 1, however, is found as the coarsest compares to the other stations. All of the sediment collected at each station displays moderately sorted to poor sorted, while kurtosis values may be categorized as very leptokurtic. The results of the sediment parameters indicate that the environment of harbor pool was in a stable state, related to a sheltered condition.

A spatial model for a stream networks of Citarik River with the environmental variables: potential of hydrogen (PH) and temperature

Science.gov (United States)

Bachrudin, A.; Mohamed, N. B.; Supian, S.; Sukono; Hidayat, Y.

2018-03-01

Application of existing geostatistical theory of stream networks provides a number of interesting and challenging problems. Most of statistical tools in the traditional geostatistics have been based on a Euclidean distance such as autocovariance functions, but for stream data is not permissible since it deals with a stream distance. To overcome this autocovariance developed a model based on the distance the flow with using convolution kernel approach (moving average construction). Spatial model for a stream networks is widely used to monitor environmental on a river networks. In a case study of a river in province of West Java, the objective of this paper is to analyze a capability of a predictive on two environmental variables, potential of hydrogen (PH) and temperature using ordinary kriging. Several the empirical results show: (1) The best fit of autocovariance functions for temperature and potential hydrogen (ph) of Citarik River is linear which also yields the smallest root mean squared prediction error (RMSPE), (2) the spatial correlation values between the locations on upstream and on downstream of Citarik river exhibit decreasingly

Joint Bayesian Stochastic Inversion of Well Logs and Seismic Data for Volumetric Uncertainty Analysis

Directory of Open Access Journals (Sweden)

Moslem Moradi

2015-06-01

Full Text Available Here in, an application of a new seismic inversion algorithm in one of Iran’s oilfields is described. Stochastic (geostatistical seismic inversion, as a complementary method to deterministic inversion, is perceived as contribution combination of geostatistics and seismic inversion algorithm. This method integrates information from different data sources with different scales, as prior information in Bayesian statistics. Data integration leads to a probability density function (named as a posteriori probability that can yield a model of subsurface. The Markov Chain Monte Carlo (MCMC method is used to sample the posterior probability distribution, and the subsurface model characteristics can be extracted by analyzing a set of the samples. In this study, the theory of stochastic seismic inversion in a Bayesian framework was described and applied to infer P-impedance and porosity models. The comparison between the stochastic seismic inversion and the deterministic model based seismic inversion indicates that the stochastic seismic inversion can provide more detailed information of subsurface character. Since multiple realizations are extracted by this method, an estimation of pore volume and uncertainty in the estimation were analyzed.

Potentially toxic metal contamination of urban soils and roadside dust in Shanghai, China

International Nuclear Information System (INIS)

Shi Guitao; Chen Zhenlou; Xu Shiyuan; Zhang Ju; Wang Li; Bi Chunjuan; Teng Jiyan

2008-01-01

A detailed investigation was conducted to understand the contamination characteristics of a selected set of potentially toxic metals in Shanghai. The amount of Pb, Zn, Cu, Cr, Cd and Ni were determined from 273 soil/dust samples collected within urban area. The results indicated that concentration of all metals except Ni in soils was significant, and metal pollution was even severer in roadside dust. A series of metal spatial distribution maps were created through geostatistical analysis, and the pollution hotspots tended to associate with city core area, major road junctions, and the regions close to industrial zones. In attempt of identifying the source of metals through geostatistical and multivariate statistical analyses, it was concluded as follows: Pb, Zn and Cu mainly originated from traffic contaminants; soil Ni was associated with natural concentration; Cd largely came from point-sourced industrial pollution; and Cr, Ni in dust were mainly related to atmospheric deposition. - Human activities have led to high accumulation of potentially toxic metals in urban soils and roadside dust of Shanghai

Potentially toxic metal contamination of urban soils and roadside dust in Shanghai, China

Energy Technology Data Exchange (ETDEWEB)

Shi Guitao [Key Laboratory of Geographic Information Science of Ministry of Education, East China Normal University, Shanghai 200062 (China); Chen Zhenlou [Key Laboratory of Geographic Information Science of Ministry of Education, East China Normal University, Shanghai 200062 (China)], E-mail: gt_shi@163.com; Xu Shiyuan [Key Laboratory of Geographic Information Science of Ministry of Education, East China Normal University, Shanghai 200062 (China); Zhang Ju [School of Environment and Planning, Liaocheng University, Liaocheng 252059 (China); Wang Li; Bi Chunjuan [Key Laboratory of Geographic Information Science of Ministry of Education, East China Normal University, Shanghai 200062 (China); Teng Jiyan [Shanghai Chongming Dongtan National Nature Reserve, Shanghai 202183 (China)

2008-11-15

A detailed investigation was conducted to understand the contamination characteristics of a selected set of potentially toxic metals in Shanghai. The amount of Pb, Zn, Cu, Cr, Cd and Ni were determined from 273 soil/dust samples collected within urban area. The results indicated that concentration of all metals except Ni in soils was significant, and metal pollution was even severer in roadside dust. A series of metal spatial distribution maps were created through geostatistical analysis, and the pollution hotspots tended to associate with city core area, major road junctions, and the regions close to industrial zones. In attempt of identifying the source of metals through geostatistical and multivariate statistical analyses, it was concluded as follows: Pb, Zn and Cu mainly originated from traffic contaminants; soil Ni was associated with natural concentration; Cd largely came from point-sourced industrial pollution; and Cr, Ni in dust were mainly related to atmospheric deposition. - Human activities have led to high accumulation of potentially toxic metals in urban soils and roadside dust of Shanghai.

Application of indicator kriging to the complementary use of bioindicators at three trophic levels

Energy Technology Data Exchange (ETDEWEB)

Figueira, Rui, E-mail: rui.figueira@iict.p [Jardim Botanico Tropical, Instituto de Investigacao Cientifica Tropical, Trav. Conde da Ribeira, 9, 1300-142 Lisboa (Portugal); CERENA, Instituto Superior Tecnico, Av. Rovisco Pais, 1049-001 Lisboa (Portugal); Tavares, Paula C. [CVRM-Geo-Systems Centre, Instituto Superior Tecnico, Av. Rovisco Pais, 1049-001 Lisboa (Portugal); Palma, Luis [CCMAR, Universidade do Algarve, FCMA, Campus de Gambelas, 8005-139 Faro (Portugal); Beja, Pedro [ERENA, Ordenamento e Gestao de Recursos Naturais, Rua Robalo Gouveia, 1-1A, 1900-392 Lisboa (Portugal); CIBIO, Centro de Investigacao em Biodiversidade e Recursos Geneticos, Campus Agrario de Vairao, Universidade do Porto, Vairao (Portugal); Sergio, Cecilia [Jardim Botanico, Museu Nacional de Historia Natural, Universidade de Lisboa, R. Escola Politecnica, 58, 1250-102 Lisboa (Portugal); CBA, Faculdade de Ciencias da Universidade de Lisboa, Campo Grande, Edificio C2, 1749-016 Lisboa (Portugal)

2009-10-15

The use of biological indicators is widespread in environmental monitoring, although it has long been recognised that each bioindicator is generally associated with a range of potential limitations and shortcomings. To circumvent this problem, this study adopted the complementary use of bioindicators representing different trophic levels and providing different type of information, in an innovative approach to integrate knowledge and to estimate the overall health state of ecosystems. The approach is illustrated using mercury contamination in primary producers (mosses), primary consumers (domestic pigeons and red-legged partridges) and top predators (Bonelli's eagles) in southern Portugal. Indicator kriging geostatistics was used to identify the areas where mercury concentration was higher than the median for each species, and to produce an index that combines mercury contamination across trophic levels. Spatial patterns of mercury contamination were consistent across species. The combined index provided a new level of information useful in incorporating measures of overall environmental contamination into pollution studies. - Mercury levels in bioindicators at three trophic levels were combined using geostatistics to build an integrated environmental contamination index.

Dimensionality of heavy metal distribution in waste disposal sites using nonlinear dynamics

International Nuclear Information System (INIS)

Modis, Kostas; Komnitsas, Kostas

2008-01-01

Mapping of heavy metal contamination in mining and waste disposal sites usually relies on geostatistical approaches and linear stochastic dynamics. The present paper aims to identify, using the Grassberger-Procaccia correlation dimension (CD) algorithm, the existence of a nonlinear deterministic and chaotic dynamic behaviour in the spatial pattern of arsenic, manganese and zinc concentration in a Russian coal waste disposal site. The analysis carried out yielded embedding dimension values ranging between 7 and 8 suggesting thus from a chaotic dynamic perspective that arsenic, manganese and zinc concentration in space is a medium dimensional problem for the regionalized scale considered in this study. This alternative nonlinear dynamics approach may complement conventional geostatistical studies and may be also used for the estimation of risk and the subsequent screening and selection of a feasible remediation scheme in wider mining and waste disposal sites. Finally, the synergistic effect of this study may be further elaborated if additional factors including among others presence of hot spots, density and depth of sampling, mineralogy of wastes and sensitivity of analytical techniques are taken into account

Spatial interpolation and simulation of post-burn duff thickness after prescribed fire

Science.gov (United States)

Peter R. Robichaud; S. M. Miller

1999-01-01

Prescribed fire is used as a site treatment after timber harvesting. These fires result in spatial patterns with some portions consuming all of the forest floor material (duff) and others consuming little. Prior to the burn, spatial sampling of duff thickness and duff water content can be used to generate geostatistical spatial simulations of these characteristics....

Geochemical studies in Alaska by the U.S. geological survey, 1989

International Nuclear Information System (INIS)

Goldfarb, R.J.; Nash, J.T.; Stoeser, J.W.

1990-01-01

This book contains six papers concerned with exploration geochemistry, and stable isotope and trace element chemistry of metallic ore deposits in Alaska. Application of geostatistical techniques to the National Uranium Resource Evaluation (NURE) program stream-sediment data allows to target new areas of southeastern Alaska that are favorable for Greens Creek-type volcanogenic massive sulfide (VMS) deposits

Neotectonics research in Catalonia

International Nuclear Information System (INIS)

Fontbote, J.M.; Santanach, P.F.; Vilaplana, J.M.

1984-01-01

Brief progress report on the neotectonics research work carried on in several areas of the Pyrenees, Catalan Massif and Coastal Ranges, and Ebro Basin. Structural and geomorphological methods are mainly applied. In several cases, available geophysical data and application of geostatistical methods are contributing to yield more precise results. Some comments on methodological questions are presented also. (author)

Hierarchical matrix approximation of large covariance matrices

KAUST Repository

Litvinenko, Alexander

2015-01-05

We approximate large non-structured covariance matrices in the H-matrix format with a log-linear computational cost and storage O(nlogn). We compute inverse, Cholesky decomposition and determinant in H-format. As an example we consider the class of Matern covariance functions, which are very popular in spatial statistics, geostatistics, machine learning and image analysis. Applications are: kriging and op- timal design.

Spatial variability in subsurface flow and transport: a review

International Nuclear Information System (INIS)

Gutjahr, A.L.; Bras, R.L.

1993-01-01

Stochastic models of spatial variations as they apply to both saturated and unsaturated flow and transport problems are examined in this paper. Both modeling and data interpretive geostatistical approaches are reviewed and an integrated discussion combining the two approaches given. The probabilistic content is of special interest for reliability and risk calculations for waste management and groundwater pollution studies. (author)

Environmental decision support system on base of geoinformational technologies for the analysis of nuclear accident consequences

International Nuclear Information System (INIS)

Haas, T.C.; Maigan, M.; Arutyunyan, R.V.; Bolshov, L.A.; Demianov, V.V.

1996-01-01

The report deals with description of the concept and prototype of environmental decision support system (EDSS) for the analysis of late off-site consequences of severe nuclear accidents and analysis, processing and presentation of spatially distributed radioecological data. General description of the available software, use of modem achievements of geostatistics and stochastic simulations for the analysis of spatial data are presented and discussed

Hierarchical matrix approximation of large covariance matrices

KAUST Repository

Litvinenko, Alexander; Genton, Marc G.; Sun, Ying; Tempone, Raul

2015-01-01

We approximate large non-structured covariance matrices in the H-matrix format with a log-linear computational cost and storage O(nlogn). We compute inverse, Cholesky decomposition and determinant in H-format. As an example we consider the class of Matern covariance functions, which are very popular in spatial statistics, geostatistics, machine learning and image analysis. Applications are: kriging and op- timal design.

Influence of sampling depth and post-sampling analysis time

African Journals Online (AJOL)

designed to select for growth of total coliform and faecal coliform bacteria. Five tubes were used for each of the three decimal dilutions (10mL, 1mL and 0.1mL). All ..... Robertson A., Porter. S., and Brodie G. Creative. Publishers, St. John's, p 13 - 31. Beliaeff B and Cochard M 1995. Applying geostatistics to identification of.

Heavy metals contents in agricultural topsoils in the Ebro basin (Spain). Application of the multivariate geoestatistical methods to study spatial variations

International Nuclear Information System (INIS)

Rodriguez Martin, Jose Antonio; Arias, Manuel Lopez; Grau Corbi, Jose Manuel

2006-01-01

In this work the content of seven heavy metals (Cd, Cr, Cu, Hg, Ni, Pb and Zn) and other parameters (the pH, organic matter, carbonates and granulometric fraction) in agricultural topsoil in the Ebro basin are quantified, based on 624 samples collected according to an 8 by 8 km square mesh. The average concentrations (mg/kg) obtained were: Cd 0.415 ± 0.163, Cr 20.27 ± 13.21, Cu 17.33 ± 14.97, Ni 20.50 ± 22.71, Pb 17.54 ± 10.41, Zn 17.53 ± 24.19 and Hg 35.6 ± 42.05 μg/kg. The concentration levels are relatively low in areas of high pH and low organic matter content concentration. The results of factor analysis group Cd, Cu, Hg, Pb and Zn in F1 and Cr y Ni in F2. The spatial heavy metals component maps based on geostatistical analysis, show definite association of these factors with the soil parent material. The local anomalies (found in Cu, Zn and Pb) are attributed to anthropogenic influence. - Geostatistical analyses showed definite association of metals with soil parent materials

Distribution of the precipitation in Colombia

International Nuclear Information System (INIS)

Romero Reyes, Jorge Arturo; Triana Zarate, Gabriel

2006-01-01

The primary target looks for to determine the distribution of the rain for the national territory using technical geostatistical, for such effect was used the rain data averages multi annual, available in 1027 sites (municipal heads of Colombia, and sites of special interest source IDEAM 1982 -1998. The rain data were analyzed through basic statistical processes like location measures and variability, geostatistical like semi-variogram, and directional influence was analyzed (anisotropy), that the mountainous system of the Andes and the zone of Convergence inter tropical corroborate the influence of the relief through (ZCIT) where converge trade winds as a result of the location of Colombia in the equatorial zone, the result corresponds to a continuous surface that shows to the distribution of the rain in Colombia map 2, generated by means of the interpolation process using the Kriging method, this surface will serve like source of intelligence in thematic general space analyses of level in manifold as agrologic for the advantage of the resource water and ground, also to make analyses correlated with variable like temperature, humidity, solar radiation, potential and real evapo-transpiration

Positive matrix factorization as source apportionment of soil lead and cadmium around a battery plant (Changxing County, China).

Science.gov (United States)

Xue, Jian-long; Zhi, Yu-you; Yang, Li-ping; Shi, Jia-chun; Zeng, Ling-zao; Wu, Lao-sheng

2014-06-01

Chemical compositions of soil samples are multivariate in nature and provide datasets suitable for the application of multivariate factor analytical techniques. One of the analytical techniques, the positive matrix factorization (PMF), uses a weighted least square by fitting the data matrix to determine the weights of the sources based on the error estimates of each data point. In this research, PMF was employed to apportion the sources of heavy metals in 104 soil samples taken within a 1-km radius of a lead battery plant contaminated site in Changxing County, Zhejiang Province, China. The site is heavily contaminated with high concentrations of lead (Pb) and cadmium (Cd). PMF successfully partitioned the variances into sources related to soil background, agronomic practices, and the lead battery plants combined with a geostatistical approach. It was estimated that the lead battery plants and the agronomic practices contributed 55.37 and 29.28%, respectively, for soil Pb of the total source. Soil Cd mainly came from the lead battery plants (65.92%), followed by the agronomic practices (21.65%), and soil parent materials (12.43%). This research indicates that PMF combined with geostatistics is a useful tool for source identification and apportionment.

Serial Analysis of Ten Precipitation-Based Indices by Land Use in Semiarid Regions

Directory of Open Access Journals (Sweden)

Victor M. Rodríguez-Moreno

2015-01-01

Full Text Available Open ecosystems in Mexico are under increasing pressure, due particularly to the expansion of cities and agricultural activities. These developments occur without integrating biodiversity concerns in land use planning and result in extensive fragmentation and transformation of the landscapes. The semiarid region of Mesa Central was characterized using ten precipitation-based indices. Using multivariate statistical and geostatistical spatial analysis techniques, the influence of those indices on five land use strata was explored. Land use analysis indicated that the maximum values of the five significant precipitation-based indices were found in Grasslands, Agricultural Use, and Shrubs; minimum values were characteristic of substrates Secondary Desert Vegetation and Other Use. Our results suggest that the greatest number of extreme precipitation events is likely to occur in open ecosystems and consequently will have a strong influence on landscaping and land use. The semivariogram analysis and geostatistical layers demand attention from research institutions, policy makers, researchers, and food producers to take the appropriate and coordinated actions to propose scenarios to deal with climate change. Perhaps this study can stimulate thought concerning research endeavours aimed at promoting initiatives for biodiversity conservation and planning programs for climate change mitigation.

Methodology and Applications in Non-linear Model-based Geostatistics

DEFF Research Database (Denmark)

Christensen, Ole Fredslund

that are approximately Gaussian. Parameter estimation and prediction for the transformed Gaussian model is studied. In some cases a transformation cannot possibly render the data Gaussian. A methodology for analysing such data was introduced by Diggle, Tawn and Moyeed (1998): The generalised linear spatial model...... priors for Bayesian inference is discussed. Procedures for parameter estimation and prediction are studied. Theoretical properties of Markov chain Monte Carlo algorithms are investigated, and different algorithms are compared. In addition, the thesis contains a manual for an R-package, geoRglmm, which...

Reconstruction of the ionospheric electron density by geostatistical inversion

Science.gov (United States)

Minkwitz, David; van den Boogaart, Karl Gerald; Hoque, Mainul; Gerzen, Tatjana

2015-04-01

The ionosphere is the upper part of the atmosphere where sufficient free electrons exist to affect the propagation of radio waves. Typically, the ionosphere extends from about 50 - 1000 km and its morphology is mainly driven by solar radiation, particle precipitation and charge exchange. Due to the strong ionospheric impact on many applications dealing with trans-ionospheric signals such as Global Navigation Satellite Systems (GNSS) positioning, navigation and remote sensing, the demand for a highly accurate reconstruction of the electron density is ever increasing. Within the Helmholtz Alliance project "Remote Sensing and Earth System Dynamics" (EDA) the utilization of the upcoming radar mission TanDEM-L and its related products are prepared. The TanDEM-L mission will operate in L-band with a wavelength of approximately 24 cm and aims at an improved understanding of environmental processes and ecosystem change, e.g. earthquakes, volcanos, glaciers, soil moisture and carbon cycle. Since its lower frequency compared to the X-band (3 cm) and C-band (5 cm) radar missions, the influence of the ionosphere will increase and might lead to a significant degradation of the radar image quality if no correction is applied. Consequently, our interest is the reconstruction of the ionospheric electron density in order to mitigate the ionospheric delay. Following the ionosphere's behaviour we establish a non-stationary and anisotropic spatial covariance model of the electron density separated into a vertical and horizontal component. In order to estimate the model's parameters we chose a maximum likelihood approach. This approach incorporates GNSS total electron content measurements, representing integral measurements of the electron density between satellite to receiver ray paths, and the NeQuick model as a non-stationary trend. Based on a multivariate normal distribution the spatial covariance model parameters are optimized and afterwards the 3D electron density can be calculated by kriging for arbitrary points or grids of interest.

Bayesian Analysis of Geostatistical Models With an Auxiliary Lattice

KAUST Repository

Park, Jincheol; Liang, Faming

2012-01-01

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

Tomography of the ionospheric electron density with geostatistical inversion

Directory of Open Access Journals (Sweden)

D. Minkwitz

2015-08-01

Full Text Available In relation to satellite applications like global navigation satellite systems (GNSS and remote sensing, the electron density distribution of the ionosphere has significant influence on trans-ionospheric radio signal propagation. In this paper, we develop a novel ionospheric tomography approach providing the estimation of the electron density's spatial covariance and based on a best linear unbiased estimator of the 3-D electron density. Therefore a non-stationary and anisotropic covariance model is set up and its parameters are determined within a maximum-likelihood approach incorporating GNSS total electron content measurements and the NeQuick model as background. As a first assessment this 3-D simple kriging approach is applied to a part of Europe. We illustrate the estimated covariance model revealing the different correlation lengths in latitude and longitude direction and its non-stationarity. Furthermore, we show promising improvements of the reconstructed electron densities compared to the background model through the validation of the ionosondes Rome, Italy (RO041, and Dourbes, Belgium (DB049, with electron density profiles for 1 day.

Hierarchical matrix approximation of large covariance matrices

KAUST Repository

Litvinenko, Alexander

2015-01-07

We approximate large non-structured covariance matrices in the H-matrix format with a log-linear computational cost and storage O(n log n). We compute inverse, Cholesky decomposition and determinant in H-format. As an example we consider the class of Matern covariance functions, which are very popular in spatial statistics, geostatistics, machine learning and image analysis. Applications are: kriging and optimal design

Linear regression on the characterization of elements of natural origin and possible implications in the use of ground

Energy Technology Data Exchange (ETDEWEB)

Oliveira, Teresa; Oliveira, Amílcar [CEAUL and Universidade Aberta (Portugal); Caroço, Adolfo [InstitutoPolitécnico de Portalegre (Portugal); Batista, Maria J. [INETI (Portugal); Oliveira, Maria Manuela [Universidade de Évora (Portugal); Borges, José [InstitutoSuperior de Agronomia,Universidade de Lisboa (Portugal)

2015-03-10

The observation of certain higher chemical element concentration, such as uranium and radiometric values, in the Alegrete-Assumar region of Portugal, has shown that locally occurrence of radioactive quartzites is responsible for these high values. The geostatistical treatment of exploration data and the crossing of the database with other variables, such as land use, allows one to study how these may affect the human health.

Proceedings (of the) first Canadian conference on environmental geotechnics. Comptes-rendus (de la) premiere conference canadienne de geotechnique environnementale

Energy Technology Data Exchange (ETDEWEB)

Chapuis, R P; Aubertin, M

1991-01-01

A conference was held to discuss responses to increasing soil and groundwater contamination. Papers were presented in the areas of regulations and guidelines, design, management and performance of sites, clay, soil-bentonite and geosynthetic barriers, hydrogeology, geochemistry and fate of contaminants, contaminated sites exploration and rehabilitation, leachate generation and treatment, and modelling and geostatistics. Separate abstracts have been prepared for 22 papers presented at the conference.

Hierarchical matrix approximation of large covariance matrices

KAUST Repository

Litvinenko, Alexander; Genton, Marc G.; Sun, Ying; Tempone, Raul

2015-01-01

We approximate large non-structured covariance matrices in the H-matrix format with a log-linear computational cost and storage O(n log n). We compute inverse, Cholesky decomposition and determinant in H-format. As an example we consider the class of Matern covariance functions, which are very popular in spatial statistics, geostatistics, machine learning and image analysis. Applications are: kriging and optimal design

Pollution fields estimation by local statistical adaptation and non-stationary approach; Estimation de champs de pollution par adaptation statistique locale et approche non stationnaire

Energy Technology Data Exchange (ETDEWEB)

Grancher, D. [Centre National de la Recherche Scientifique (CNRS), Lab. de Geographie Physique UMR 8591, 92 - Meudon-Bellevue (France); Bel, L. [Paris-11 Univ., Lab. de Mathematiques, 91 - Orsay (France); Vautard, R. [Ecole Polytechnique, Lab. de Meteorologie Dynamique, 91 - Palaiseau (France)

2005-07-01

Several methods are designed to produce objective and physically consistent maps of ozone concentration fields using a Chemistry Transport Model on a coarse grid (50 km) and a few monitoring stations. We use geostatistical methods to correct the CTM with external variables available in a cheap way: other pollutant measured, urbanism rate, coast effect... Methods taking into account the non stationarity have also been experimented. (authors)

Modelling estimation and analysis of dynamic processes from image sequences using temporal random closed sets and point processes with application to the cell exocytosis and endocytosis

OpenAIRE

Díaz Fernández, Ester

2010-01-01

In this thesis, new models and methodologies are introduced for the analysis of dynamic processes characterized by image sequences with spatial temporal overlapping. The spatial temporal overlapping exists in many natural phenomena and should be addressed properly in several Science disciplines such as Microscopy, Material Sciences, Biology, Geostatistics or Communication Networks. This work is related to the Point Process and Random Closed Set theories, within Stochastic Ge...

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

Science.gov (United States)

Høyer, Anne-Sophie; Vignoli, Giulio; Mejer Hansen, Thomas; Thanh Vu, Le; Keefer, Donald A.; Jørgensen, Flemming

2017-12-01

Most studies on the application of geostatistical simulations based on multiple-point statistics (MPS) to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2-D or quasi-3-D training images. In the present study, we demonstrate a novel strategy for 3-D MPS modelling characterized by (i) realistic 3-D training images and (ii) an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810 km2 in the southern part of Denmark. MPS simulations are performed on a subset of the geological succession (the lower to middle Miocene sediments) which is characterized by relatively uniform structures and dominated by sand and clay. The simulated domain is large and each of the geostatistical realizations contains approximately 45 million voxels with size 100 m × 100 m × 5 m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3-D geological model. We apply a series of different strategies for the simulations based on data quality, and develop a novel method to effectively create observed spatial trends. The training image is constructed as a relatively small 3-D voxel model covering an area of 90 km2. We use an iterative training image development strategy and find that even slight modifications in the training image create significant changes in simulations. Thus, this study shows how to include both the geological environment and the type and quality of input information in order to achieve optimal results from MPS modelling. We present a practical workflow to build the training image and

Fusion of Tomography Tests for DNAPL Source Zone Characterization: Technology Development and Validation

Science.gov (United States)

2011-07-01

McLaughlin and Townley , 1996 for a comprehensive review]. In general, the minimum-output-error [MOE] approach or its variation with Gauss-Newton’s...effort and data storage. McLaughlin and Townley [1996] further pointed out that “The extended Kalman filter is most useful in applications where the...from the maximum a posterior [McLaughlin and Townley , 1996] and the quasi-linear geostatistical inverse approach [Kitanidis, 1995]. The SSLE

Identification of seafloor provinces - specific applications at the deep-sea Hà ¥kon Mosby Mud Volcano and the North Sea

OpenAIRE

Jerosch, Kerstin

2006-01-01

The identification of distinct provinces is currently an emphasis of marine research geosciences. Typological approaches for the Hà ¥kon Mosby Mud Volcano and the North Sea combining geological, biological and chemical properties are accomplished by geostatistical, multivariate statistical, and GIS techniques. Besides scientific needs seafloor provinces support management decisions related to upcoming economic use of the seafloor and bear up to model spatio-temporal connections and changes o...

Spatial analysis of the distribution of Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) and losses in maize crop productivity using geo statistics

International Nuclear Information System (INIS)

Farias, Paulo R.S.; Miranda, Vicente S.; Ribeiro, Susane M.; Barbosa, Jose C.; Busoli, Antonio C.; Overal, William L.

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 Sao 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. (author)

Estimation of Fine Particulate Matter in Taipei Using Landuse Regression and Bayesian Maximum Entropy Methods

Directory of Open Access Journals (Sweden)

Yi-Ming Kuo

2011-06-01

Full Text Available Fine airborne particulate matter (PM2.5 has adverse effects on human health. Assessing the long-term effects of PM2.5 exposure on human health and ecology is often limited by a lack of reliable PM2.5 measurements. In Taipei, PM2.5 levels were not systematically measured until August, 2005. Due to the popularity of geographic information systems (GIS, the landuse regression method has been widely used in the spatial estimation of PM concentrations. This method accounts for the potential contributing factors of the local environment, such as traffic volume. Geostatistical methods, on other hand, account for the spatiotemporal dependence among the observations of ambient pollutants. This study assesses the performance of the landuse regression model for the spatiotemporal estimation of PM2.5 in the Taipei area. Specifically, this study integrates the landuse regression model with the geostatistical approach within the framework of the Bayesian maximum entropy (BME method. The resulting epistemic framework can assimilate knowledge bases including: (a empirical-based spatial trends of PM concentration based on landuse regression, (b the spatio-temporal dependence among PM observation information, and (c site-specific PM observations. The proposed approach performs the spatiotemporal estimation of PM2.5 levels in the Taipei area (Taiwan from 2005–2007.

Estimation of fine particulate matter in Taipei using landuse regression and bayesian maximum entropy methods.

Science.gov (United States)

Yu, Hwa-Lung; Wang, Chih-Hsih; Liu, Ming-Che; Kuo, Yi-Ming

2011-06-01

Fine airborne particulate matter (PM2.5) has adverse effects on human health. Assessing the long-term effects of PM2.5 exposure on human health and ecology is often limited by a lack of reliable PM2.5 measurements. In Taipei, PM2.5 levels were not systematically measured until August, 2005. Due to the popularity of geographic information systems (GIS), the landuse regression method has been widely used in the spatial estimation of PM concentrations. This method accounts for the potential contributing factors of the local environment, such as traffic volume. Geostatistical methods, on other hand, account for the spatiotemporal dependence among the observations of ambient pollutants. This study assesses the performance of the landuse regression model for the spatiotemporal estimation of PM2.5 in the Taipei area. Specifically, this study integrates the landuse regression model with the geostatistical approach within the framework of the Bayesian maximum entropy (BME) method. The resulting epistemic framework can assimilate knowledge bases including: (a) empirical-based spatial trends of PM concentration based on landuse regression, (b) the spatio-temporal dependence among PM observation information, and (c) site-specific PM observations. The proposed approach performs the spatiotemporal estimation of PM2.5 levels in the Taipei area (Taiwan) from 2005-2007.

SPATIAL VARIABILITY AND VITALITY OF EPIGEOUS TERMITE MOUNDS IN PASTURES OF MATO GROSSO DO SUL, BRAZIL

Directory of Open Access Journals (Sweden)

Sandra Santana Lima

2015-02-01

Full Text Available Epigeous termite mounds are frequently observed in pasture areas, but the processes regulating their population dynamics are poorly known. This study evaluated epigeous termite mounds in cultivated grasslands used as pastures, assessing their spatial distribution by means of geostatistics and evaluating their vitality. The study was conducted in the Cerrado biome in the municipality of Rio Brilhante, Mato Grosso do Sul, Brazil. In two pasture areas (Pasture 1 and Pasture 2, epigeous mounds (nests were georeferenced and analyzed for height, circumference and vitality (inhabited or not. The area occupied by the mounds was calculated and termite specimens were collected for taxonomic identification. The spatial distribution pattern of the mounds was analyzed with geostatistical procedures. In both pasture areas, all epigeous mounds were built by the same species, Cornitermes cumulans. The mean number of mounds per hectare was 68 in Pasture 1 and 127 in Pasture 2, representing 0.4 and 1 % of the entire area, respectively. A large majority of the mounds were active (vitality, 91 % in Pasture 1 and 84 % in Pasture 2. A “pure nugget effect” was observed in the semivariograms of height and nest circumference in both pastures reflecting randomized spatial distribution and confirming that the distribution of termite mounds in pastures had a non-standard distribution.

Morphometric Change Detection of Lake Hawassa in the Ethiopian Rift Valley

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Yonas Abebe

2018-05-01

Full Text Available The Ethiopian Rift Valley lakes have been subjected to environmental and ecological changes due to recent development endeavors and natural phenomena, which are visible in the alterations to the quality and quantity of the water resources. Monitoring lakes for temporal and spatial alterations has become a valuable indicator of environmental change. In this regard, hydrographic information has a paramount importance. The first extensive hydrographic survey of Lake Hawassa was conducted in 1999. In this study, a bathymetric map was prepared using advances in global positioning systems, portable sonar sounder technology, geostatistics, remote sensing and geographic information system (GIS software analysis tools with the aim of detecting morphometric changes. Results showed that the surface area of Lake Hawassa increased by 7.5% in 1999 and 3.2% in 2011 from that of 1985. Water volume decreased by 17% between 1999 and 2011. Silt accumulated over more than 50% of the bed surface has caused a 4% loss of the lake’s storage capacity. The sedimentation patterns identified may have been strongly impacted by anthropogenic activities including urbanization and farming practices located on the northern, eastern and western sides of the lake watershed. The study demonstrated this geostatistical modeling approach to be a rapid and cost-effective method for bathymetric mapping.

[Spatial pattern of land surface dead combustible fuel load in Huzhong forest area in Great Xing'an Mountains].

Science.gov (United States)

Liu, Zhi-Hua; Chang, Yu; Chen, Hong-Wei; Zhou, Rui; Jing, Guo-Zhi; Zhang, Hong-Xin; Zhang, Chang-Meng

2008-03-01

By using geo-statistics and based on time-lag classification standard, a comparative study was made on the land surface dead combustible fuels in Huzhong forest area in Great Xing'an Mountains. The results indicated that the first level land surface dead combustible fuel, i. e., 1 h time-lag dead fuel, presented stronger spatial auto-correlation, with an average of 762.35 g x m(-2) and contributing to 55.54% of the total load. Its determining factors were species composition and stand age. The second and third levels land surface dead combustible fuel, i. e., 10 h and 100 h time-lag dead fuels, had a sum of 610.26 g x m(-2), and presented weaker spatial auto-correlation than 1 h time-lag dead fuel. Their determining factor was the disturbance history of forest stand. The complexity and heterogeneity of the factors determining the quality and quantity of forest land surface dead combustible fuels were the main reasons for the relatively inaccurate interpolation. However, the utilization of field survey data coupled with geo-statistics could easily and accurately interpolate the spatial pattern of forest land surface dead combustible fuel loads, and indirectly provide a practical basis for forest management.

An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

Directory of Open Access Journals (Sweden)

M. Anitha

2012-04-01

Full Text Available Settings White Matter lesions (WMLs are small areas of dead cells found in parts of the brain. In general, it is difficult for medical experts to accurately quantify the WMLs due to decreased contrast between White Matter (WM and Grey Matter (GM. The aim of this paper is to
automatically detect the White Matter Lesions which is present in the brains of elderly people. WML detection process includes the following stages: 1. Image preprocessing, 2. Clustering (Fuzzy c-means clustering, Geostatistical Possibilistic clustering and Geostatistical Fuzzy clustering and 3.Optimization using Particle Swarm Optimization (PSO. The proposed system is tested on a database of 208 MRI images. GFCM yields high sensitivity of 89%, specificity of 94% and overall accuracy of 93% over FCM and GPC. The clustered brain images are then subjected to Particle Swarm Optimization (PSO. The optimized result obtained from GFCM-PSO provides sensitivity of 90%, specificity of 94% and accuracy of 95%. The detection results reveals that GFCM and GFCMPSO better localizes the large regions of lesions and gives less false positive rate when compared to GPC and GPC-PSO which captures the largest loads of WMLs only in the upper ventral horns of the brain.

Accounting for pH heterogeneity and variability in modelling human health risks from cadmium in contaminated land

International Nuclear Information System (INIS)

Gay, J. Rebecca; Korre, Anna

2009-01-01

The authors have previously published a methodology which combines quantitative probabilistic human health risk assessment and spatial statistical methods (geostatistics) to produce an assessment, incorporating uncertainty, of risks to human health from exposure to contaminated land. The model assumes a constant soil to plant concentration factor (CF veg ) when calculating intake of contaminants. This model is modified here to enhance its use in a situation where CF veg varies according to soil pH, as is the case for cadmium. The original methodology uses sequential indicator simulation (SIS) to map soil concentration estimates for one contaminant across a site. A real, age-stratified population is mapped across the contaminated area, and intake of soil contaminants by individuals is calculated probabilistically using an adaptation of the Contaminated Land Exposure Assessment (CLEA) model. The proposed improvement involves not only the geostatistical estimation of the contaminant concentration, but also that of soil pH, which in turn leads to a variable CF veg estimate which influences the human intake results. The results presented demonstrate that taking pH into account can influence the outcome of the risk assessment greatly. It is proposed that a similar adaptation could be used for other combinations of soil variables which influence CF veg .

Modeling Short-Range Soil Variability and its Potential Use in Variable-Rate Treatment of Experimental Plots

Directory of Open Access Journals (Sweden)

A Moameni

2011-02-01

Full Text Available Abstract In Iran, the experimental plots under fertilizer trials are managed in such a way that the whole plot area uniformly receives agricultural inputs. This could lead to biased research results and hence to suppressing of the efforts made by the researchers. This research was conducted in a selected site belonging to the Gonbad Agricultural Research Station, located in the semiarid region, northeastern Iran. The aim was to characterize the short-range spatial variability of the inherent and management-depended soil properties and to determine if this variation is large and can be managed at practical scales. The soils were sampled using a grid 55 m apart. In total, 100 composite soil samples were collected from topsoil (0-30 cm and were analyzed for calcium carbonate equivalent, organic carbon, clay, available phosphorus, available potassium, iron, copper, zinc and manganese. Descriptive statistics were applied to check data trends. Geostatistical analysis was applied to variography, model fitting and contour mapping. Sampling at 55 m made it possible to split the area of the selected experimental plot into relatively uniform areas that allow application of agricultural inputs with variable rates. Keywords: Short-range soil variability, Within-field soil variability, Interpolation, Precision agriculture, Geostatistics

Hybrid optimal design of the eco-hydrological wireless sensor network in the middle reach of the Heihe River Basin, China.

Science.gov (United States)

Kang, Jian; Li, Xin; Jin, Rui; Ge, Yong; Wang, Jinfeng; Wang, Jianghao

2014-10-14

The eco-hydrological wireless sensor network (EHWSN) in the middle reaches of the Heihe River Basin in China is designed to capture the spatial and temporal variability and to estimate the ground truth for validating the remote sensing productions. However, there is no available prior information about a target variable. To meet both requirements, a hybrid model-based sampling method without any spatial autocorrelation assumptions is developed to optimize the distribution of EHWSN nodes based on geostatistics. This hybrid model incorporates two sub-criteria: one for the variogram modeling to represent the variability, another for improving the spatial prediction to evaluate remote sensing productions. The reasonability of the optimized EHWSN is validated from representativeness, the variogram modeling and the spatial accuracy through using 15 types of simulation fields generated with the unconditional geostatistical stochastic simulation. The sampling design shows good representativeness; variograms estimated by samples have less than 3% mean error relative to true variograms. Then, fields at multiple scales are predicted. As the scale increases, estimated fields have higher similarities to simulation fields at block sizes exceeding 240 m. The validations prove that this hybrid sampling method is effective for both objectives when we do not know the characteristics of an optimized variables.

Spatial analysis of the distribution of Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) and losses in maize crop productivity using geo statistics

Energy Technology Data Exchange (ETDEWEB)

Farias, Paulo R.S.; Miranda, Vicente S.; Ribeiro, Susane M. [Universidade Federal Rural da Amazonia (UFRA), Belem, PA (Brazil). Inst. de Ciencias Agrarias]. E-mail: paulo.farias@ufra.edu.br; Barbosa, Jose C. [UNESP, Jaboticabal, SP (Brazil). Faculdade de Ciencias Agrarias e Veterinarias. Dept. de Ciencias Exatas; Busoli, Antonio C. [UNESP, Jaboticabal, SP (Brazil). Faculdade de Ciencias Agrarias e Veterinarias. Dept. de Fitossanidade; Overal, William L. [Museu Paraense Emilio Goeldi (MPEG), Belem, PA (Brazil). Coordenacao de Zoologia

2008-05-15

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 Sao 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. (author)

Distribution of petrophysical properties for sandy-clayey reservoirs by fractal interpolation

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M. Lozada-Zumaeta

2012-04-01

Full Text Available The sandy-clayey hydrocarbon reservoirs of the Upper Paleocene and Lower Eocene located to the north of Veracruz State, Mexico, present highly complex geological and petrophysical characteristics. These reservoirs, which consist of sandstone and shale bodies within a depth interval ranging from 500 to 2000 m, were characterized statistically by means of fractal modeling and geostatistical tools. For 14 wells within an area of study of approximately 6 km², various geophysical well logs were initially edited and further analyzed to establish a correlation between logs and core data. The fractal modeling based on the R/S (rescaled range methodology and the interpolation method by successive random additions were used to generate pseudo-well logs between observed wells. The application of geostatistical tools, sequential Gaussian simulation and exponential model variograms contributed to estimate the spatial distribution of petrophysical properties such as effective porosity (PHIE, permeability (K and shale volume (VSH. From the analysis and correlation of the information generated in the present study, it can be said, from a general point of view, that the results not only are correlated with already reported information but also provide significant characterization elements that would be hardly obtained by means of conventional techniques.

Mapping the distribution of the denitrifier community at large scales (Invited)

Science.gov (United States)

Philippot, L.; Bru, D.; Ramette, A.; Dequiedt, S.; Ranjard, L.; Jolivet, C.; Arrouays, D.

2010-12-01

Little information is available regarding the landscape-scale distribution of microbial communities and its environmental determinants. Here we combined molecular approaches and geostatistical modeling to explore spatial patterns of the denitrifying community at large scales. The distribution of denitrifrying community was investigated over 107 sites in Burgundy, a 31 500 km2 region of France, using a 16 X 16 km sampling grid. At each sampling site, the abundances of denitrifiers and 42 soil physico-chemical properties were measured. The relative contributions of land use, spatial distance, climatic conditions, time and soil physico-chemical properties to the denitrifier spatial distribution were analyzed by canonical variation partitioning. Our results indicate that 43% to 85% of the spatial variation in community abundances could be explained by the measured environmental parameters, with soil chemical properties (mostly pH) being the main driver. We found spatial autocorrelation up to 740 km and used geostatistical modelling to generate predictive maps of the distribution of denitrifiers at the landscape scale. Studying the distribution of the denitrifiers at large scale can help closing the artificial gap between the investigation of microbial processes and microbial community ecology, therefore facilitating our understanding of the relationships between the ecology of denitrifiers and N-fluxes by denitrification.

Mapping spatial patterns of denitrifiers at large scales (Invited)

Science.gov (United States)

Philippot, L.; Ramette, A.; Saby, N.; Bru, D.; Dequiedt, S.; Ranjard, L.; Jolivet, C.; Arrouays, D.

2010-12-01

Little information is available regarding the landscape-scale distribution of microbial communities and its environmental determinants. Here we combined molecular approaches and geostatistical modeling to explore spatial patterns of the denitrifying community at large scales. The distribution of denitrifrying community was investigated over 107 sites in Burgundy, a 31 500 km2 region of France, using a 16 X 16 km sampling grid. At each sampling site, the abundances of denitrifiers and 42 soil physico-chemical properties were measured. The relative contributions of land use, spatial distance, climatic conditions, time and soil physico-chemical properties to the denitrifier spatial distribution were analyzed by canonical variation partitioning. Our results indicate that 43% to 85% of the spatial variation in community abundances could be explained by the measured environmental parameters, with soil chemical properties (mostly pH) being the main driver. We found spatial autocorrelation up to 739 km and used geostatistical modelling to generate predictive maps of the distribution of denitrifiers at the landscape scale. Studying the distribution of the denitrifiers at large scale can help closing the artificial gap between the investigation of microbial processes and microbial community ecology, therefore facilitating our understanding of the relationships between the ecology of denitrifiers and N-fluxes by denitrification.

Spatio-Temporal Patterns of Barmah Forest Virus Disease in Queensland, Australia

Science.gov (United States)

Naish, Suchithra; Hu, Wenbiao; Mengersen, Kerrie; Tong, Shilu

2011-01-01

Background Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. Methods/Principal Findings We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ2 = 7587, df = 7327,pQueensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland. PMID:22022430

Interpolación espacial de la evapotranspiración del cultivo de referencia, ET0 a partir de imágenes de satélite

OpenAIRE

Sánchez Martínez, Marcela; Chuvieco Salinero, Emilio

2002-01-01

This paper intends to obtain accurate estimations of reference évapotranspiration from multitemporal analysis of NOAA-AVHRR images. The study area corresponds to the Autonomous Community of Andalucía, and the period of analysis comprehends the spring and summer seasons of 1994 to 1997. Results obtained from multiple regression analysis are compared with those derived from spatial interpolation, using geostatistical methods. The results show a better fitting and more realistic trends for those...

Spatial variability of noise level in agricultural machines Variabilidade espacial do nível de ruído em máquinas agrícolas

OpenAIRE

Tadayuki Yanagi Junior; Leonardo Schiassi; Diogo F. Rossoni; Patrícia F. Ponciano; Renato R. de Lima

2012-01-01

The knowledge of the spatial variability of noise levels and the build of kriging maps can help the evaluation of the salubrity of environments occupied by agricultural workers. Therefore, the objective of this research was to characterize the spatial variability of the noise level generated by four agricultural machines, using geostatistics, and to verify if the values are within the limits of human comfort. The evaluated machines were: harvester, chainsaw, brushcutter and tractor. The data ...

Uranium assessment for the Precambrian pebble conglomerates in southeastern Wyoming

International Nuclear Information System (INIS)

Borgman, L.E.; Sever, C.; Quimby, W.F.; Andrew, M.E.; Karlstrom, K.E.; Houston, R.S.

1981-03-01

This volume is a geostatistical resource estimate of uranium and thorium in quartz-pebble conglomerates, and is a companion to Volume 1: The Geology and Uranium Potential to Precambrian Conglomerates in the Medicine Bow Mountains and Sierra Madre of Southeastern Wyoming; and to Volume 2: Drill-Hole Data, Drill-Site Geology, and Geochemical Data from the Study of Precambrian Uraniferous Conglomerates of the Medicine Bow Mountains and the Sierra Madre of Southeastern Wyoming

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.

Periodicity in spatial data and geostatistical models: autocorrelation between patches

Science.gov (United States)

Volker C. Radeloff; Todd F. Miller; Hong S. He; David J. Mladenoff

2000-01-01

Several recent studies in landscape ecology have found periodicity in correlograms or semi-variograms calculated, for instance, from spatial data of soils, forests, or animal populations. Some of the studies interpreted this as an indication of regular or periodic landscape patterns. This interpretation is in disagreement with other studies that doubt whether such...

Reservoir Modeling Combining Geostatistics with Markov Chain Monte Carlo Inversion

DEFF Research Database (Denmark)

Zunino, Andrea; Lange, Katrine; Melnikova, Yulia

2014-01-01

We present a study on the inversion of seismic reflection data generated from a synthetic reservoir model. Our aim is to invert directly for rock facies and porosity of the target reservoir zone. We solve this inverse problem using a Markov chain Monte Carlo (McMC) method to handle the nonlinear...

Geostatistical estimates of future recharge for the Death Valley region

International Nuclear Information System (INIS)

Hevesi, J.A.; Flint, A.L.

1998-01-01

Spatially distributed estimates of regional ground water recharge rates under both current and potential future climates are needed to evaluate a potential geologic repository for high-level nuclear waste at Yucca Mountain, Nevada, which is located within the Death Valley ground-water region (DVGWR). Determining the spatial distribution of recharge is important for regional saturated-zone ground-water flow models. In the southern Nevada region, the Maxey-Eakin method has been used for estimating recharge based on average annual precipitation. Although this method does not directly account for a variety of location-specific factors which control recharge (such as bedrock permeability, soil cover, and net radiation), precipitation is the primary factor that controls in the region. Estimates of recharge obtained by using the Maxey-Eakin method are comparable to estimates of recharge obtained by using chloride balance studies. The authors consider the Maxey-Eakin approach as a relatively simple method of obtaining preliminary estimates of recharge on a regional scale

Impact of radionuclide spatial variability on groundwater quality downstream from a shallow waste burial in the Chernobyl Exclusion Zone

Science.gov (United States)

Nguyen, H. L.; de Fouquet, C.; Courbet, C.; Simonucci, C. A.

2016-12-01

The effects of spatial variability of hydraulic parameters and initial groundwater plume localization on the possible extent of groundwater pollution plumes have already been broadly studied. However, only a few studies, such as Kjeldsen et al. (1995), take into account the effect of source term spatial variability. We explore this question with the 90Sr migration modeling from a shallow waste burial located in the Chernobyl Exclusion Zone to the underlying sand aquifer. Our work is based upon groundwater sampled once or twice a year since 1995 until 2015 from about 60 piezometers and more than 3,000 137Cs soil activity measurements. These measurements were taken in 1999 from one of the trenches dug after the explosion of the Chernobyl nuclear power plant, the so-called "T22 Trench", where radioactive waste was buried in 1987. The geostatistical analysis of 137Cs activity data in soils from Bugai et al. (2005) is first reconsidered to delimit the trench borders using georadar data as a covariable and to perform geostatistical simulations in order to evaluate the uncertainties of this inventory. 90Sr activity in soils is derived from 137Cs/154Eu and 90Sr/154Eu activity ratios in Chernobyl hot fuel particles (Bugai et al., 2003). Meanwhile, a coupled 1D non saturated/3D saturated transient transport model is constructed under the MELODIE software (IRSN, 2009). The previous 90Sr transport model developed by Bugai et al. (2012) did not take into account the effect of water table fluctuations highlighted by Van Meir et al. (2007) which may cause some discrepancies between model predictions and field observations. They are thus reproduced on a 1D vertical non saturated model. The equiprobable radionuclide localization maps produced by the geostatistical simulations are selected to illustrate different heterogeneities in the radionuclide inventory and are implemented in the 1D model. The obtained activity fluxes from all the 1D vertical models are then injected in a 3D

Advantages and applicability of commonly used homogenisation methods for climate data

Science.gov (United States)

Ribeiro, Sara; Caineta, Júlio; Henriques, Roberto; Soares, Amílcar; Costa, Ana Cristina

2014-05-01

Homogenisation of climate data is a very relevant subject since these data are required as an input in a wide range of studies, such as atmospheric modelling, weather forecasting, climate change monitoring, or hydrological and environmental projects. Often, climate data series include non-natural irregularities which have to be detected and removed prior to their use, otherwise it would generate biased and erroneous results. Relocation of weather stations or changes in the measuring instruments are amongst the most relevant causes for these inhomogeneities. Depending on the climate variable, its temporal resolution and spatial continuity, homogenisation methods can be more or less effective. For example, due to its natural variability, precipitation is identified as a very challenging variable to be homogenised. During the last two decades, numerous methods have been proposed to homogenise climate data. In order to compare, evaluate and develop those methods, the European project COST Action ES0601, Advances in homogenisation methods of climate series: an integrated approach (HOME), was released in 2008. Existing homogenisation methods were improved based on the benchmark exercise issued by this project. A recent approach based on Direct Sequential Simulation (DSS), not yet evaluated by the benchmark exercise, is also presented as an innovative methodology for homogenising climate data series. DSS already proved to be a successful geostatistical method in environmental and hydrological studies, and it provides promising results for the homogenisation of climate data. Since DSS is a geostatistical stochastic approach, it accounts for the joint spatial and temporal dependence between observations, as well as the relative importance of stations both in terms of distance and correlation. This work presents a chronological review of the most commonly used homogenisation methods for climate data and available software packages. A short description and classification is

Spatial structure of monthly rainfall measurements average over 25 years and trends of the hourly variability of a current rainy day in Rwanda.

Science.gov (United States)

Nduwayezu, Emmanuel; Kanevski, Mikhail; Jaboyedoff, Michel

2013-04-01

Climate plays a vital role in a wide range of socio-economic activities of most nations particularly of developing countries. Climate (rainfall) plays a central role in agriculture which is the main stay of the Rwandan economy and community livelihood and activities. The majority of the Rwandan population (81,1% in 2010) relies on rain fed agriculture for their livelihoods, and the impacts of variability in climate patterns are already being felt. Climate-related events like heavy rainfall or too little rainfall are becoming more frequent and are impacting on human wellbeing.The torrential rainfall that occurs every year in Rwanda could disturb the circulation for many days, damages houses, infrastructures and causes heavy economic losses and deaths. Four rainfall seasons have been identified, corresponding to the four thermal Earth ones in the south hemisphere: the normal season (summer), the rainy season (autumn), the dry season (winter) and the normo-rainy season (spring). Globally, the spatial rainfall decreasing from West to East, especially in October (spring) and February (summer) suggests an «Atlantic monsoon influence» while the homogeneous spatial rainfall distribution suggests an «Inter-tropical front» mechanism. What is the hourly variability in this mountainous area? Is there any correlation with the identified zones of the monthly average series (from 1965 to 1990 established by the Rwandan meteorological services)? Where could we have hazards with several consecutive rainy days (using forecasted datas from the Norwegian Meteorological Institute)? Spatio-temporal analysis allows for identifying and explaining large-scale anomalies which are useful for understanding hydrological characteristics and subsequently predicting these hydrological events. The objective of our current research (Rainfall variability) is to proceed to an evaluation of the potential rainfall risk by applying advanced geospatial modelling tools in Rwanda: geostatistical

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

Directory of Open Access Journals (Sweden)

A.-S. Høyer

2017-12-01

Full Text Available Most studies on the application of geostatistical simulations based on multiple-point statistics (MPS to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2-D or quasi-3-D training images. In the present study, we demonstrate a novel strategy for 3-D MPS modelling characterized by (i realistic 3-D training images and (ii an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810 km2 in the southern part of Denmark. MPS simulations are performed on a subset of the geological succession (the lower to middle Miocene sediments which is characterized by relatively uniform structures and dominated by sand and clay. The simulated domain is large and each of the geostatistical realizations contains approximately 45 million voxels with size 100 m  ×  100 m  ×  5 m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3-D geological model. We apply a series of different strategies for the simulations based on data quality, and develop a novel method to effectively create observed spatial trends. The training image is constructed as a relatively small 3-D voxel model covering an area of 90 km2. We use an iterative training image development strategy and find that even slight modifications in the training image create significant changes in simulations. Thus, this study shows how to include both the geological environment and the type and quality of input information in order to achieve optimal results from MPS modelling. We present a practical

Producing Distribution Maps for a Spatially-Explicit Ecosystem Model Using Large Monitoring and Environmental Databases and a Combination of Interpolation and Extrapolation

Directory of Open Access Journals (Sweden)

Arnaud Grüss

2018-01-01

Full Text Available To be able to simulate spatial patterns of predator-prey interactions, many spatially-explicit ecosystem modeling platforms, including Atlantis, need to be provided with distribution maps defining the annual or seasonal spatial distributions of functional groups and life stages. We developed a methodology combining extrapolation and interpolation of the predictions made by statistical habitat models to produce distribution maps for the fish and invertebrates represented in the Atlantis model of the Gulf of Mexico (GOM Large Marine Ecosystem (LME (“Atlantis-GOM”. This methodology consists of: (1 compiling a large monitoring database, gathering all the fisheries-independent and fisheries-dependent data collected in the northern (U.S. GOM since 2000; (2 compiling a large environmental database, storing all the environmental parameters known to influence the spatial distribution patterns of fish and invertebrates of the GOM; (3 fitting binomial generalized additive models (GAMs to the large monitoring and environmental databases, and geostatistical binomial generalized linear mixed models (GLMMs to the large monitoring database; and (4 employing GAM predictions to infer spatial distributions in the southern GOM, and GLMM predictions to infer spatial distributions in the U.S. GOM. Thus, our methodology allows for reasonable extrapolation in the southern GOM based on a large amount of monitoring and environmental data, and for interpolation in the U.S. GOM accurately reflecting the probability of encountering fish and invertebrates in that region. We used an iterative cross-validation procedure to validate GAMs. When a GAM did not pass the validation test, we employed a GAM for a related functional group/life stage to generate distribution maps for the southern GOM. In addition, no geostatistical GLMMs were fit for the functional groups and life stages whose depth, longitudinal and latitudinal ranges within the U.S. GOM are not entirely covered by

Environment modeling and mathematics; Modelisation de l'environnement et mathematiques

Energy Technology Data Exchange (ETDEWEB)

Armand, P.; Renard, F. [CEA Bruyeres-le-Chatel, 91 (France)

2011-01-15

Environmental modeling is a permanently expanding field of studies and research, relying on numerous mathematical techniques. Some of them are illustrated here, in the framework of the pollutant transport in the atmosphere and hydro-geosphere, two extensively developed subjects at CEA, both for compliance with regulatory requirements, and for impact assessment of gaseous or liquid accidental releases. Our paper deals with meteorological forecast, hydrogeology, fluid mechanics, multiple space and time scales, data assimilation, optimization, geo-statistics and uncertainties. (authors)

Spatial analysis of radiocesium food contamination in rural settlements of Belarus

International Nuclear Information System (INIS)

Krivoruchko, K.; Makejchik, A.

1997-01-01

An analysis of 53207 records of cesium 137 contents in 83 types of food products obtained in 1993 in Belarus was carried out. Internal exposure from intake of eight selected food components has been estimated. To map the non-uniformly distributed data, different geostatistical approaches are used. The results of spatial analysis of long term internal dose loads on populations under high radiation risk can be used in decision making. (authors). 9 refs., 1 tab., 4 figs

Adaptive sampling program support for expedited site characterization

International Nuclear Information System (INIS)

Johnson, R.

1993-01-01

Expedited site characterizations offer substantial savings in time and money when assessing hazardous waste sites. Key to some of these savings is the ability to adapt a sampling program to the ''real-time'' data generated by an expedited site characterization. This paper presents a two-prong approach to supporting adaptive sampling programs: a specialized object-oriented database/geographical information system for data fusion, management and display; and combined Bayesian/geostatistical methods for contamination extent estimation and sample location selection

Multivariate-Statistical Assessment of Heavy Metals for Agricultural Soils in Northern China

OpenAIRE

Yang, Pingguo; Yang, Miao; Mao, Renzhao; Shao, Hongbo

2014-01-01

The study evaluated eight heavy metals content and soil pollution from agricultural soils in northern China. Multivariate and geostatistical analysis approaches were used to determine the anthropogenic and natural contribution of soil heavy metal concentrations. Single pollution index and integrated pollution index could be used to evaluate soil heavy metal risk. The results show that the first factor explains 27.3% of the eight soil heavy metals with strong positive loadings on Cu, Zn, and C...

Calculation of climatic reference values and its use for automatic outlier detection in meteorological datasets

Directory of Open Access Journals (Sweden)

B. Téllez

2008-04-01

Full Text Available The climatic reference values for monthly and annual average air temperature and total precipitation in Catalonia – northeast of Spain – are calculated using a combination of statistical methods and geostatistical techniques of interpolation. In order to estimate the uncertainty of the method, the initial dataset is split into two parts that are, respectively, used for estimation and validation. The resulting maps are then used in the automatic outlier detection in meteorological datasets.

Value Assessment of Artificial Wetland Derived from Mining Subsided Lake: A Case Study of Jiuli Lake Wetland in Xuzhou

Directory of Open Access Journals (Sweden)

Laijian Wang

2017-10-01

Full Text Available Mining subsided lakes are major obstacles for ecological restoration and resource reuse in mining regions. Transforming mining subsided lakes into artificial wetlands is an ecological restoration approach that has been attempted in China in recent years, but a value assessment of the approach still needs systematic research. This paper considers Jiuli Lake wetland, an artificial wetland derived from restoration of a mining subsided lake in plain area, as a case study. A value assessment model for the artificial wetland was established based on cost–benefit analysis by means of field monitoring, social surveys, GIS geostatistics, raster calculation methods, etc. Empirical analysis and calculations were performed on the case study region. The following conclusions were drawn: (1 after ecological restoration, ecosystem services of Jiuli Lake wetland which has become a national level wetland park yield positive values; (2 the improved environment of the Jiuli Lake wetland has a spillover effect on the price of surrounding land, resulting in land price appreciation; (3 using GIS geostatistics and raster calculation methods, the impact range, strength, and value of the spillover effect can be explicitly measured; (4 through the establishment of a value assessment model of the artificial wetland, incomes of the ecological restoration was found to be sufficient to cover the implementation costs, which provides a research foundation for economic feasibility of ecological restoration of mining subsided lakes.

Rapid identification of soil cadmium pollution risk at regional scale based on visible and near-infrared spectroscopy

International Nuclear Information System (INIS)

Chen, Tao; Chang, Qingrui; Clevers, J.G.P.W.; Kooistra, L.

2015-01-01

Soil heavy metal pollution due to long-term sewage irrigation is a serious environmental problem in many irrigation areas in northern China. Quickly identifying its pollution status is an important basis for remediation. Visible-near-infrared reflectance spectroscopy (VNIRS) provides a useful tool. In a case study, 76 soil samples were collected and their reflectance spectra were used to estimate cadmium (Cd) concentration by partial least squares regression (PLSR) and back propagation neural network (BPNN). To reduce noise, six pre-treatments were compared, in which orthogonal signal correction (OSC) was first used in soil Cd estimation. Spectral analysis and geostatistics were combined to identify Cd pollution hotspots. Results showed that Cd was accumulated in topsoil at the study area. OSC can effectively remove irrelevant information to improve prediction accuracy. More accurate estimation was achieved by applying a BPNN. Soil Cd pollution hotspots could be identified by interpolating the predicted values obtained from spectral estimates. - Highlights: • Soil reflectance spectroscopy provides a promising tool for detecting soil contaminants. • Orthogonal signal correction efficiently extracted information from noisy spectra. • Back propagation neural network achieved a more accurate estimation for soil Cd. • Soil Cd pollution hotspots could be identified by interpolating the predicted Cd. - Combining spectral analysis and geostatistics can provide a rapid method for identifying the pollution hotspot of soil heavy metal at regional scale.

Spatial variability of forage yield and soil physical attributes of a Brachiaria decumbens pasture in the Brazilian Cerrado

Directory of Open Access Journals (Sweden)

Cristiano Magalhães Pariz

2011-10-01

Full Text Available The objective of this study was to analyze variability, linear and spatial correlations of forage dry mass yield (FDM and dry matter percentage (DM% of Brachiaria decumbens with the bulk density (BD, gravimetric (GM and volumetric (VM moisture, mechanical resistance to penetration (RP and organic matter content (OM, at depths 1 (0-0.10 m and 2 (0.10-0.20 m, in a Red Latosol (Oxisol, in order to select an indicator of soil physical quality and identify possible causes of pasture degradation. The geostatistical grid was installed to collect soil and plant data, with 121 sampling points, over an area of 2.56 ha. The linear correlation between FDM × DM% and FDM × BD2 was low, but highly significant. Spatial correlations varied inversely and positively, respectively. Except for DM% and BD, at both depths, the other attributes showed average to high variability, indicating a heterogeneous environment. Thus, geostatistics emerges as an important tool in understanding the interactions in pasture ecosystems, in order to minimize possible causes of degradation and indicate better alternatives for soil-plant-animal management. The decrease in FDM and increased BD1 are indicators of physical degradation (compaction of Red Latosol (Oxisol, particularly in the places with the highest concentration of animals and excessive trampling, in Cerrado conditions, in the municipality of Selvíria, Mato Grosso do Sul State, Brazil.

Evaluation of environmental monitoring thermoluminescent dosimeter locations

International Nuclear Information System (INIS)

Kinnison, R.

1992-12-01

Geostatistics, particularly kriging, has been used to assess the adequacy of the existing NTS thermoluminescent dosimeter network for determination of environmental exposure levels. (Kriging is a linear estimation method that results in contour plots of both the pattern of the estimated gamma radiation over the area of measurements and also of the standard deviations of the estimated exposure levels.) Even though the network was not designed as an environmental monitoring network, ft adequately serves this function in the region of Pahute and Rainier Mesas.. The Yucca Flat network is adequate only if a reasonable definition of environmental exposure levels is required; R is not adequate for environmental monitoring in Yucca Flat if a coefficient of variation of 10 percent or less is chosen as the criterion for network design. A revision of the Yucca Flat network design should be based on a square grid pattern with nodes 5000 feet (about one mile) apart, if a 10 percent coefficient of variation criterion is adopted. There were insufficient data for southern and western sections of the NTS to perform the geostatistical analysis. A very significant finding was that a single network design cannot be used for the entire NTS, because different areas have different variograms. Before any design can be finalized, the NTS management must specify the exposure unit area and coefficient of variation that are to be used as design criteria

Soil quality assessment using GIS-based chemometric approach and pollution indices: Nakhlak mining district, Central Iran.

Science.gov (United States)

Moore, Farid; Sheykhi, Vahideh; Salari, Mohammad; Bagheri, Adel

2016-04-01

This paper is a comprehensive assessment of the quality of soil in the Nakhlak mining district in Central Iran with special reference to potentially toxic metals. In this regard, an integrated approach involving geostatistical, correlation matrix, pollution indices, and chemical fractionation measurement is used to evaluate selected potentially toxic metals in soil samples. The fractionation of metals indicated a relatively high variability. Some metals (Mo, Ag, and Pb) showed important enrichment in the bioavailable fractions (i.e., exchangeable and carbonate), whereas the residual fraction mostly comprised Sb and Cr. The Cd, Zn, Co, Ni, Mo, Cu, and As were retained in Fe-Mn oxide and oxidizable fractions, suggesting that they may be released to the environment by changes in physicochemical conditions. The spatial variability patterns of 11 soil heavy metals (Ag, As, Cd, Co, Cr, Cu, Mo, Ni, Pb, Sb, and Zn) were identified and mapped. The results demonstrated that Ag, As, Cd, Mo, Cu, Pb, Sb, and Zn pollution are associated with mineralized veins and mining operations in this area. Further environmental monitoring and remedial actions are required for management of soil heavy metals in the study area. The present study not only enhanced our knowledge regarding soil pollution in the study area but also introduced a better technique to analyze pollution indices by multivariate geostatistical methods.

Use of element model to evaluate transmissibility reduction due to barriers

Energy Technology Data Exchange (ETDEWEB)

Svanes, T.; South, D.; Dronen, O.M. [Statoil, Bergen (Norway)

1997-08-01

Water breakthrough has been observed a year earlier than expected in the productive Oseberg Formation in the Veslefrikk Field. Production data revealed extensive water override, whereas the opposite situation was expected based on a homogeneous and coarse flow simulation model. A new model was developed to include geological heterogeneities using a simple upscaling method. The Oseberg Fm. consists of an upper homogeneous unit (zone 2) and a lower unit containing thin barriers of shale and calcite cemented sandstone (zone 1). The barrier content varies laterally. When barriers are distributed in a complex 3D pattern, they reduce the upscaled horizontal transmissibility more than what is obtained by multiplying the sand permeability by the net-to-gross ratio (N/G). However, the transmissibility reduction strongly depends on the spatial distribution of barriers and their geometry. Therefore, a fine scale element model was used to derive the average transmissibility reduction as a function of N/G for alternative geological descriptions of the barriers. A geo-statistical method called General Marked Point Process was used to generate the fine scale descriptions. This work has resulted in a simple upscaling routine for horizontal transmissibility, which represents an effective bridge between geological evaluation of uncertainties and fluid flow simulation. The method combines geo-statistical and deterministic modelling in an elegant manner, recognising that most often these methods complement one another.

Spatial and temporal distribution of weeds in no-tillage system

Directory of Open Access Journals (Sweden)

Eduardo L. Bottega

Full Text Available ABSTRACT Brazil is one of the largest grain producers in the world, due to its extensive arable land and favorable climate for the cultivation of any species. The production could be higher, but problems such as competition between crops and weeds reduces crop yields. This study aimed to analyze the spatial distribution of weeds, especially milkweed (Sonchus oleraceus, horse weed (Conyza spp. and ‘maria-mole’ (Senecio brasiliensis in an area under no-tillage system for two harvests. The work was carried out during the 2013/14 and 2014/15 crop years in an area of 22.5 ha, where soybean is grown in the summer and oat in the winter. The weeds were mapped using a sampling grid of 85 points. The number of plants present in 0.25 m2 was recorded at each sampling point. The data collected in the field composed the information bank and were used for geostatistical analysis and production of maps. Using geostatistical analysis, it was possible to map the horseweed infestation in the studied area for the 2013/14 season. The species ‘maria-mole’ and milkweed did not show spatial dependence. During the 2014/2015 season, because of the management carried out in the off season, none of the studied species showed spatial dependence.

Development of subsurface characterization method for decommissioning site remediation

Energy Technology Data Exchange (ETDEWEB)

Hong, Sang Bum; Nam, Jong Soo; Choi, Yong Suk; Seo, Bum Kyoung; Moon, Jei Kwon; Choi, Jong Won [KAERI, Daejeon (Korea, Republic of)

2016-05-15

In situ measurement of peak to valley method based on the ratio of counting rate between the full energy peak and Compton region was applied to identify the depth distribution of 137Cs. The In situ measurement and sampling results were applied to evaluate a residual radioactivity before and after remediation in decommissioning KRR site. Spatial analysis based on the Geostatistics method provides a reliable estimating the volume of contaminated soil with a graphical analysis, which was applied to the site characterization in the decommissioning KRR site. The in situ measurement and spatial analysis results for characterization of subsurface contamination are presented. The objective of a remedial action is to reduce risks to human health to acceptable levels by removing the source of contamination. Site characterization of the subsurface contamination is an important factor for planning and implementation of site remediation. Radiological survey and evaluation technology are required to ensure the reliability of the results, and the process must be easily applied during field measurements. In situ gamma-ray spectrometry is a powerful method for site characterization that can be used to identify the depth distribution and quantify radionuclides directly at the measurement site. The in situ measurement and Geostatistics method was applied to the site characterization for remediation and final status survey in decommissioning KRR site.

Modelling seasonal variations of natural radioactivity in soils: A case study in southern Italy

Science.gov (United States)

Guagliardi, Ilaria; Rovella, Natalia; Apollaro, Carmine; Bloise, Andrea; Rosa, Rosanna De; Scarciglia, Fabio; Buttafuoco, Gabriele

2016-12-01

The activity of natural radionuclides in soil has become an environmental concern for local public and national authorities because of the harmful effects of radiation exposure on human health. In this context, modelling and mapping the activity of natural radionuclides in soil is an important research topic. The study was aimed to model, in a spatial sense, the soil radioactivity in an urban and peri-urban soils area in southern Italy to analyse the seasonal influence on soil radioactivity. Measures of gamma radiation naturally emitted through the decay of radioactive isotopes (potassium, uranium and thorium) were analysed using a geostatistical approach to map the spatial distribution of soil radioactivity. The activity of three radionuclides was measured at 181 locations using a high-resolution ?-ray spectrometry. To take into account the influence of season, the measurements were carried out in summer and in winter. Activity data were analysed by using a geostatistical approach and zones of relatively high or low radioactivity were delineated. Among the main processes which influence natural radioactivity such as geology, geochemical, pedological, and ecological processes, results of this study showed a prominent control of radio-emission measurements by seasonal changes. Low natural radioactivity levels were measured in December associated with winter weather and moist soil conditions (due to high rainfall and low temperature), and higher activity values in July, when the soil was dry and no precipitations occurred.

Arsenic-Safe Aquifers in Coastal Bangladesh: AN Investigation with Ordinary Kriging Estimation

Science.gov (United States)

Hassan, M. M.; Ahamed, R.

2017-10-01

Spatial point pattern is one of the most suitable methods for analysing groundwater arsenic concentrations. Groundwater arsenic poisoning in Bangladesh has been one of the biggest environmental health disasters in recent times. About 85 million people are exposed to arsenic more than 50 μg/L in drinking water. The paper seeks to identify the existing suitable aquifers for arsenic-safe drinking water along with "spatial arsenic discontinuity" using GIS-based spatial geostatistical analysis in a small study site (12.69 km2) in the coastal belt of southwest Bangladesh (Dhopakhali union of Bagerhat district). The relevant spatial data were collected with Geographical Positioning Systems (GPS), arsenic data with field testing kits, tubewell attributes with observation and questionnaire survey. Geostatistics with kriging methods can design water quality monitoring in different aquifers with hydrochemical evaluation by spatial mapping. The paper presents the interpolation of the regional estimates of arsenic data for spatial discontinuity mapping with Ordinary Kriging (OK) method that overcomes the areal bias problem for administrative boundary. This paper also demonstrates the suitability of isopleth maps that is easier to read than choropleth maps. The OK method investigated that around 80 percent of the study site are contaminated following the Bangladesh Drinking Water Standards (BDWS) of 50 μg/L. The study identified a very few scattered "pockets" of arsenic-safe zone at the shallow aquifer.

Geological and geophysical investigations at Sierra del Medio massif - Argentine

International Nuclear Information System (INIS)

Perucca, J.C.; Llambias, E.; Puigdomenech, H.H.; Cebrelli, E.; Castro, C.E.; Grassi, I.; Salinas, L.I.

1987-01-01

Geological investigations were performed at Sierra del Medio (Chubut Province), a mountainous massif of about 25 km by 8 km of migmatic origin, which emerges from a depressed tectonic trench or graben called Pampa de Gastre. The most ancient rocks belong to biotitic and anphibolic schist that passed almost entirely to tonalitoid migmatites with a second process producing granitic rocks. Boreholes were drilled on the basis of conclusions from Landsat satellites imagery and aerial photographic sets, folowed by field work on geological, petrographic, geophysical and hydrogeological features at surface, structural interpretation supported by geostatistical computations. Two sets of boreholes were drilled to investigate subsurface rock behaviour al 300 m depth and 800 m depth respectively, beginning at peripheral places and ending at the central part or selected site. Basic purposes of boreholes were to define structural and petrographic features of the rock massif by a good comprehension of master joints and faulting distribution with its belts of alteration mylonitization or brecciation, mechanical properties of samples, chemical composition and varitions, petrographic facies and mineralogy. Boreholes provided data to investigate joints, faults and dikes as general discontinuities for hydraulic research like permeability or effective hydraulic conductivity, and their geostatistical modelling. Boreholes are also being prepared for geophysical logging from which logthermal ones have already been completed. (Author) [es

Modeling of surface dust concentration in snow cover at industrial area using neural networks and kriging

Science.gov (United States)

Sergeev, A. P.; Tarasov, D. A.; Buevich, A. G.; Shichkin, A. V.; Tyagunov, A. G.; Medvedev, A. N.

2017-06-01

Modeling of spatial distribution of pollutants in the urbanized territories is difficult, especially if there are multiple emission sources. When monitoring such territories, it is often impossible to arrange the necessary detailed sampling. Because of this, the usual methods of analysis and forecasting based on geostatistics are often less effective. Approaches based on artificial neural networks (ANNs) demonstrate the best results under these circumstances. This study compares two models based on ANNs, which are multilayer perceptron (MLP) and generalized regression neural networks (GRNNs) with the base geostatistical method - kriging. Models of the spatial dust distribution in the snow cover around the existing copper quarry and in the area of emissions of a nickel factory were created. To assess the effectiveness of the models three indices were used: the mean absolute error (MAE), the root-mean-square error (RMSE), and the relative root-mean-square error (RRMSE). Taking into account all indices the model of GRNN proved to be the most accurate which included coordinates of the sampling points and the distance to the likely emission source as input parameters for the modeling. Maps of spatial dust distribution in the snow cover were created in the study area. It has been shown that the models based on ANNs were more accurate than the kriging, particularly in the context of a limited data set.

Neural network stochastic simulation applied for quantifying uncertainties

Directory of Open Access Journals (Sweden)

N Foudil-Bey

2016-09-01

Full Text Available Generally the geostatistical simulation methods are used to generate several realizations of physical properties in the sub-surface, these methods are based on the variogram analysis and limited to measures correlation between variables at two locations only. In this paper, we propose a simulation of properties based on supervised Neural network training at the existing drilling data set. The major advantage is that this method does not require a preliminary geostatistical study and takes into account several points. As a result, the geological information and the diverse geophysical data can be combined easily. To do this, we used a neural network with multi-layer perceptron architecture like feed-forward, then we used the back-propagation algorithm with conjugate gradient technique to minimize the error of the network output. The learning process can create links between different variables, this relationship can be used for interpolation of the properties on the one hand, or to generate several possible distribution of physical properties on the other hand, changing at each time and a random value of the input neurons, which was kept constant until the period of learning. This method was tested on real data to simulate multiple realizations of the density and the magnetic susceptibility in three-dimensions at the mining camp of Val d'Or, Québec (Canada.

Quantification of centimeter-scale spatial variation in PAH, glucose and benzoic acid mineralization and soil organic matter in road-side soil

Energy Technology Data Exchange (ETDEWEB)

Hybholt, Trine K.; Aamand, Jens [Department of Geochemistry, Geological Survey of Denmark and Greenland (GEUS), Oster Voldgade 10, DK-1350 Copenhagen K (Denmark); Johnsen, Anders R., E-mail: arj@geus.dk [Department of Geochemistry, Geological Survey of Denmark and Greenland (GEUS), Oster Voldgade 10, DK-1350 Copenhagen K (Denmark)

2011-05-15

The aim of the study was to determine centimeter-scale spatial variation in mineralization potential in diffusely polluted soil. To this end we employed a 96-well microplate method to measure the mineralization of {sup 14}C-labeled organic compounds in deep-well microplates and thereby compile mineralization curves for 348 soil samples of 0.2-cm{sup 3}. Centimeter-scale spatial variation in organic matter and the mineralization of glucose, benzoic acid, and PAHs (phenanthrene and pyrene) was determined for urban road-side soil sampled as arrays (7 x 11 cm) of 96 subsamples. The spatial variation in mineralization was visualized by means of 2-D contour maps and quantified by means of semivariograms. The geostatistical analysis showed that the easily degradable compounds (glucose and benzoic acid) exhibited little spatial variation in mineralization potential, whereas the mineralization was highly heterogeneous for the PAH compounds that require specialized degraders. The spatial heterogeneity should be taken into account when estimating natural attenuation rates. - Highlights: > Geostatistics were applied at the centimeter scale. > Glucose and benzoic acid mineralization showed little spatial variation. > PAH mineralization was highly variable at the sub-centimeter scale. > High spatial heterogeneity may be caused by low functional redundancy. - This study supports the hypothesis that specialized xenobiotic degraders may show high spatial heterogeneity in soil due to low functional redundancy.

Characterizing the estuarine riverbed using acoustic technique

Digital Repository Service at National Institute of Oceanography (India)

Chakraborty, B.; Menezes, A.A.A.; Muthukumar, K.; Fernandes, W.A.; Saran, A.K.; Mehra, P.

carried out using geostatistics and non- linear techniques. These methods have been applied to bathymetry and backscatter data collected using multi-beam echo sounder (EM 1002, M/s Kongsberg) operating at a frequency of 95 kHz, installed onboard..., May 04 & May 16 of 2007 respectively. The EM1002 multi-beam echo-sounding system possesses 111 preformed beams i.e., recording 111 depth values in a single ping. In addition to the depth, the system also records quantitative backscatter data...

Determination of reliability of express forecasting evaluation of radiometric enriching ability of non-ferrous ores

International Nuclear Information System (INIS)

Kirpishchikov, S.P.

1991-01-01

Use of the data of nuclear physical methods of sampling and logging enables to improve reliability of evaluation of radiometric enriching ability of ores, as well as to evaluate quantitatively this reliability. This problem may be solved by using some concepts of geostatistics. The presented results enable to conclude, that the data of nuclear-physical methods of sampling and logging can provide high reliability of evaluation of radiometric enriching ability of non-ferrous ores and their geometrization by technological types

Potential ecological risk assessment and predicting zinc accumulation in soils

OpenAIRE

Baran, Agnieszka; Wieczorek, Jerzy; Mazurek, Ryszard; Urbański, Krzysztof; Klimkowicz-Pawlas, Agnieszka

2017-01-01

The aims of this study were to investigate zinc content in the studied soils; evaluate the efficiency of geostatistics in presenting spatial variability of zinc in the soils; assess bioavailable forms of zinc in the soils and to assess soil–zinc binding ability; and to estimate the potential ecological risk of zinc in soils. The study was conducted in southern Poland, in the Malopolska Province. This area is characterized by a great diversity of geological structures and types of land use and...

Characterization uncertainty and its effects on models and performance

International Nuclear Information System (INIS)

Rautman, C.A.; Treadway, A.H.

1991-01-01

Geostatistical simulation is being used to develop multiple geologic models of rock properties at the proposed Yucca Mountain repository site. Because each replicate model contains the same known information, and is thus essentially indistinguishable statistically from others, the differences between models may be thought of as representing the uncertainty in the site description. The variability among performance measures, such as ground water travel time, calculated using these replicate models therefore quantifies the uncertainty in performance that arises from uncertainty in site characterization

Rock mechanics. Proceedings of the 33rd U.S. symposium

International Nuclear Information System (INIS)

Tillerson, J.R.; Wawersik, W.R.

1992-01-01

After giving abstracts of the award winning papers for 1991 and the keynote lecture, papers are presented under the following headings: origin of stresses in the lithosphere; fault mechanics; rock mass monitoring; subsidence and ground motions; blasting; reservoir completion and stimulation; underground storage and sealing; design and supports; fluid and contaminant transport; numerical methods; constitutive modelling and strain localization; nonlinear dynamic systems; geostatistics and reliability; fracture mechanics; physical rock properties; experimental methods; geotechnical design methodology - workshop; and induced seismicity - workshop. Six papers have been abstracted separately

Cartographie numérique en géologie de surface. Application aux altérites à silex de l'Ouest du bassin de Paris

OpenAIRE

Quesnel , Florence

1997-01-01

Thèse publiée. Document du BRGM, 263, 268 p et 160 p d'annexes.; The compilation of data from 50 geological maps (1:50,000 scale), 2,500 boreholes and their combination in the creation of thematic maps using G.I.S. software, has enabled the anatomy of the flint alterites (termed RS, residu à silex or "clay-with-flints") of the western Paris Basin to be established. Geostatistical treatment of the thickness variable of superficial plateau formations (RS + plateau loams + Cenozoic residuals) an...

Validation study of safety assessment model for radioactive waste disposal

Energy Technology Data Exchange (ETDEWEB)

Munakata, Masahiro; Takeda, Seiji; Kimura, Hideo [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

2001-12-01

The JAERI-AECL collaboration research program has been conducted to validate a groundwater flow and radionuclide transport models for safety assessment. JAERI have developed a geostatistical model for radionuclide transport through a heterogeneous geological media and verify using experimental results of field tracer tests. The simulated tracer plumes explain favorably the experimental tracer plumes. A regional groundwater flow and transport model using site-scale parameter obtained from tracer tests have been verified by comparing simulation results with observation ones of natural environmental tracer. (author)

Comparison of the spatial patterns of schistosomiasis in Zimbabwe at two points in time, spaced twenty-nine years apart

DEFF Research Database (Denmark)

Pedersen, Ulrik Bo; Karagiannis-Voules, Dimitrios-Alexios; Midzi, Nicholas

2017-01-01

Temperature, precipitation and humidity are known to be important factors for the development of schistosome parasites as well as their intermediate snail hosts. Climate therefore plays an important role in determining the geographical distribution of schistosomiasis and it is expected that climate......, a Bayesian geostatistical model was fitted to a range of climatic, environmental and other potential risk factors to identify significant predictors that could help us to obtain spatially explicit schistosomiasis risk estimates for Zimbabwe. The observed general downward trend in schistosomiasis prevalence...

Solving inverse problems through a smooth formulation of multiple-point geostatistics

DEFF Research Database (Denmark)

Melnikova, Yulia

be inferred, for instance, from a conceptual geological model termed a training image.The main motivation for this study was the challenge posed by history matching, an inverse problem aimed at estimating rock properties from production data. We addressed two main difficulties of the history matching problem...... corresponding inverse problems. However, noise in data, non-linear relationships and sparse observations impede creation of realistic reservoir models. Including complex a priori information on reservoir parameters facilitates the process of obtaining acceptable solutions. Such a priori knowledge may...... strategies including both theoretical motivation and practical aspects of implementation. Finally, it is complemented by six research papers submitted, reviewed and/or published in the period 2010 - 2013....

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...... be compensated by model parameters, e.g. when hydraulic heads are considered. However, geological structure is the primary source of uncertainty with respect to simulations of groundwater age and capture zone. Operational MPS based software has been on stage for just around ten years; yet, issues regarding...... geological structures of these three sites provided appropriate conditions for testing the methods. Our study documented that MPS is an efficient approach for simulating geological heterogeneity, especially for non-stationary system. The high resolution of geophysical data such as SkyTEM is valuable both...

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

NARCIS (Netherlands)

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

2011-01-01

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

Spatio-temporal patterns of Cu contamination in mosses using geostatistical estimation

International Nuclear Information System (INIS)

Martins, Anabela; Figueira, Rui; Sousa, António Jorge; Sérgio, Cecília

2012-01-01

Several recent studies have reported temporal trends in metal contamination in mosses, but such assessments did not evaluate uncertainty in temporal changes, therefore providing weak statistical support for time comparisons. Furthermore, levels of contaminants in the environment change in both space and time, requiring space-time modelling methods for map estimation. We propose an indicator of spatial and temporal variation based on space-time estimation by indicator kriging, where uncertainty at each location is estimated from the local distribution function, thereby calculating variability intervals for comparison between several biomonitoring dates. This approach was exemplified using copper concentrations in mosses from four Portuguese surveys (1992, 1997, 2002 and 2006). Using this approach, we identified a general decrease in copper contamination, but spatial patterns were not uniform, and from the uncertainty intervals, changes could not be considered significant in the majority of the study area. - Highlights: ► We estimated copper contamination in mosses by spatio-temporal kriging between 1992 and 2006. ► We determined local distribution functions to define variation intervals at each location. ► Significance of temporal changes is assessed using an indicator based on uncertainty interval. ► There is general decrease in copper contamination, but spatial patterns are not uniform. - The contamination of copper in mosses was estimated by spatio-temporal kriging, with determination of uncertainty classes in the temporal variation.

Multivariate geostatistical application for climate characterization of Minas Gerais State, Brazil

Science.gov (United States)

de Carvalho, Luiz G.; de Carvalho Alves, Marcelo; de Oliveira, Marcelo S.; Vianello, Rubens L.; Sediyama, Gilberto C.; de Carvalho, Luis M. T.

2010-11-01

The objective of the present study was to assess for Minas Gerais the cokriging methodology, in order to characterize the spatial variability of Thornthwaite annual moisture index, annual rainfall, and average annual air temperature, based on geographical coordinates, altitude, latitude, and longitude. The climatic element data referred to 39 INMET climatic stations located in the state of Minas Gerais and in nearby areas and the covariables altitude, latitude, and longitude to the SRTM digital elevation model. Spatial dependence of data was observed through spherical cross semivariograms and cross covariance models. Box-Cox and log transformation were applied to the positive variables. In these situations, kriged predictions were back-transformed and returned to the same scale as the original data. Trend was removed using global polynomial interpolation. Universal simple cokriging best characterized the climate variables without tendentiousness and with high accuracy and precision when compared to simple cokriging. Considering the satisfactory implementation of universal simple cokriging for the monitoring of climatic elements, this methodology presents enormous potential for the characterization of climate change impact in Minas Gerais state.