Earthquake number forecasts testing

Science.gov (United States)

Kagan, Yan Y.

2017-10-01

and kurtosis both tend to zero for large earthquake rates: for the Gaussian law, these values are identically zero. A calculation of the NBD skewness and kurtosis levels based on the values of the first two statistical moments of the distribution, shows rapid increase of these upper moments levels. However, the observed catalogue values of skewness and kurtosis are rising even faster. This means that for small time intervals, the earthquake number distribution is even more heavy-tailed than the NBD predicts. Therefore for small time intervals, we propose using empirical number distributions appropriately smoothed for testing forecasted earthquake numbers.

1/f and the Earthquake Problem: Scaling constraints that facilitate operational earthquake forecasting

Science.gov (United States)

yoder, M. R.; Rundle, J. B.; Turcotte, D. L.

2012-12-01

The difficulty of forecasting earthquakes can fundamentally be attributed to the self-similar, or "1/f", nature of seismic sequences. Specifically, the rate of occurrence of earthquakes is inversely proportional to their magnitude m, or more accurately to their scalar moment M. With respect to this "1/f problem," it can be argued that catalog selection (or equivalently, determining catalog constraints) constitutes the most significant challenge to seismicity based earthquake forecasting. Here, we address and introduce a potential solution to this most daunting problem. Specifically, we introduce a framework to constrain, or partition, an earthquake catalog (a study region) in order to resolve local seismicity. In particular, we combine Gutenberg-Richter (GR), rupture length, and Omori scaling with various empirical measurements to relate the size (spatial and temporal extents) of a study area (or bins within a study area) to the local earthquake magnitude potential - the magnitude of earthquake the region is expected to experience. From this, we introduce a new type of time dependent hazard map for which the tuning parameter space is nearly fully constrained. In a similar fashion, by combining various scaling relations and also by incorporating finite extents (rupture length, area, and duration) as constraints, we develop a method to estimate the Omori (temporal) and spatial aftershock decay parameters as a function of the parent earthquake's magnitude m. From this formulation, we develop an ETAS type model that overcomes many point-source limitations of contemporary ETAS. These models demonstrate promise with respect to earthquake forecasting applications. Moreover, the methods employed suggest a general framework whereby earthquake and other complex-system, 1/f type, problems can be constrained from scaling relations and finite extents.; Record-breaking hazard map of southern California, 2012-08-06. "Warm" colors indicate local acceleration (elevated hazard

Gambling scores for earthquake predictions and forecasts

Science.gov (United States)

Zhuang, Jiancang

2010-04-01

This paper presents a new method, namely the gambling score, for scoring the performance earthquake forecasts or predictions. Unlike most other scoring procedures that require a regular scheme of forecast and treat each earthquake equally, regardless their magnitude, this new scoring method compensates the risk that the forecaster has taken. Starting with a certain number of reputation points, once a forecaster makes a prediction or forecast, he is assumed to have betted some points of his reputation. The reference model, which plays the role of the house, determines how many reputation points the forecaster can gain if he succeeds, according to a fair rule, and also takes away the reputation points betted by the forecaster if he loses. This method is also extended to the continuous case of point process models, where the reputation points betted by the forecaster become a continuous mass on the space-time-magnitude range of interest. We also calculate the upper bound of the gambling score when the true model is a renewal process, the stress release model or the ETAS model and when the reference model is the Poisson model.

Short-term volcano-tectonic earthquake forecasts based on a moving mean recurrence time algorithm: the El Hierro seismo-volcanic crisis experience

Science.gov (United States)

García, Alicia; De la Cruz-Reyna, Servando; Marrero, José M.; Ortiz, Ramón

2016-05-01

Under certain conditions, volcano-tectonic (VT) earthquakes may pose significant hazards to people living in or near active volcanic regions, especially on volcanic islands; however, hazard arising from VT activity caused by localized volcanic sources is rarely addressed in the literature. The evolution of VT earthquakes resulting from a magmatic intrusion shows some orderly behaviour that may allow the occurrence and magnitude of major events to be forecast. Thus governmental decision makers can be supplied with warnings of the increased probability of larger-magnitude earthquakes on the short-term timescale. We present here a methodology for forecasting the occurrence of large-magnitude VT events during volcanic crises; it is based on a mean recurrence time (MRT) algorithm that translates the Gutenberg-Richter distribution parameter fluctuations into time windows of increased probability of a major VT earthquake. The MRT forecasting algorithm was developed after observing a repetitive pattern in the seismic swarm episodes occurring between July and November 2011 at El Hierro (Canary Islands). From then on, this methodology has been applied to the consecutive seismic crises registered at El Hierro, achieving a high success rate in the real-time forecasting, within 10-day time windows, of volcano-tectonic earthquakes.

Retrospective evaluation of the five-year and ten-year CSEP-Italy earthquake forecasts

Directory of Open Access Journals (Sweden)

Stefan Wiemer

2010-11-01

Full Text Available On August 1, 2009, the global Collaboratory for the Study of Earthquake Predictability (CSEP launched a prospective and comparative earthquake predictability experiment in Italy. The goal of this CSEP-Italy experiment is to test earthquake occurrence hypotheses that have been formalized as probabilistic earthquake forecasts over temporal scales that range from days to years. In the first round of forecast submissions, members of the CSEP-Italy Working Group presented 18 five-year and ten-year earthquake forecasts to the European CSEP Testing Center at ETH Zurich. We have considered here the twelve time-independent earthquake forecasts among this set, and evaluated them with respect to past seismicity data from two Italian earthquake catalogs. We present the results of the tests that measure the consistencies of the forecasts according to past observations. As well as being an evaluation of the time-independent forecasts submitted, this exercise provides insight into a number of important issues in predictability experiments with regard to the specification of the forecasts, the performance of the tests, and the trade-off between robustness of results and experiment duration. We conclude with suggestions for the design of future earthquake predictability experiments.

Lessons of L'Aquila for Operational Earthquake Forecasting

Science.gov (United States)

Jordan, T. H.

2012-12-01

The L'Aquila earthquake of 6 Apr 2009 (magnitude 6.3) killed 309 people and left tens of thousands homeless. The mainshock was preceded by a vigorous seismic sequence that prompted informal earthquake predictions and evacuations. In an attempt to calm the population, the Italian Department of Civil Protection (DPC) convened its Commission on the Forecasting and Prevention of Major Risk (MRC) in L'Aquila on 31 March 2009 and issued statements about the hazard that were widely received as an "anti-alarm"; i.e., a deterministic prediction that there would not be a major earthquake. On October 23, 2012, a court in L'Aquila convicted the vice-director of DPC and six scientists and engineers who attended the MRC meeting on charges of criminal manslaughter, and it sentenced each to six years in prison. A few weeks after the L'Aquila disaster, the Italian government convened an International Commission on Earthquake Forecasting for Civil Protection (ICEF) with the mandate to assess the status of short-term forecasting methods and to recommend how they should be used in civil protection. The ICEF, which I chaired, issued its findings and recommendations on 2 Oct 2009 and published its final report, "Operational Earthquake Forecasting: Status of Knowledge and Guidelines for Implementation," in Aug 2011 (www.annalsofgeophysics.eu/index.php/annals/article/view/5350). As defined by the Commission, operational earthquake forecasting (OEF) involves two key activities: the continual updating of authoritative information about the future occurrence of potentially damaging earthquakes, and the officially sanctioned dissemination of this information to enhance earthquake preparedness in threatened communities. Among the main lessons of L'Aquila is the need to separate the role of science advisors, whose job is to provide objective information about natural hazards, from that of civil decision-makers who must weigh the benefits of protective actions against the costs of false alarms

A spatiotemporal clustering model for the Third Uniform California Earthquake Rupture Forecast (UCERF3‐ETAS): Toward an operational earthquake forecast

Science.gov (United States)

Field, Edward; Milner, Kevin R.; Hardebeck, Jeanne L.; Page, Morgan T.; van der Elst, Nicholas; Jordan, Thomas H.; Michael, Andrew J.; Shaw, Bruce E.; Werner, Maximillan J.

2017-01-01

We, the ongoing Working Group on California Earthquake Probabilities, present a spatiotemporal clustering model for the Third Uniform California Earthquake Rupture Forecast (UCERF3), with the goal being to represent aftershocks, induced seismicity, and otherwise triggered events as a potential basis for operational earthquake forecasting (OEF). Specifically, we add an epidemic‐type aftershock sequence (ETAS) component to the previously published time‐independent and long‐term time‐dependent forecasts. This combined model, referred to as UCERF3‐ETAS, collectively represents a relaxation of segmentation assumptions, the inclusion of multifault ruptures, an elastic‐rebound model for fault‐based ruptures, and a state‐of‐the‐art spatiotemporal clustering component. It also represents an attempt to merge fault‐based forecasts with statistical seismology models, such that information on fault proximity, activity rate, and time since last event are considered in OEF. We describe several unanticipated challenges that were encountered, including a need for elastic rebound and characteristic magnitude–frequency distributions (MFDs) on faults, both of which are required to get realistic triggering behavior. UCERF3‐ETAS produces synthetic catalogs of M≥2.5 events, conditioned on any prior M≥2.5 events that are input to the model. We evaluate results with respect to both long‐term (1000 year) simulations as well as for 10‐year time periods following a variety of hypothetical scenario mainshocks. Although the results are very plausible, they are not always consistent with the simple notion that triggering probabilities should be greater if a mainshock is located near a fault. Important factors include whether the MFD near faults includes a significant characteristic earthquake component, as well as whether large triggered events can nucleate from within the rupture zone of the mainshock. Because UCERF3‐ETAS has many sources of uncertainty, as

A way to synchronize models with seismic faults for earthquake forecasting

DEFF Research Database (Denmark)

González, Á.; Gómez, J.B.; Vázquez-Prada, M.

2006-01-01

Numerical models are starting to be used for determining the future behaviour of seismic faults and fault networks. Their final goal would be to forecast future large earthquakes. In order to use them for this task, it is necessary to synchronize each model with the current status of the actual....... Earthquakes, though, provide indirect but measurable clues of the stress and strain status in the lithosphere, which should be helpful for the synchronization of the models. The rupture area is one of the measurable parameters of earthquakes. Here we explore how it can be used to at least synchronize fault...... models between themselves and forecast synthetic earthquakes. Our purpose here is to forecast synthetic earthquakes in a simple but stochastic (random) fault model. By imposing the rupture area of the synthetic earthquakes of this model on other models, the latter become partially synchronized...

UCERF3: A new earthquake forecast for California's complex fault system

Science.gov (United States)

Field, Edward H.; ,

2015-01-01

With innovations, fresh data, and lessons learned from recent earthquakes, scientists have developed a new earthquake forecast model for California, a region under constant threat from potentially damaging events. The new model, referred to as the third Uniform California Earthquake Rupture Forecast, or "UCERF" (http://www.WGCEP.org/UCERF3), provides authoritative estimates of the magnitude, location, and likelihood of earthquake fault rupture throughout the state. Overall the results confirm previous findings, but with some significant changes because of model improvements. For example, compared to the previous forecast (Uniform California Earthquake Rupture Forecast 2), the likelihood of moderate-sized earthquakes (magnitude 6.5 to 7.5) is lower, whereas that of larger events is higher. This is because of the inclusion of multifault ruptures, where earthquakes are no longer confined to separate, individual faults, but can occasionally rupture multiple faults simultaneously. The public-safety implications of this and other model improvements depend on several factors, including site location and type of structure (for example, family dwelling compared to a long-span bridge). Building codes, earthquake insurance products, emergency plans, and other risk-mitigation efforts will be updated accordingly. This model also serves as a reminder that damaging earthquakes are inevitable for California. Fortunately, there are many simple steps residents can take to protect lives and property.

Prospective testing of Coulomb short-term earthquake forecasts

Science.gov (United States)

Jackson, D. D.; Kagan, Y. Y.; Schorlemmer, D.; Zechar, J. D.; Wang, Q.; Wong, K.

2009-12-01

Earthquake induced Coulomb stresses, whether static or dynamic, suddenly change the probability of future earthquakes. Models to estimate stress and the resulting seismicity changes could help to illuminate earthquake physics and guide appropriate precautionary response. But do these models have improved forecasting power compared to empirical statistical models? The best answer lies in prospective testing in which a fully specified model, with no subsequent parameter adjustments, is evaluated against future earthquakes. The Center of Study of Earthquake Predictability (CSEP) facilitates such prospective testing of earthquake forecasts, including several short term forecasts. Formulating Coulomb stress models for formal testing involves several practical problems, mostly shared with other short-term models. First, earthquake probabilities must be calculated after each “perpetrator” earthquake but before the triggered earthquakes, or “victims”. The time interval between a perpetrator and its victims may be very short, as characterized by the Omori law for aftershocks. CSEP evaluates short term models daily, and allows daily updates of the models. However, lots can happen in a day. An alternative is to test and update models on the occurrence of each earthquake over a certain magnitude. To make such updates rapidly enough and to qualify as prospective, earthquake focal mechanisms, slip distributions, stress patterns, and earthquake probabilities would have to be made by computer without human intervention. This scheme would be more appropriate for evaluating scientific ideas, but it may be less useful for practical applications than daily updates. Second, triggered earthquakes are imperfectly recorded following larger events because their seismic waves are buried in the coda of the earlier event. To solve this problem, testing methods need to allow for “censoring” of early aftershock data, and a quantitative model for detection threshold as a function of

A forecast experiment of earthquake activity in Japan under Collaboratory for the Study of Earthquake Predictability (CSEP)

Science.gov (United States)

Hirata, N.; Yokoi, S.; Nanjo, K. Z.; Tsuruoka, H.

2012-04-01

One major focus of the current Japanese earthquake prediction research program (2009-2013), which is now integrated with the research program for prediction of volcanic eruptions, is to move toward creating testable earthquake forecast models. For this purpose we started an experiment of forecasting earthquake activity in Japan under the framework of the Collaboratory for the Study of Earthquake Predictability (CSEP) through an international collaboration. We established the CSEP Testing Centre, an infrastructure to encourage researchers to develop testable models for Japan, and to conduct verifiable prospective tests of their model performance. We started the 1st earthquake forecast testing experiment in Japan within the CSEP framework. We use the earthquake catalogue maintained and provided by the Japan Meteorological Agency (JMA). The experiment consists of 12 categories, with 4 testing classes with different time spans (1 day, 3 months, 1 year, and 3 years) and 3 testing regions called "All Japan," "Mainland," and "Kanto." A total of 105 models were submitted, and are currently under the CSEP official suite of tests for evaluating the performance of forecasts. The experiments were completed for 92 rounds for 1-day, 6 rounds for 3-month, and 3 rounds for 1-year classes. For 1-day testing class all models passed all the CSEP's evaluation tests at more than 90% rounds. The results of the 3-month testing class also gave us new knowledge concerning statistical forecasting models. All models showed a good performance for magnitude forecasting. On the other hand, observation is hardly consistent in space distribution with most models when many earthquakes occurred at a spot. Now we prepare the 3-D forecasting experiment with a depth range of 0 to 100 km in Kanto region. The testing center is improving an evaluation system for 1-day class experiment to finish forecasting and testing results within one day. The special issue of 1st part titled Earthquake Forecast

Earthquake focal mechanism forecasting in Italy for PSHA purposes

Science.gov (United States)

Roselli, Pamela; Marzocchi, Warner; Mariucci, Maria Teresa; Montone, Paola

2018-01-01

In this paper, we put forward a procedure that aims to forecast focal mechanism of future earthquakes. One of the primary uses of such forecasts is in probabilistic seismic hazard analysis (PSHA); in fact, aiming at reducing the epistemic uncertainty, most of the newer ground motion prediction equations consider, besides the seismicity rates, the forecast of the focal mechanism of the next large earthquakes as input data. The data set used to this purpose is relative to focal mechanisms taken from the latest stress map release for Italy containing 392 well-constrained solutions of events, from 1908 to 2015, with Mw ≥ 4 and depths from 0 down to 40 km. The data set considers polarity focal mechanism solutions until to 1975 (23 events), whereas for 1976-2015, it takes into account only the Centroid Moment Tensor (CMT)-like earthquake focal solutions for data homogeneity. The forecasting model is rooted in the Total Weighted Moment Tensor concept that weighs information of past focal mechanisms evenly distributed in space, according to their distance from the spatial cells and magnitude. Specifically, for each cell of a regular 0.1° × 0.1° spatial grid, the model estimates the probability to observe a normal, reverse, or strike-slip fault plane solution for the next large earthquakes, the expected moment tensor and the related maximum horizontal stress orientation. These results will be available for the new PSHA model for Italy under development. Finally, to evaluate the reliability of the forecasts, we test them with an independent data set that consists of some of the strongest earthquakes with Mw ≥ 3.9 occurred during 2016 in different Italian tectonic provinces.

Statistical physics approach to earthquake occurrence and forecasting

Energy Technology Data Exchange (ETDEWEB)

Arcangelis, Lucilla de [Department of Industrial and Information Engineering, Second University of Naples, Aversa (CE) (Italy); Godano, Cataldo [Department of Mathematics and Physics, Second University of Naples, Caserta (Italy); Grasso, Jean Robert [ISTerre, IRD-CNRS-OSUG, University of Grenoble, Saint Martin d’Héres (France); Lippiello, Eugenio, E-mail: eugenio.lippiello@unina2.it [Department of Mathematics and Physics, Second University of Naples, Caserta (Italy)

2016-04-25

There is striking evidence that the dynamics of the Earth crust is controlled by a wide variety of mutually dependent mechanisms acting at different spatial and temporal scales. The interplay of these mechanisms produces instabilities in the stress field, leading to abrupt energy releases, i.e., earthquakes. As a consequence, the evolution towards instability before a single event is very difficult to monitor. On the other hand, collective behavior in stress transfer and relaxation within the Earth crust leads to emergent properties described by stable phenomenological laws for a population of many earthquakes in size, time and space domains. This observation has stimulated a statistical mechanics approach to earthquake occurrence, applying ideas and methods as scaling laws, universality, fractal dimension, renormalization group, to characterize the physics of earthquakes. In this review we first present a description of the phenomenological laws of earthquake occurrence which represent the frame of reference for a variety of statistical mechanical models, ranging from the spring-block to more complex fault models. Next, we discuss the problem of seismic forecasting in the general framework of stochastic processes, where seismic occurrence can be described as a branching process implementing space–time-energy correlations between earthquakes. In this context we show how correlations originate from dynamical scaling relations between time and energy, able to account for universality and provide a unifying description for the phenomenological power laws. Then we discuss how branching models can be implemented to forecast the temporal evolution of the earthquake occurrence probability and allow to discriminate among different physical mechanisms responsible for earthquake triggering. In particular, the forecasting problem will be presented in a rigorous mathematical framework, discussing the relevance of the processes acting at different temporal scales for

Varenna workshop report. Operational earthquake forecasting and decision making

Directory of Open Access Journals (Sweden)

Warner Marzocchi

2015-09-01

Full Text Available A workshop on Operational earthquake forecasting and decision making was convened in Varenna, Italy, on June 8-11, 2014, under the sponsorship of the EU FP 7 REAKT (Strategies and tools for Real-time EArthquake risK reducTion project, the Seismic Hazard Center at the Istituto Nazionale di Geofisica e Vulcanologia (INGV, and the Southern California Earthquake Center (SCEC. The main goal was to survey the interdisciplinary issues of operational earthquake forecasting (OEF, including the problems that OEF raises for decision making and risk communication. The workshop was attended by 64 researchers from universities, research centers, and governmental institutions in 11 countries. Participants and the workshop agenda are listed in the appendix.The workshop comprised six topical sessions structured around three main themes: the science of operational earthquake forecasting, decision making in a low-probability environment, and communicating hazard and risk. Each topic was introduced by a moderator and surveyed by a few invited speakers, who were then empaneled for an open discussion. The presentations were followed by poster sessions. During a wrap-up session on the last day, the reporters for each topical session summarized the main points that they had gleaned from the talks and open discussions. This report attempts to distill this workshop record into a brief overview of the workshop themes and to describe the range of opinions expressed during the discussions.

Earthquake Forecasting Methodology Catalogue - A collection and comparison of the state-of-the-art in earthquake forecasting and prediction methodologies

Science.gov (United States)

Schaefer, Andreas; Daniell, James; Wenzel, Friedemann

2015-04-01

Earthquake forecasting and prediction has been one of the key struggles of modern geosciences for the last few decades. A large number of approaches for various time periods have been developed for different locations around the world. A categorization and review of more than 20 of new and old methods was undertaken to develop a state-of-the-art catalogue in forecasting algorithms and methodologies. The different methods have been categorised into time-independent, time-dependent and hybrid methods, from which the last group represents methods where additional data than just historical earthquake statistics have been used. It is necessary to categorize in such a way between pure statistical approaches where historical earthquake data represents the only direct data source and also between algorithms which incorporate further information e.g. spatial data of fault distributions or which incorporate physical models like static triggering to indicate future earthquakes. Furthermore, the location of application has been taken into account to identify methods which can be applied e.g. in active tectonic regions like California or in less active continental regions. In general, most of the methods cover well-known high-seismicity regions like Italy, Japan or California. Many more elements have been reviewed, including the application of established theories and methods e.g. for the determination of the completeness magnitude or whether the modified Omori law was used or not. Target temporal scales are identified as well as the publication history. All these different aspects have been reviewed and catalogued to provide an easy-to-use tool for the development of earthquake forecasting algorithms and to get an overview in the state-of-the-art.

Retrospective Evaluation of the Five-Year and Ten-Year CSEP-Italy Earthquake Forecasts

OpenAIRE

Werner, M. J.; Zechar, J. D.; Marzocchi, W.; Wiemer, S.

2010-01-01

On 1 August 2009, the global Collaboratory for the Study of Earthquake Predictability (CSEP) launched a prospective and comparative earthquake predictability experiment in Italy. The goal of the CSEP-Italy experiment is to test earthquake occurrence hypotheses that have been formalized as probabilistic earthquake forecasts over temporal scales that range from days to years. In the first round of forecast submissions, members of the CSEP-Italy Working Group presented eighteen five-year and ten...

Stress-based aftershock forecasts made within 24h post mainshock: Expected north San Francisco Bay area seismicity changes after the 2014M=6.0 West Napa earthquake

Science.gov (United States)

Parsons, Thomas E.; Segou, Margaret; Sevilgen, Volkan; Milner, Kevin; Field, Edward; Toda, Shinji; Stein, Ross S.

2014-01-01

We calculate stress changes resulting from the M= 6.0 West Napa earthquake on north San Francisco Bay area faults. The earthquake ruptured within a series of long faults that pose significant hazard to the Bay area, and we are thus concerned with potential increases in the probability of a large earthquake through stress transfer. We conduct this exercise as a prospective test because the skill of stress-based aftershock forecasting methodology is inconclusive. We apply three methods: (1) generalized mapping of regional Coulomb stress change, (2) stress changes resolved on Uniform California Earthquake Rupture Forecast faults, and (3) a mapped rate/state aftershock forecast. All calculations were completed within 24 h after the main shock and were made without benefit of known aftershocks, which will be used to evaluative the prospective forecast. All methods suggest that we should expect heightened seismicity on parts of the southern Rodgers Creek, northern Hayward, and Green Valley faults.

Study on the Forecast of Ground Motion Parameters from Real Time Earthquake Information Based on Wave Form Data at the Front Site

OpenAIRE

萩原, 由訓; 源栄, 正人; 三辻, 和弥; 野畑, 有秀; Yoshinori, HAGIWARA; Masato, MOTOSAKA; Kazuya, MITSUJI; Arihide, NOBATA; (株)大林組 技術研究所; 東北大学大学院工学研究科; 山形大学地域教育文化学部生活総合学科生活環境科学コース; (株)大林組 技術研究所; Obayashi Corporation Technical Research Institute; Graduate School of Eng., Tohoku University; Faculty of Education, Art and Science, Yamagata University

2011-01-01

The Japan Meteorological Agency(JMA) provides Earthquake Early Warnings(EEW) for advanced users from August 1, 2006. Advanced EEW users can forecaste seismic ground motion (example: Seismic Intensity, Peak Ground Acceleration) from information of the earthquake in EEW. But there are limits to the accuracy and the earliness of the forecasting. This paper describes regression equation to decrease the error and to increase rapidity of the forecast of ground motion parameters from Real Time Earth...

Forecasting Induced Seismicity Using Saltwater Disposal Data and a Hydromechanical Earthquake Nucleation Model

Science.gov (United States)

Norbeck, J. H.; Rubinstein, J. L.

2017-12-01

The earthquake activity in Oklahoma and Kansas that began in 2008 reflects the most widespread instance of induced seismicity observed to date. In this work, we demonstrate that the basement fault stressing conditions that drive seismicity rate evolution are related directly to the operational history of 958 saltwater disposal wells completed in the Arbuckle aquifer. We developed a fluid pressurization model based on the assumption that pressure changes are dominated by reservoir compressibility effects. Using injection well data, we established a detailed description of the temporal and spatial variability in stressing conditions over the 21.5-year period from January 1995 through June 2017. With this stressing history, we applied a numerical model based on rate-and-state friction theory to generate seismicity rate forecasts across a broad range of spatial scales. The model replicated the onset of seismicity, the timing of the peak seismicity rate, and the reduction in seismicity following decreased disposal activity. The behavior of the induced earthquake sequence was consistent with the prediction from rate-and-state theory that the system evolves toward a steady seismicity rate depending on the ratio between the current and background stressing rates. Seismicity rate transients occurred over characteristic timescales inversely proportional to stressing rate. We found that our hydromechanical earthquake rate model outperformed observational and empirical forecast models for one-year forecast durations over the period 2008 through 2016.

Physics-based and statistical earthquake forecasting in a continental rift zone: the case study of Corinth Gulf (Greece)

Science.gov (United States)

Segou, Margarita

2016-01-01

I perform a retrospective forecast experiment in the most rapid extensive continental rift worldwide, the western Corinth Gulf (wCG, Greece), aiming to predict shallow seismicity (depth statistics, four physics-based (CRS) models, combining static stress change estimations and the rate-and-state laboratory law and one hybrid model. For the latter models, I incorporate the stress changes imparted from 31 earthquakes with magnitude M ≥ 4.5 at the extended area of wCG. Special attention is given on the 3-D representation of active faults, acting as potential receiver planes for the estimation of static stress changes. I use reference seismicity between 1990 and 1995, corresponding to the learning phase of physics-based models, and I evaluate the forecasts for six months following the 1995 M = 6.4 Aigio earthquake using log-likelihood performance metrics. For the ETAS realizations, I use seismic events with magnitude M ≥ 2.5 within daily update intervals to enhance their predictive power. For assessing the role of background seismicity, I implement a stochastic reconstruction (aka declustering) aiming to answer whether M > 4.5 earthquakes correspond to spontaneous events and identify, if possible, different triggering characteristics between aftershock sequences and swarm-type seismicity periods. I find that: (1) ETAS models outperform CRS models in most time intervals achieving very low rejection ratio RN = 6 per cent, when I test their efficiency to forecast the total number of events inside the study area, (2) the best rejection ratio for CRS models reaches RN = 17 per cent, when I use varying target depths and receiver plane geometry, (3) 75 per cent of the 1995 Aigio aftershocks that occurred within the first month can be explained by static stress changes, (4) highly variable performance on behalf of both statistical and physical models is suggested by large confidence intervals of information gain per earthquake and (5) generic ETAS models can adequately

LASSCI2009.2: layered earthquake rupture forecast model for central Italy, submitted to the CSEP project

Directory of Open Access Journals (Sweden)

Francesco Visini

2010-11-01

Full Text Available The Collaboratory for the Study of Earthquake Predictability (CSEP selected Italy as a testing region for probabilistic earthquake forecast models in October, 2008. The model we have submitted for the two medium-term forecast periods of 5 and 10 years (from 2009 is a time-dependent, geologically based earthquake rupture forecast that is defined for central Italy only (11-15˚ E; 41-45˚ N. The model took into account three separate layers of seismogenic sources: background seismicity; seismotectonic provinces; and individual faults that can produce major earthquakes (seismogenic boxes. For CSEP testing purposes, the background seismicity layer covered a range of magnitudes from 5.0 to 5.3 and the seismicity rates were obtained by truncated Gutenberg-Richter relationships for cells centered on the CSEP grid. Then the seismotectonic provinces layer returned the expected rates of medium-to-large earthquakes following a traditional Cornell-type approach. Finally, for the seismogenic boxes layer, the rates were based on the geometry and kinematics of the faults that different earthquake recurrence models have been assigned to, ranging from pure Gutenberg-Richter behavior to characteristic events, with the intermediate behavior named as the hybrid model. The results for different magnitude ranges highlight the contribution of each of the three layers to the total computation. The expected rates for M >6.0 on April 1, 2009 (thus computed before the L'Aquila, 2009, MW= 6.3 earthquake are of particular interest. They showed local maxima in the two seismogenic-box sources of Paganica and Sulmona, one of which was activated by the L'Aquila earthquake of April 6, 2009. Earthquake rates as of August 1, 2009, (now under test also showed a maximum close to the Sulmona source for MW ~6.5; significant seismicity rates (10-4 to 10-3 in 5 years for destructive events (magnitude up to 7.0 were located in other individual sources identified as being capable of such

2017 One‐year seismic‐hazard forecast for the central and eastern United States from induced and natural earthquakes

Science.gov (United States)

Petersen, Mark D.; Mueller, Charles; Moschetti, Morgan P.; Hoover, Susan M.; Shumway, Allison; McNamara, Daniel E.; Williams, Robert; Llenos, Andrea L.; Ellsworth, William L.; Rubinstein, Justin L.; McGarr, Arthur F.; Rukstales, Kenneth S.

2017-01-01

We produce a one‐year 2017 seismic‐hazard forecast for the central and eastern United States from induced and natural earthquakes that updates the 2016 one‐year forecast; this map is intended to provide information to the public and to facilitate the development of induced seismicity forecasting models, methods, and data. The 2017 hazard model applies the same methodology and input logic tree as the 2016 forecast, but with an updated earthquake catalog. We also evaluate the 2016 seismic‐hazard forecast to improve future assessments. The 2016 forecast indicated high seismic hazard (greater than 1% probability of potentially damaging ground shaking in one year) in five focus areas: Oklahoma–Kansas, the Raton basin (Colorado/New Mexico border), north Texas, north Arkansas, and the New Madrid Seismic Zone. During 2016, several damaging induced earthquakes occurred in Oklahoma within the highest hazard region of the 2016 forecast; all of the 21 moment magnitude (M) ≥4 and 3 M≥5 earthquakes occurred within the highest hazard area in the 2016 forecast. Outside the Oklahoma–Kansas focus area, two earthquakes with M≥4 occurred near Trinidad, Colorado (in the Raton basin focus area), but no earthquakes with M≥2.7 were observed in the north Texas or north Arkansas focus areas. Several observations of damaging ground‐shaking levels were also recorded in the highest hazard region of Oklahoma. The 2017 forecasted seismic rates are lower in regions of induced activity due to lower rates of earthquakes in 2016 compared with 2015, which may be related to decreased wastewater injection caused by regulatory actions or by a decrease in unconventional oil and gas production. Nevertheless, the 2017 forecasted hazard is still significantly elevated in Oklahoma compared to the hazard calculated from seismicity before 2009.

Spatial organization of foreshocks as a tool to forecast large earthquakes.

Science.gov (United States)

Lippiello, E; Marzocchi, W; de Arcangelis, L; Godano, C

2012-01-01

An increase in the number of smaller magnitude events, retrospectively named foreshocks, is often observed before large earthquakes. We show that the linear density probability of earthquakes occurring before and after small or intermediate mainshocks displays a symmetrical behavior, indicating that the size of the area fractured during the mainshock is encoded in the foreshock spatial organization. This observation can be used to discriminate spatial clustering due to foreshocks from the one induced by aftershocks and is implemented in an alarm-based model to forecast m > 6 earthquakes. A retrospective study of the last 19 years Southern California catalog shows that the daily occurrence probability presents isolated peaks closely located in time and space to the epicenters of five of the six m > 6 earthquakes. We find daily probabilities as high as 25% (in cells of size 0.04 × 0.04deg(2)), with significant probability gains with respect to standard models.

Forecasting of future earthquakes in the northeast region of India considering energy released concept

Science.gov (United States)

Zarola, Amit; Sil, Arjun

2018-04-01

This study presents the forecasting of time and magnitude size of the next earthquake in the northeast India, using four probability distribution models (Gamma, Lognormal, Weibull and Log-logistic) considering updated earthquake catalog of magnitude Mw ≥ 6.0 that occurred from year 1737-2015 in the study area. On the basis of past seismicity of the region, two types of conditional probabilities have been estimated using their best fit model and respective model parameters. The first conditional probability is the probability of seismic energy (e × 1020 ergs), which is expected to release in the future earthquake, exceeding a certain level of seismic energy (E × 1020 ergs). And the second conditional probability is the probability of seismic energy (a × 1020 ergs/year), which is expected to release per year, exceeding a certain level of seismic energy per year (A × 1020 ergs/year). The logarithm likelihood functions (ln L) were also estimated for all four probability distribution models. A higher value of ln L suggests a better model and a lower value shows a worse model. The time of the future earthquake is forecasted by dividing the total seismic energy expected to release in the future earthquake with the total seismic energy expected to release per year. The epicentre of recently occurred 4 January 2016 Manipur earthquake (M 6.7), 13 April 2016 Myanmar earthquake (M 6.9) and the 24 August 2016 Myanmar earthquake (M 6.8) are located in zone Z.12, zone Z.16 and zone Z.15, respectively and that are the identified seismic source zones in the study area which show that the proposed techniques and models yield good forecasting accuracy.

Risk Management in Earthquakes, Financial Markets, and the Game of 21: The role of Forecasting, Nowcasting, and Timecasting

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Rundle, J. B.

2017-12-01

Earthquakes and financial markets share surprising similarities [1]. For example, the well-known VIX index, which by definition is the implied volatility of the Standard and Poors 500 index, behaves in very similar quantitative fashion to time series for earthquake rates. Both display sudden increases at the time of an earthquake or an announcement of the US Federal Reserve Open Market Committee [2], and both decay as an inverse power of time. Both can be regarded as examples of first order phase transitions [1], and display fractal and scaling behavior associated with critical transitions, such as power-law magnitude-frequency relations in the tails of the distributions. Early quantitative investors such as Edward Thorpe and John Kelly invented novel methods to mitigate or manage risk in games of chance such as blackjack, and in markets using hedging techniques that are still in widespread use today. The basic idea is the concept of proportional betting, where the gambler/investor bets a fraction of the bankroll whose size is determined by the "edge" or inside knowledge of the real (and changing) odds. For earthquake systems, the "edge" over nature can only exist in the form of a forecast (probability of a future earthquake); a nowcast (knowledge of the current state of an earthquake fault system); or a timecast (statistical estimate of the waiting time until the next major earthquake). In our terminology, a forecast is a model, while the nowcast and timecast are analysis methods using observed data only (no model). We also focus on defined geographic areas rather than on faults, thereby eliminating the need to consider specific fault data or fault interactions. Data used are online earthquake catalogs, generally since 1980. Forecasts are based on the Weibull (1952) probability law, and only a handful of parameters are needed. These methods allow the development of real time hazard and risk estimation using cloud-based technologies, and permit the application of

Aftershock Forecasting: Recent Developments and Lessons from the 2016 M5.8 Pawnee, Oklahoma, Earthquake

Science.gov (United States)

Michael, A. J.; Field, E. H.; Hardebeck, J.; Llenos, A. L.; Milner, K. R.; Page, M. T.; Perry, S. C.; van der Elst, N.; Wein, A. M.

2016-12-01

After the Mw 5.8 Pawnee, Oklahoma, earthquake of September 3, 2016 the USGS issued a series of aftershock forecasts for the next month and year. These forecasts were aimed at the emergency response community, those making decisions about well operations in the affected region, and the general public. The forecasts were generated manually using methods planned for automatically released Operational Aftershock Forecasts. The underlying method is from Reasenberg and Jones (Science, 1989) with improvements recently published in Page et al. (BSSA, 2016), implemented in a JAVA Graphical User Interface and presented in a template that is under development. The methodological improvements include initial models based on the tectonic regime as defined by Garcia et al. (BSSA, 2012) and the inclusion of both uncertainty in the clustering parameters and natural random variability. We did not utilize the time-dependent magnitude of completeness model from Page et al. because it applies only to teleseismic events recorded by NEIC. The parameters for Garcia's Generic Active Continental Region underestimated the modified-Omori decay parameter and underestimated the aftershock rate by a factor of 2. And the sequence following the Mw 5.7 Prague, Oklahoma, earthquake of November 6, 2011 was about 3 to 4 times more productive than the Pawnee sequence. The high productivity for these potentially induced sequences is consistent with an increase in productivity in Oklahoma since 2009 (Llenos and Michael, BSSA, 2013) and makes a general tectonic model inapplicable to sequences in this region. Soon after the mainshock occurred, the forecasts relied on the sequence specific parameters. After one month, the Omori decay parameter p is less than one, implying a very long-lived sequence. However, the decay parameter is known to be biased low at early times due to secondary aftershock triggering, and the p-value determined early in the sequence may be inaccurate for long-term forecasting.

Earthquakes and forecast reliability: thermoactivation and mesomechanics of the focal zone

Science.gov (United States)

Kalinnikov, I. I.; Manukin, A. B.; Matyunin, V. P.

2017-06-01

According to our data, the involvement of the fundamental laws of physics, in particular, consideration of an earthquake as a particular macroprocess with a peak together with the thermofluctuational activation of mechanical stresses in some environments, makes it possible to move beyond the traditional idea of the issue of earthquake prediction. Many formal parameters of statistical processing of the geophysical data can be provided with a physical sense related to the mesomechanics of structural changes in a stressed solid body. Measures for improving the efficiency of observations and their mathematical processing to solve the forecasting issues have been specified.

Earthquake forecasting studies using radon time series data in Taiwan

Science.gov (United States)

Walia, Vivek; Kumar, Arvind; Fu, Ching-Chou; Lin, Shih-Jung; Chou, Kuang-Wu; Wen, Kuo-Liang; Chen, Cheng-Hong

2017-04-01

For few decades, growing number of studies have shown usefulness of data in the field of seismogeochemistry interpreted as geochemical precursory signals for impending earthquakes and radon is idendified to be as one of the most reliable geochemical precursor. Radon is recognized as short-term precursor and is being monitored in many countries. This study is aimed at developing an effective earthquake forecasting system by inspecting long term radon time series data. The data is obtained from a network of radon monitoring stations eastblished along different faults of Taiwan. The continuous time series radon data for earthquake studies have been recorded and some significant variations associated with strong earthquakes have been observed. The data is also examined to evaluate earthquake precursory signals against environmental factors. An automated real-time database operating system has been developed recently to improve the data processing for earthquake precursory studies. In addition, the study is aimed at the appraisal and filtrations of these environmental parameters, in order to create a real-time database that helps our earthquake precursory study. In recent years, automatic operating real-time database has been developed using R, an open source programming language, to carry out statistical computation on the data. To integrate our data with our working procedure, we use the popular and famous open source web application solution, AMP (Apache, MySQL, and PHP), creating a website that could effectively show and help us manage the real-time database.

Next-generation probabilistic seismicity forecasting

Energy Technology Data Exchange (ETDEWEB)

Hiemer, S.

2014-07-01

The development of probabilistic seismicity forecasts is one of the most important tasks of seismologists at present time. Such forecasts form the basis of probabilistic seismic hazard assessment, a widely used approach to generate ground motion exceedance maps. These hazard maps guide the development of building codes, and in the absence of the ability to deterministically predict earthquakes, good building and infrastructure planning is key to prevent catastrophes. Probabilistic seismicity forecasts are models that specify the occurrence rate of earthquakes as a function of space, time and magnitude. The models presented in this thesis are time-invariant mainshock occurrence models. Accordingly, the reliable estimation of the spatial and size distribution of seismicity are of crucial importance when constructing such probabilistic forecasts. Thereby we focus on data-driven approaches to infer these distributions, circumventing the need for arbitrarily chosen external parameters and subjective expert decisions. Kernel estimation has been shown to appropriately transform discrete earthquake locations into spatially continuous probability distributions. However, we show that neglecting the information from fault networks constitutes a considerable shortcoming and thus limits the skill of these current seismicity models. We present a novel earthquake rate forecast that applies the kernel-smoothing method to both past earthquake locations and slip rates on mapped crustal faults applied to Californian and European data. Our model is independent from biases caused by commonly used non-objective seismic zonations, which impose artificial borders of activity that are not expected in nature. Studying the spatial variability of the seismicity size distribution is of great importance. The b-value of the well-established empirical Gutenberg-Richter model forecasts the rates of hazard-relevant large earthquakes based on the observed rates of abundant small events. We propose a

Next-generation probabilistic seismicity forecasting

International Nuclear Information System (INIS)

Hiemer, S.

2014-01-01

The development of probabilistic seismicity forecasts is one of the most important tasks of seismologists at present time. Such forecasts form the basis of probabilistic seismic hazard assessment, a widely used approach to generate ground motion exceedance maps. These hazard maps guide the development of building codes, and in the absence of the ability to deterministically predict earthquakes, good building and infrastructure planning is key to prevent catastrophes. Probabilistic seismicity forecasts are models that specify the occurrence rate of earthquakes as a function of space, time and magnitude. The models presented in this thesis are time-invariant mainshock occurrence models. Accordingly, the reliable estimation of the spatial and size distribution of seismicity are of crucial importance when constructing such probabilistic forecasts. Thereby we focus on data-driven approaches to infer these distributions, circumventing the need for arbitrarily chosen external parameters and subjective expert decisions. Kernel estimation has been shown to appropriately transform discrete earthquake locations into spatially continuous probability distributions. However, we show that neglecting the information from fault networks constitutes a considerable shortcoming and thus limits the skill of these current seismicity models. We present a novel earthquake rate forecast that applies the kernel-smoothing method to both past earthquake locations and slip rates on mapped crustal faults applied to Californian and European data. Our model is independent from biases caused by commonly used non-objective seismic zonations, which impose artificial borders of activity that are not expected in nature. Studying the spatial variability of the seismicity size distribution is of great importance. The b-value of the well-established empirical Gutenberg-Richter model forecasts the rates of hazard-relevant large earthquakes based on the observed rates of abundant small events. We propose a

Earthquake forecasting and warning

Energy Technology Data Exchange (ETDEWEB)

Rikitake, T.

1983-01-01

This review briefly describes two other books on the same subject either written or partially written by Rikitake. In this book, the status of earthquake prediction efforts in Japan, China, the Soviet Union, and the United States are updated. An overview of some of the organizational, legal, and societal aspects of earthquake prediction in these countries is presented, and scientific findings of precursory phenomena are included. A summary of circumstances surrounding the 1975 Haicheng earthquake, the 1978 Tangshan earthquake, and the 1976 Songpan-Pingwu earthquake (all magnitudes = 7.0) in China and the 1978 Izu-Oshima earthquake in Japan is presented. This book fails to comprehensively summarize recent advances in earthquake prediction research.

Earthquake cycles and physical modeling of the process leading up to a large earthquake

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Ohnaka, Mitiyasu

2004-08-01

A thorough discussion is made on what the rational constitutive law for earthquake ruptures ought to be from the standpoint of the physics of rock friction and fracture on the basis of solid facts observed in the laboratory. From this standpoint, it is concluded that the constitutive law should be a slip-dependent law with parameters that may depend on slip rate or time. With the long-term goal of establishing a rational methodology of forecasting large earthquakes, the entire process of one cycle for a typical, large earthquake is modeled, and a comprehensive scenario that unifies individual models for intermediate-and short-term (immediate) forecasts is presented within the framework based on the slip-dependent constitutive law and the earthquake cycle model. The earthquake cycle includes the phase of accumulation of elastic strain energy with tectonic loading (phase II), and the phase of rupture nucleation at the critical stage where an adequate amount of the elastic strain energy has been stored (phase III). Phase II plays a critical role in physical modeling of intermediate-term forecasting, and phase III in physical modeling of short-term (immediate) forecasting. The seismogenic layer and individual faults therein are inhomogeneous, and some of the physical quantities inherent in earthquake ruptures exhibit scale-dependence. It is therefore critically important to incorporate the properties of inhomogeneity and physical scaling, in order to construct realistic, unified scenarios with predictive capability. The scenario presented may be significant and useful as a necessary first step for establishing the methodology for forecasting large earthquakes.

Volcanic Eruption Forecasts From Accelerating Rates of Drumbeat Long-Period Earthquakes

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Bell, Andrew F.; Naylor, Mark; Hernandez, Stephen; Main, Ian G.; Gaunt, H. Elizabeth; Mothes, Patricia; Ruiz, Mario

2018-02-01

Accelerating rates of quasiperiodic "drumbeat" long-period earthquakes (LPs) are commonly reported before eruptions at andesite and dacite volcanoes, and promise insights into the nature of fundamental preeruptive processes and improved eruption forecasts. Here we apply a new Bayesian Markov chain Monte Carlo gamma point process methodology to investigate an exceptionally well-developed sequence of drumbeat LPs preceding a recent large vulcanian explosion at Tungurahua volcano, Ecuador. For more than 24 hr, LP rates increased according to the inverse power law trend predicted by material failure theory, and with a retrospectively forecast failure time that agrees with the eruption onset within error. LPs resulted from repeated activation of a single characteristic source driven by accelerating loading, rather than a distributed failure process, showing that similar precursory trends can emerge from quite different underlying physics. Nevertheless, such sequences have clear potential for improving forecasts of eruptions at Tungurahua and analogous volcanoes.

Earthquake likelihood model testing

Science.gov (United States)

Schorlemmer, D.; Gerstenberger, M.C.; Wiemer, S.; Jackson, D.D.; Rhoades, D.A.

2007-01-01

INTRODUCTIONThe Regional Earthquake Likelihood Models (RELM) project aims to produce and evaluate alternate models of earthquake potential (probability per unit volume, magnitude, and time) for California. Based on differing assumptions, these models are produced to test the validity of their assumptions and to explore which models should be incorporated in seismic hazard and risk evaluation. Tests based on physical and geological criteria are useful but we focus on statistical methods using future earthquake catalog data only. We envision two evaluations: a test of consistency with observed data and a comparison of all pairs of models for relative consistency. Both tests are based on the likelihood method, and both are fully prospective (i.e., the models are not adjusted to fit the test data). To be tested, each model must assign a probability to any possible event within a specified region of space, time, and magnitude. For our tests the models must use a common format: earthquake rates in specified “bins” with location, magnitude, time, and focal mechanism limits.Seismology cannot yet deterministically predict individual earthquakes; however, it should seek the best possible models for forecasting earthquake occurrence. This paper describes the statistical rules of an experiment to examine and test earthquake forecasts. The primary purposes of the tests described below are to evaluate physical models for earthquakes, assure that source models used in seismic hazard and risk studies are consistent with earthquake data, and provide quantitative measures by which models can be assigned weights in a consensus model or be judged as suitable for particular regions.In this paper we develop a statistical method for testing earthquake likelihood models. A companion paper (Schorlemmer and Gerstenberger 2007, this issue) discusses the actual implementation of these tests in the framework of the RELM initiative.Statistical testing of hypotheses is a common task and a

On one approach to an earthquakes forecasting problems solution

International Nuclear Information System (INIS)

Khugaev, A.V.; Koblik, Yu.N.; Rakhmanov, T.T.

2007-01-01

The problem of earthquake forecasting is practically important one but it is extremely complex and so it does not solved yet. In the report the problem of data analysis obtained in measurements of radioactive gases emission (for example, radon, thoron, action) from the earth surface, data in magnetic fields anomalies measurement and their correlation in accordance of seismic activity is considered. In a general case the problem has an unlikely total solution in an analytic meaning due to it nonlinearity, multi-parametration and influence of random factors. It is suggested that useful solution could be found only at reasonable combination of empiric knowledge got at a long observations, its generalization and numerical simulation. In the base of the offered calculation method the correlation analysis between seismic activity and , for example, radioactive gases emission variations of earthquake signs one can present in form of two components, one of which is regular component, and the second one is irregular one. The key interest presents the analysis of irregular component reasoned by random factors. At problem solution of irregular component of the Earth magnetic fields determination which with high precise could measured with help of magnetic sensors is determined. At that time in the base of mathematical apparatus for analysis the approach for irregular component determination applied at determination of irregular component of galactic magnet field. Hear values of irregular component and field size in which they play considerable role are obtained. Besides, the work the approach allowing solving problem about complex surface oscillation with necessary precision for practical requirements is discussed

Probabilistic model to forecast earthquakes in the Zemmouri (Algeria) seismoactive area on the basis of moment magnitude scale distribution functions

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Baddari, Kamel; Makdeche, Said; Bellalem, Fouzi

2013-02-01

Based on the moment magnitude scale, a probabilistic model was developed to predict the occurrences of strong earthquakes in the seismoactive area of Zemmouri, Algeria. Firstly, the distributions of earthquake magnitudes M i were described using the distribution function F 0(m), which adjusts the magnitudes considered as independent random variables. Secondly, the obtained result, i.e., the distribution function F 0(m) of the variables M i was used to deduce the distribution functions G(x) and H(y) of the variables Y i = Log M 0,i and Z i = M 0,i , where (Y i)i and (Z i)i are independent. Thirdly, some forecast for moments of the future earthquakes in the studied area is given.

Spatial Evaluation and Verification of Earthquake Simulators

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Wilson, John Max; Yoder, Mark R.; Rundle, John B.; Turcotte, Donald L.; Schultz, Kasey W.

2017-06-01

In this paper, we address the problem of verifying earthquake simulators with observed data. Earthquake simulators are a class of computational simulations which attempt to mirror the topological complexity of fault systems on which earthquakes occur. In addition, the physics of friction and elastic interactions between fault elements are included in these simulations. Simulation parameters are adjusted so that natural earthquake sequences are matched in their scaling properties. Physically based earthquake simulators can generate many thousands of years of simulated seismicity, allowing for a robust capture of the statistical properties of large, damaging earthquakes that have long recurrence time scales. Verification of simulations against current observed earthquake seismicity is necessary, and following past simulator and forecast model verification methods, we approach the challenges in spatial forecast verification to simulators; namely, that simulator outputs are confined to the modeled faults, while observed earthquake epicenters often occur off of known faults. We present two methods for addressing this discrepancy: a simplistic approach whereby observed earthquakes are shifted to the nearest fault element and a smoothing method based on the power laws of the epidemic-type aftershock (ETAS) model, which distributes the seismicity of each simulated earthquake over the entire test region at a decaying rate with epicentral distance. To test these methods, a receiver operating characteristic plot was produced by comparing the rate maps to observed m>6.0 earthquakes in California since 1980. We found that the nearest-neighbor mapping produced poor forecasts, while the ETAS power-law method produced rate maps that agreed reasonably well with observations.

Physics-based forecasting of induced seismicity at Groningen gas field, the Netherlands

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Dempsey, David; Suckale, Jenny

2017-08-01

Earthquakes induced by natural gas extraction from the Groningen reservoir, the Netherlands, put local communities at risk. Responsible operation of a reservoir whose gas reserves are of strategic importance to the country requires understanding of the link between extraction and earthquakes. We synthesize observations and a model for Groningen seismicity to produce forecasts for felt seismicity (M > 2.5) in the period February 2017 to 2024. Our model accounts for poroelastic earthquake triggering and rupture on the 325 largest reservoir faults, using an ensemble approach to model unknown heterogeneity and replicate earthquake statistics. We calculate probability distributions for key model parameters using a Bayesian method that incorporates the earthquake observations with a nonhomogeneous Poisson process. Our analysis indicates that the Groningen reservoir was not critically stressed prior to the start of production. Epistemic uncertainty and aleatoric uncertainty are incorporated into forecasts for three different future extraction scenarios. The largest expected earthquake was similar for all scenarios, with a 5% likelihood of exceeding M 4.0.

Short-term and long-term earthquake occurrence models for Italy: ETES, ERS and LTST

Directory of Open Access Journals (Sweden)

Maura Murru

2010-11-01

Full Text Available This study describes three earthquake occurrence models as applied to the whole Italian territory, to assess the occurrence probabilities of future (M ≥5.0 earthquakes: two as short-term (24 hour models, and one as long-term (5 and 10 years. The first model for short-term forecasts is a purely stochastic epidemic type earthquake sequence (ETES model. The second short-term model is an epidemic rate-state (ERS forecast based on a model that is physically constrained by the application to the earthquake clustering of the Dieterich rate-state constitutive law. The third forecast is based on a long-term stress transfer (LTST model that considers the perturbations of earthquake probability for interacting faults by static Coulomb stress changes. These models have been submitted to the Collaboratory for the Study of Earthquake Predictability (CSEP for forecast testing for Italy (ETH-Zurich, and they were locked down to test their validity on real data in a future setting starting from August 1, 2009.

Smartphone-Based Earthquake and Tsunami Early Warning in Chile

Science.gov (United States)

Brooks, B. A.; Baez, J. C.; Ericksen, T.; Barrientos, S. E.; Minson, S. E.; Duncan, C.; Guillemot, C.; Smith, D.; Boese, M.; Cochran, E. S.; Murray, J. R.; Langbein, J. O.; Glennie, C. L.; Dueitt, J.; Parra, H.

2016-12-01

Many locations around the world face high seismic hazard, but do not have the resources required to establish traditional earthquake and tsunami warning systems (E/TEW) that utilize scientific grade seismological sensors. MEMs accelerometers and GPS chips embedded in, or added inexpensively to, smartphones are sensitive enough to provide robust E/TEW if they are deployed in sufficient numbers. We report on a pilot project in Chile, one of the most productive earthquake regions world-wide. There, magnitude 7.5+ earthquakes occurring roughly every 1.5 years and larger tsunamigenic events pose significant local and trans-Pacific hazard. The smartphone-based network described here is being deployed in parallel to the build-out of a scientific-grade network for E/TEW. Our sensor package comprises a smartphone with internal MEMS and an external GPS chipset that provides satellite-based augmented positioning and phase-smoothing. Each station is independent of local infrastructure, they are solar-powered and rely on cellular SIM cards for communications. An Android app performs initial onboard processing and transmits both accelerometer and GPS data to a server employing the FinDer-BEFORES algorithm to detect earthquakes, producing an acceleration-based line source model for smaller magnitude earthquakes or a joint seismic-geodetic finite-fault distributed slip model for sufficiently large magnitude earthquakes. Either source model provides accurate ground shaking forecasts, while distributed slip models for larger offshore earthquakes can be used to infer seafloor deformation for local tsunami warning. The network will comprise 50 stations by Sept. 2016 and 100 stations by Dec. 2016. Since Nov. 2015, batch processing has detected, located, and estimated the magnitude for Mw>5 earthquakes. Operational since June, 2016, we have successfully detected two earthquakes > M5 (M5.5, M5.1) that occurred within 100km of our network while producing zero false alarms.

Spatial Distribution of the Coefficient of Variation and Bayesian Forecast for the Paleo-Earthquakes in Japan

Science.gov (United States)

Nomura, Shunichi; Ogata, Yosihiko

2016-04-01

We propose a Bayesian method of probability forecasting for recurrent earthquakes of inland active faults in Japan. Renewal processes with the Brownian Passage Time (BPT) distribution are applied for over a half of active faults in Japan by the Headquarters for Earthquake Research Promotion (HERP) of Japan. Long-term forecast with the BPT distribution needs two parameters; the mean and coefficient of variation (COV) for recurrence intervals. The HERP applies a common COV parameter for all of these faults because most of them have very few specified paleoseismic events, which is not enough to estimate reliable COV values for respective faults. However, different COV estimates are proposed for the same paleoseismic catalog by some related works. It can make critical difference in forecast to apply different COV estimates and so COV should be carefully selected for individual faults. Recurrence intervals on a fault are, on the average, determined by the long-term slip rate caused by the tectonic motion but fluctuated by nearby seismicities which influence surrounding stress field. The COVs of recurrence intervals depend on such stress perturbation and so have spatial trends due to the heterogeneity of tectonic motion and seismicity. Thus we introduce a spatial structure on its COV parameter by Bayesian modeling with a Gaussian process prior. The COVs on active faults are correlated and take similar values for closely located faults. It is found that the spatial trends in the estimated COV values coincide with the density of active faults in Japan. We also show Bayesian forecasts by the proposed model using Markov chain Monte Carlo method. Our forecasts are different from HERP's forecast especially on the active faults where HERP's forecasts are very high or low.

Earthquake recurrence models fail when earthquakes fail to reset the stress field

Science.gov (United States)

Tormann, Thessa; Wiemer, Stefan; Hardebeck, Jeanne L.

2012-01-01

Parkfield's regularly occurring M6 mainshocks, about every 25 years, have over two decades stoked seismologists' hopes to successfully predict an earthquake of significant size. However, with the longest known inter-event time of 38 years, the latest M6 in the series (28 Sep 2004) did not conform to any of the applied forecast models, questioning once more the predictability of earthquakes in general. Our study investigates the spatial pattern of b-values along the Parkfield segment through the seismic cycle and documents a stably stressed structure. The forecasted rate of M6 earthquakes based on Parkfield's microseismicity b-values corresponds well to observed rates. We interpret the observed b-value stability in terms of the evolution of the stress field in that area: the M6 Parkfield earthquakes do not fully unload the stress on the fault, explaining why time recurrent models fail. We present the 1989 M6.9 Loma Prieta earthquake as counter example, which did release a significant portion of the stress along its fault segment and yields a substantial change in b-values.

Calibration and validation of earthquake catastrophe models. Case study: Impact Forecasting Earthquake Model for Algeria

Science.gov (United States)

Trendafiloski, G.; Gaspa Rebull, O.; Ewing, C.; Podlaha, A.; Magee, B.

2012-04-01

Calibration and validation are crucial steps in the production of the catastrophe models for the insurance industry in order to assure the model's reliability and to quantify its uncertainty. Calibration is needed in all components of model development including hazard and vulnerability. Validation is required to ensure that the losses calculated by the model match those observed in past events and which could happen in future. Impact Forecasting, the catastrophe modelling development centre of excellence within Aon Benfield, has recently launched its earthquake model for Algeria as a part of the earthquake model for the Maghreb region. The earthquake model went through a detailed calibration process including: (1) the seismic intensity attenuation model by use of macroseismic observations and maps from past earthquakes in Algeria; (2) calculation of the country-specific vulnerability modifiers by use of past damage observations in the country. The use of Benouar, 1994 ground motion prediction relationship was proven as the most appropriate for our model. Calculation of the regional vulnerability modifiers for the country led to 10% to 40% larger vulnerability indexes for different building types compared to average European indexes. The country specific damage models also included aggregate damage models for residential, commercial and industrial properties considering the description of the buildings stock given by World Housing Encyclopaedia and the local rebuilding cost factors equal to 10% for damage grade 1, 20% for damage grade 2, 35% for damage grade 3, 75% for damage grade 4 and 100% for damage grade 5. The damage grades comply with the European Macroseismic Scale (EMS-1998). The model was validated by use of "as-if" historical scenario simulations of three past earthquake events in Algeria M6.8 2003 Boumerdes, M7.3 1980 El-Asnam and M7.3 1856 Djidjelli earthquake. The calculated return periods of the losses for client market portfolio align with the

Earthquake precursors: spatial-temporal gravity changes before the great earthquakes in the Sichuan-Yunnan area

Science.gov (United States)

Zhu, Yi-Qing; Liang, Wei-Feng; Zhang, Song

2018-01-01

Using multiple-scale mobile gravity data in the Sichuan-Yunnan area, we systematically analyzed the relationships between spatial-temporal gravity changes and the 2014 Ludian, Yunnan Province Ms6.5 earthquake and the 2014 Kangding Ms6.3, 2013 Lushan Ms7.0, and 2008 Wenchuan Ms8.0 earthquakes in Sichuan Province. Our main results are as follows. (1) Before the occurrence of large earthquakes, gravity anomalies occur in a large area around the epicenters. The directions of gravity change gradient belts usually agree roughly with the directions of the main fault zones of the study area. Such gravity changes might reflect the increase of crustal stress, as well as the significant active tectonic movements and surface deformations along fault zones, during the period of gestation of great earthquakes. (2) Continuous significant changes of the multiple-scale gravity fields, as well as greater gravity changes with larger time scales, can be regarded as medium-range precursors of large earthquakes. The subsequent large earthquakes always occur in the area where the gravity changes greatly. (3) The spatial-temporal gravity changes are very useful in determining the epicenter of coming large earthquakes. The large gravity networks are useful to determine the general areas of coming large earthquakes. However, the local gravity networks with high spatial-temporal resolution are suitable for determining the location of epicenters. Therefore, denser gravity observation networks are necessary for better forecasts of the epicenters of large earthquakes. (4) Using gravity changes from mobile observation data, we made medium-range forecasts of the Kangding, Ludian, Lushan, and Wenchuan earthquakes, with especially successful forecasts of the location of their epicenters. Based on the above discussions, we emphasize that medium-/long-term potential for large earthquakes might exist nowadays in some areas with significant gravity anomalies in the study region. Thus, the monitoring

Lessons Learned about Best Practices for Communicating Earthquake Forecasting and Early Warning to Non-Scientific Publics

Science.gov (United States)

Sellnow, D. D.; Sellnow, T. L.

2017-12-01

Earthquake scientists are without doubt experts in understanding earthquake probabilities, magnitudes, and intensities, as well as the potential consequences of them to community infrastructures and inhabitants. One critical challenge these scientific experts face, however, rests with communicating what they know to the people they want to help. Helping scientists translate scientific information to non-scientists is something Drs. Tim and Deanna Sellnow have been committed to for decades. As such, they have compiled a host of data-driven best practices for communicating effectively to non-scientific publics about earthquake forecasting, probabilities, and warnings. In this session, they will summarize what they have learned as it may help earthquake scientists, emergency managers, and other key spokespersons share these important messages to disparate publics in ways that result in positive outcomes, the most important of which is saving lives.

Conditional Probabilities of Large Earthquake Sequences in California from the Physics-based Rupture Simulator RSQSim

Science.gov (United States)

Gilchrist, J. J.; Jordan, T. H.; Shaw, B. E.; Milner, K. R.; Richards-Dinger, K. B.; Dieterich, J. H.

2017-12-01

Within the SCEC Collaboratory for Interseismic Simulation and Modeling (CISM), we are developing physics-based forecasting models for earthquake ruptures in California. We employ the 3D boundary element code RSQSim (Rate-State Earthquake Simulator of Dieterich & Richards-Dinger, 2010) to generate synthetic catalogs with tens of millions of events that span up to a million years each. This code models rupture nucleation by rate- and state-dependent friction and Coulomb stress transfer in complex, fully interacting fault systems. The Uniform California Earthquake Rupture Forecast Version 3 (UCERF3) fault and deformation models are used to specify the fault geometry and long-term slip rates. We have employed the Blue Waters supercomputer to generate long catalogs of simulated California seismicity from which we calculate the forecasting statistics for large events. We have performed probabilistic seismic hazard analysis with RSQSim catalogs that were calibrated with system-wide parameters and found a remarkably good agreement with UCERF3 (Milner et al., this meeting). We build on this analysis, comparing the conditional probabilities of sequences of large events from RSQSim and UCERF3. In making these comparisons, we consider the epistemic uncertainties associated with the RSQSim parameters (e.g., rate- and state-frictional parameters), as well as the effects of model-tuning (e.g., adjusting the RSQSim parameters to match UCERF3 recurrence rates). The comparisons illustrate how physics-based rupture simulators might assist forecasters in understanding the short-term hazards of large aftershocks and multi-event sequences associated with complex, multi-fault ruptures.

Operational foreshock forecasting: Fifteen years after

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Ogata, Y.

2010-12-01

We are concerned with operational forecasting of the probability that events are foreshocks of a forthcoming earthquake that is significantly larger (mainshock). Specifically, we define foreshocks as the preshocks substantially smaller than the mainshock by a magnitude gap of 0.5 or larger. The probability gain of foreshock forecast is extremely high compare to long-term forecast by renewal processes or various alarm-based intermediate-term forecasts because of a large event’s low occurrence rate in a short period and a narrow target region. Thus, it is desired to establish operational foreshock probability forecasting as seismologists have done for aftershocks. When a series of earthquakes occurs in a region, we attempt to discriminate foreshocks from a swarm or mainshock-aftershock sequence. Namely, after real time identification of an earthquake cluster using methods such as the single-link algorithm, the probability is calculated by applying statistical features that discriminate foreshocks from other types of clusters, by considering the events' stronger proximity in time and space and tendency towards chronologically increasing magnitudes. These features were modeled for probability forecasting and the coefficients of the model were estimated in Ogata et al. (1996) for the JMA hypocenter data (M≧4, 1926-1993). Currently, fifteen years has passed since the publication of the above-stated work so that we are able to present the performance and validation of the forecasts (1994-2009) by using the same model. Taking isolated events into consideration, the probability of the first events in a potential cluster being a foreshock vary in a range between 0+% and 10+% depending on their locations. This conditional forecasting performs significantly better than the unconditional (average) foreshock probability of 3.7% throughout Japan region. Furthermore, when we have the additional events in a cluster, the forecast probabilities range more widely from nearly 0% to

Forecasting Italian seismicity through a spatio-temporal physical model: importance of considering time-dependency and reliability of the forecast

Directory of Open Access Journals (Sweden)

Amir Hakimhashemi

2010-11-01

Full Text Available We apply here a forecasting model to the Italian region for the spatio-temporal distribution of seismicity based on a smoothing Kernel function, Coulomb stress variations, and a rate-and-state friction law. We tested the feasibility of this approach, and analyzed the importance of introducing time-dependency in forecasting future events. The change in seismicity rate as a function of time was estimated by calculating the Coulomb stress change imparted by large earthquakes. We applied our approach to the region of Italy, and used all of the cataloged earthquakes that occurred up to 2006 to generate the reference seismicity rate. For calculation of the time-dependent seismicity rate changes, we estimated the rate-and-state stress transfer imparted by all of the ML≥4.0 earthquakes that occurred during 2007 and 2008. To validate the results, we first compared the reference seismicity rate with the distribution of ML≥1.8 earthquakes since 2007, using both a non-declustered and a declustered catalog. A positive correlation was found, and all of the forecast earthquakes had locations within 82% and 87% of the study area with the highest seismicity rate, respectively. Furthermore, 95% of the forecast earthquakes had locations within 27% and 47% of the study area with the highest seismicity rate, respectively. For the time-dependent seismicity rate changes, the number of events with locations in the regions with a seismicity rate increase was 11% more than in the regions with a seismicity rate decrease.

The EM Earthquake Precursor

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Jones, K. B., II; Saxton, P. T.

2013-12-01

Many attempts have been made to determine a sound forecasting method regarding earthquakes and warn the public in turn. Presently, the animal kingdom leads the precursor list alluding to a transmission related source. By applying the animal-based model to an electromagnetic (EM) wave model, various hypotheses were formed, but the most interesting one required the use of a magnetometer with a differing design and geometry. To date, numerous, high-end magnetometers have been in use in close proximity to fault zones for potential earthquake forecasting; however, something is still amiss. The problem still resides with what exactly is forecastable and the investigating direction of EM. After the 1989 Loma Prieta Earthquake, American earthquake investigators predetermined magnetometer use and a minimum earthquake magnitude necessary for EM detection. This action was set in motion, due to the extensive damage incurred and public outrage concerning earthquake forecasting; however, the magnetometers employed, grounded or buried, are completely subject to static and electric fields and have yet to correlate to an identifiable precursor. Secondly, there is neither a networked array for finding any epicentral locations, nor have there been any attempts to find even one. This methodology needs dismissal, because it is overly complicated, subject to continuous change, and provides no response time. As for the minimum magnitude threshold, which was set at M5, this is simply higher than what modern technological advances have gained. Detection can now be achieved at approximately M1, which greatly improves forecasting chances. A propagating precursor has now been detected in both the field and laboratory. Field antenna testing conducted outside the NE Texas town of Timpson in February, 2013, detected three strong EM sources along with numerous weaker signals. The antenna had mobility, and observations were noted for recurrence, duration, and frequency response. Next, two

Forecasting the Rupture Directivity of Large Earthquakes: Centroid Bias of the Conditional Hypocenter Distribution

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Donovan, J.; Jordan, T. H.

2012-12-01

Forecasting the rupture directivity of large earthquakes is an important problem in probabilistic seismic hazard analysis (PSHA), because directivity is known to strongly influence ground motions. We describe how rupture directivity can be forecast in terms of the "conditional hypocenter distribution" or CHD, defined to be the probability distribution of a hypocenter given the spatial distribution of moment release (fault slip). The simplest CHD is a uniform distribution, in which the hypocenter probability density equals the moment-release probability density. For rupture models in which the rupture velocity and rise time depend only on the local slip, the CHD completely specifies the distribution of the directivity parameter D, defined in terms of the degree-two polynomial moments of the source space-time function. This parameter, which is zero for a bilateral rupture and unity for a unilateral rupture, can be estimated from finite-source models or by the direct inversion of seismograms (McGuire et al., 2002). We compile D-values from published studies of 65 large earthquakes and show that these data are statistically inconsistent with the uniform CHD advocated by McGuire et al. (2002). Instead, the data indicate a "centroid biased" CHD, in which the expected distance between the hypocenter and the hypocentroid is less than that of a uniform CHD. In other words, the observed directivities appear to be closer to bilateral than predicted by this simple model. We discuss the implications of these results for rupture dynamics and fault-zone heterogeneities. We also explore their PSHA implications by modifying the CyberShake simulation-based hazard model for the Los Angeles region, which assumed a uniform CHD (Graves et al., 2011).

Earthquake and failure forecasting in real-time: A Forecasting Model Testing Centre

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Filgueira, Rosa; Atkinson, Malcolm; Bell, Andrew; Main, Ian; Boon, Steven; Meredith, Philip

2013-04-01

Across Europe there are a large number of rock deformation laboratories, each of which runs many experiments. Similarly there are a large number of theoretical rock physicists who develop constitutive and computational models both for rock deformation and changes in geophysical properties. Here we consider how to open up opportunities for sharing experimental data in a way that is integrated with multiple hypothesis testing. We present a prototype for a new forecasting model testing centre based on e-infrastructures for capturing and sharing data and models to accelerate the Rock Physicist (RP) research. This proposal is triggered by our work on data assimilation in the NERC EFFORT (Earthquake and Failure Forecasting in Real Time) project, using data provided by the NERC CREEP 2 experimental project as a test case. EFFORT is a multi-disciplinary collaboration between Geoscientists, Rock Physicists and Computer Scientist. Brittle failure of the crust is likely to play a key role in controlling the timing of a range of geophysical hazards, such as volcanic eruptions, yet the predictability of brittle failure is unknown. Our aim is to provide a facility for developing and testing models to forecast brittle failure in experimental and natural data. Model testing is performed in real-time, verifiably prospective mode, in order to avoid selection biases that are possible in retrospective analyses. The project will ultimately quantify the predictability of brittle failure, and how this predictability scales from simple, controlled laboratory conditions to the complex, uncontrolled real world. Experimental data are collected from controlled laboratory experiments which includes data from the UCL Laboratory and from Creep2 project which will undertake experiments in a deep-sea laboratory. We illustrate the properties of the prototype testing centre by streaming and analysing realistically noisy synthetic data, as an aid to generating and improving testing methodologies in

Multiscale seismicity analysis and forecasting: examples from the Western Pacific and Iceland

International Nuclear Information System (INIS)

Eberhard, A. J.

2014-01-01

Seismicity forecasting has three major challenges: (1) the development of models or algorithms to forecast of seismicity, (2) the evaluation and testing of the forecasts and (3) the need for an appropriate software package. The goal of my thesis is to improve seismicity forecasting. I do this by contributing to a solution for all of the three challenges, using data from the West Pacific and from Iceland. The thesis is split into three chapters, each focusing on one of the challenges and each chapter will be (or already is) published in a peer-reviewed scientific journal. This first chapter (chapter 2 in this thesis) is about testing of seismic forecasts. The Collaboratory for the Study of Earthquake Predictability (CSEP, www.cseptesting.org; Jordan, 2006) has been conducting an earthquake forecast experiment in the western Pacific. The data and forecasts of this experiment serve as example for this chapter. For the three participating statistical models, I analyze the first four years of this experiment. I use likelihood-based metrics to evaluate the consistency of the forecasts with the observed target earthquakes, and I apply measures based on Student’s t-test and the Wilcoxon signed-rank test to compare the forecasts. I estimate also the uncertainties of the test results resulting from uncertainties in earthquake location and seismic moment. The estimated uncertainties are relatively small and suggest that the evaluation metrics are relatively robust. In chapter 3 I focus on the forecast itself with an Operational Earthquake Forecasting (OEF) experiment–forecasting seismicity in near real-time–for Iceland. The goal is to estimate the feasibility of OEF with the state-of-the-art models. I use the period between 2007 and 2010 and a sparsely processed catalogue as basis for a retrospective forecasting experiment with next-day forecasts. To better understand the effect of updating cycle on the probability gain I also use scheme in which the forecasts are

Multiscale seismicity analysis and forecasting: examples from the Western Pacific and Iceland

Energy Technology Data Exchange (ETDEWEB)

Eberhard, A. J.

2014-07-01

Seismicity forecasting has three major challenges: (1) the development of models or algorithms to forecast of seismicity, (2) the evaluation and testing of the forecasts and (3) the need for an appropriate software package. The goal of my thesis is to improve seismicity forecasting. I do this by contributing to a solution for all of the three challenges, using data from the West Pacific and from Iceland. The thesis is split into three chapters, each focusing on one of the challenges and each chapter will be (or already is) published in a peer-reviewed scientific journal. This first chapter (chapter 2 in this thesis) is about testing of seismic forecasts. The Collaboratory for the Study of Earthquake Predictability (CSEP, www.cseptesting.org; Jordan, 2006) has been conducting an earthquake forecast experiment in the western Pacific. The data and forecasts of this experiment serve as example for this chapter. For the three participating statistical models, I analyze the first four years of this experiment. I use likelihood-based metrics to evaluate the consistency of the forecasts with the observed target earthquakes, and I apply measures based on Student’s t-test and the Wilcoxon signed-rank test to compare the forecasts. I estimate also the uncertainties of the test results resulting from uncertainties in earthquake location and seismic moment. The estimated uncertainties are relatively small and suggest that the evaluation metrics are relatively robust. In chapter 3 I focus on the forecast itself with an Operational Earthquake Forecasting (OEF) experiment–forecasting seismicity in near real-time–for Iceland. The goal is to estimate the feasibility of OEF with the state-of-the-art models. I use the period between 2007 and 2010 and a sparsely processed catalogue as basis for a retrospective forecasting experiment with next-day forecasts. To better understand the effect of updating cycle on the probability gain I also use scheme in which the forecasts are

Recent Achievements of the Collaboratory for the Study of Earthquake Predictability

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Jackson, D. D.; Liukis, M.; Werner, M. J.; Schorlemmer, D.; Yu, J.; Maechling, P. J.; Zechar, J. D.; Jordan, T. H.

2015-12-01

Maria Liukis, SCEC, USC; Maximilian Werner, University of Bristol; Danijel Schorlemmer, GFZ Potsdam; John Yu, SCEC, USC; Philip Maechling, SCEC, USC; Jeremy Zechar, Swiss Seismological Service, ETH; Thomas H. Jordan, SCEC, USC, and the CSEP Working Group The Collaboratory for the Study of Earthquake Predictability (CSEP) supports a global program to conduct prospective earthquake forecasting experiments. CSEP testing centers are now operational in California, New Zealand, Japan, China, and Europe with 435 models under evaluation. The California testing center, operated by SCEC, has been operational since Sept 1, 2007, and currently hosts 30-minute, 1-day, 3-month, 1-year and 5-year forecasts, both alarm-based and probabilistic, for California, the Western Pacific, and worldwide. We have reduced testing latency, implemented prototype evaluation of M8 forecasts, and are currently developing formats and procedures to evaluate externally-hosted forecasts and predictions. These efforts are related to CSEP support of the USGS program in operational earthquake forecasting and a DHS project to register and test external forecast procedures from experts outside seismology. A retrospective experiment for the 2010-2012 Canterbury earthquake sequence has been completed, and the results indicate that some physics-based and hybrid models outperform purely statistical (e.g., ETAS) models. The experiment also demonstrates the power of the CSEP cyberinfrastructure for retrospective testing. Our current development includes evaluation strategies that increase computational efficiency for high-resolution global experiments, such as the evaluation of the Global Earthquake Activity Rate (GEAR) model. We describe the open-source CSEP software that is available to researchers as they develop their forecast models (http://northridge.usc.edu/trac/csep/wiki/MiniCSEP). We also discuss applications of CSEP infrastructure to geodetic transient detection and how CSEP procedures are being

Thermal infrared anomalies of several strong earthquakes.

Science.gov (United States)

Wei, Congxin; Zhang, Yuansheng; Guo, Xiao; Hui, Shaoxing; Qin, Manzhong; Zhang, Ying

2013-01-01

In the history of earthquake thermal infrared research, it is undeniable that before and after strong earthquakes there are significant thermal infrared anomalies which have been interpreted as preseismic precursor in earthquake prediction and forecasting. In this paper, we studied the characteristics of thermal radiation observed before and after the 8 great earthquakes with magnitude up to Ms7.0 by using the satellite infrared remote sensing information. We used new types of data and method to extract the useful anomaly information. Based on the analyses of 8 earthquakes, we got the results as follows. (1) There are significant thermal radiation anomalies before and after earthquakes for all cases. The overall performance of anomalies includes two main stages: expanding first and narrowing later. We easily extracted and identified such seismic anomalies by method of "time-frequency relative power spectrum." (2) There exist evident and different characteristic periods and magnitudes of thermal abnormal radiation for each case. (3) Thermal radiation anomalies are closely related to the geological structure. (4) Thermal radiation has obvious characteristics in abnormal duration, range, and morphology. In summary, we should be sure that earthquake thermal infrared anomalies as useful earthquake precursor can be used in earthquake prediction and forecasting.

Applying Binary Forecasting Approaches to Induced Seismicity in the Western Canada Sedimentary Basin

Science.gov (United States)

Kahue, R.; Shcherbakov, R.

2016-12-01

The Western Canada Sedimentary Basin has been chosen as a focus due to an increase in the recent observed seismicity there which is most likely linked to anthropogenic activities related to unconventional oil and gas exploration. Seismicity caused by these types of activities is called induced seismicity. The occurrence of moderate to larger induced earthquakes in areas where critical infrastructure is present can be potentially problematic. Here we use a binary forecast method to analyze past seismicity and well production data in order to quantify future areas of increased seismicity. This method splits the given region into spatial cells. The binary forecast method used here has been suggested in the past to retroactively forecast large earthquakes occurring globally in areas called alarm cells. An alarm cell, or alert zone, is a bin in which there is a higher likelihood for earthquakes to occur based on previous data. The first method utilizes the cumulative Benioff strain, based on earthquakes that had occurred in each bin above a given magnitude over a time interval called the training period. The second method utilizes the cumulative well production data within each bin. Earthquakes that occurred within an alert zone in the retrospective forecast period contribute to the hit rate, while alert zones that did not have an earthquake occur within them in the forecast period contribute to the false alarm rate. In the resulting analysis the hit rate and false alarm rate are determined after optimizing and modifying the initial parameters using the receiver operating characteristic diagram. It is found that when modifying the cell size and threshold magnitude parameters within various training periods, hit and false alarm rates are obtained for specific regions in Western Canada using both recent seismicity and cumulative well production data. Certain areas are thus shown to be more prone to potential larger earthquakes based on both datasets. This has implications

Forecasting in Complex Systems

Science.gov (United States)

Rundle, J. B.; Holliday, J. R.; Graves, W. R.; Turcotte, D. L.; Donnellan, A.

2014-12-01

Complex nonlinear systems are typically characterized by many degrees of freedom, as well as interactions between the elements. Interesting examples can be found in the areas of earthquakes and finance. In these two systems, fat tails play an important role in the statistical dynamics. For earthquake systems, the Gutenberg-Richter magnitude-frequency is applicable, whereas for daily returns for the securities in the financial markets are known to be characterized by leptokurtotic statistics in which the tails are power law. Very large fluctuations are present in both systems. In earthquake systems, one has the example of great earthquakes such as the M9.1, March 11, 2011 Tohoku event. In financial systems, one has the example of the market crash of October 19, 1987. Both were largely unexpected events that severely impacted the earth and financial systems systemically. Other examples include the M9.3 Andaman earthquake of December 26, 2004, and the Great Recession which began with the fall of Lehman Brothers investment bank on September 12, 2013. Forecasting the occurrence of these damaging events has great societal importance. In recent years, national funding agencies in a variety of countries have emphasized the importance of societal relevance in research, and in particular, the goal of improved forecasting technology. Previous work has shown that both earthquakes and financial crashes can be described by a common Landau-Ginzburg-type free energy model. These metastable systems are characterized by fat tail statistics near the classical spinodal. Correlations in these systems can grow and recede, but do not imply causation, a common source of misunderstanding. In both systems, a common set of techniques can be used to compute the probabilities of future earthquakes or crashes. In this talk, we describe the basic phenomenology of these systems and emphasize their similarities and differences. We also consider the problem of forecast validation and verification

Earthquake risk assessment of building structures

International Nuclear Information System (INIS)

Ellingwood, Bruce R.

2001-01-01

During the past two decades, probabilistic risk analysis tools have been applied to assess the performance of new and existing building structural systems. Structural design and evaluation of buildings and other facilities with regard to their ability to withstand the effects of earthquakes requires special considerations that are not normally a part of such evaluations for other occupancy, service and environmental loads. This paper reviews some of these special considerations, specifically as they pertain to probability-based codified design and reliability-based condition assessment of existing buildings. Difficulties experienced in implementing probability-based limit states design criteria for earthquake are summarized. Comparisons of predicted and observed building damage highlight the limitations of using current deterministic approaches for post-earthquake building condition assessment. The importance of inherent randomness and modeling uncertainty in forecasting building performance is examined through a building fragility assessment of a steel frame with welded connections that was damaged during the Northridge Earthquake of 1994. The prospects for future improvements in earthquake-resistant design procedures based on a more rational probability-based treatment of uncertainty are examined

Multicomponent ensemble models to forecast induced seismicity

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Király-Proag, E.; Gischig, V.; Zechar, J. D.; Wiemer, S.

2018-01-01

In recent years, human-induced seismicity has become a more and more relevant topic due to its economic and social implications. Several models and approaches have been developed to explain underlying physical processes or forecast induced seismicity. They range from simple statistical models to coupled numerical models incorporating complex physics. We advocate the need for forecast testing as currently the best method for ascertaining if models are capable to reasonably accounting for key physical governing processes—or not. Moreover, operational forecast models are of great interest to help on-site decision-making in projects entailing induced earthquakes. We previously introduced a standardized framework following the guidelines of the Collaboratory for the Study of Earthquake Predictability, the Induced Seismicity Test Bench, to test, validate, and rank induced seismicity models. In this study, we describe how to construct multicomponent ensemble models based on Bayesian weightings that deliver more accurate forecasts than individual models in the case of Basel 2006 and Soultz-sous-Forêts 2004 enhanced geothermal stimulation projects. For this, we examine five calibrated variants of two significantly different model groups: (1) Shapiro and Smoothed Seismicity based on the seismogenic index, simple modified Omori-law-type seismicity decay, and temporally weighted smoothed seismicity; (2) Hydraulics and Seismicity based on numerically modelled pore pressure evolution that triggers seismicity using the Mohr-Coulomb failure criterion. We also demonstrate how the individual and ensemble models would perform as part of an operational Adaptive Traffic Light System. Investigating seismicity forecasts based on a range of potential injection scenarios, we use forecast periods of different durations to compute the occurrence probabilities of seismic events M ≥ 3. We show that in the case of the Basel 2006 geothermal stimulation the models forecast hazardous levels

Temporal variation of soil gas compositions for earthquake surveillance in Taiwan

International Nuclear Information System (INIS)

Walia, Vivek; Yang, Tsanyao Frank; Lin, Shih-Jung; Kumar, Arvind; Fu, Ching-Chou; Chiu, Jun-Ming; Chang, Hsaio-Hsien; Wen, Kuo-Liang; Chen, Cheng-Hong

2013-01-01

The present study is proposed to investigate temporal variations of soil–gas composition in the vicinity of different fault zones in Taiwan. To carry out the investigations, variations of soil–gases compositions were measured at continuous earthquake monitoring stations along Hsincheng and Hsinhua faults in Hsinchu and Tainan areas, respectively. Before selecting a monitoring site, the occurrence of deeper gas emanation was investigated by the soil–gas surveys and followed by continuous monitoring of some selected sites with respect to tectonic activity to check the sensitivity of the sites. Based on the results of long term geochemical monitoring at the established monitoring stations we can divide the studied area in two different tectonic zones. We proposed tectonic based model for earthquake forecasting in Taiwan and tested it for some big earthquakes occurred during observation period i.e. 2009–2010. Based on the anomalous signatures from particular monitoring stations we are in a state to identify the area for impending earthquakes of magnitude ≥5 and we have tested it for some earthquakes which rocked the country during that period. It can be concluded from above results that the stress/strain transmission for a particular earthquake is hindered by different tectonic settings of the region under study. - Highlights: ► Variations of soil–gases composition is studied at two different faults of Taiwan. ► Tectonic based model for earthquake forecasting in Taiwan was proposed and tested. ► Selection criteria to identify threshold earthquakes have been defined. ► Stress/strain transmission for earthquake may be hindered by tectonic settings

Identified EM Earthquake Precursors

Science.gov (United States)

Jones, Kenneth, II; Saxton, Patrick

2014-05-01

Many attempts have been made to determine a sound forecasting method regarding earthquakes and warn the public in turn. Presently, the animal kingdom leads the precursor list alluding to a transmission related source. By applying the animal-based model to an electromagnetic (EM) wave model, various hypotheses were formed, but the most interesting one required the use of a magnetometer with a differing design and geometry. To date, numerous, high-end magnetometers have been in use in close proximity to fault zones for potential earthquake forecasting; however, something is still amiss. The problem still resides with what exactly is forecastable and the investigating direction of EM. After a number of custom rock experiments, two hypotheses were formed which could answer the EM wave model. The first hypothesis concerned a sufficient and continuous electron movement either by surface or penetrative flow, and the second regarded a novel approach to radio transmission. Electron flow along fracture surfaces was determined to be inadequate in creating strong EM fields, because rock has a very high electrical resistance making it a high quality insulator. Penetrative flow could not be corroborated as well, because it was discovered that rock was absorbing and confining electrons to a very thin skin depth. Radio wave transmission and detection worked with every single test administered. This hypothesis was reviewed for propagating, long-wave generation with sufficient amplitude, and the capability of penetrating solid rock. Additionally, fracture spaces, either air or ion-filled, can facilitate this concept from great depths and allow for surficial detection. A few propagating precursor signals have been detected in the field occurring with associated phases using custom-built loop antennae. Field testing was conducted in Southern California from 2006-2011, and outside the NE Texas town of Timpson in February, 2013. The antennae have mobility and observations were noted for

Uniform California earthquake rupture forecast, version 3 (UCERF3): the time-independent model

Science.gov (United States)

Field, Edward H.; Biasi, Glenn P.; Bird, Peter; Dawson, Timothy E.; Felzer, Karen R.; Jackson, David D.; Johnson, Kaj M.; Jordan, Thomas H.; Madden, Christopher; Michael, Andrew J.; Milner, Kevin R.; Page, Morgan T.; Parsons, Thomas; Powers, Peter M.; Shaw, Bruce E.; Thatcher, Wayne R.; Weldon, Ray J.; Zeng, Yuehua; ,

2013-01-01

In this report we present the time-independent component of the Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3), which provides authoritative estimates of the magnitude, location, and time-averaged frequency of potentially damaging earthquakes in California. The primary achievements have been to relax fault segmentation assumptions and to include multifault ruptures, both limitations of the previous model (UCERF2). The rates of all earthquakes are solved for simultaneously, and from a broader range of data, using a system-level "grand inversion" that is both conceptually simple and extensible. The inverse problem is large and underdetermined, so a range of models is sampled using an efficient simulated annealing algorithm. The approach is more derivative than prescriptive (for example, magnitude-frequency distributions are no longer assumed), so new analysis tools were developed for exploring solutions. Epistemic uncertainties were also accounted for using 1,440 alternative logic tree branches, necessitating access to supercomputers. The most influential uncertainties include alternative deformation models (fault slip rates), a new smoothed seismicity algorithm, alternative values for the total rate of M≥5 events, and different scaling relationships, virtually all of which are new. As a notable first, three deformation models are based on kinematically consistent inversions of geodetic and geologic data, also providing slip-rate constraints on faults previously excluded because of lack of geologic data. The grand inversion constitutes a system-level framework for testing hypotheses and balancing the influence of different experts. For example, we demonstrate serious challenges with the Gutenberg-Richter hypothesis for individual faults. UCERF3 is still an approximation of the system, however, and the range of models is limited (for example, constrained to stay close to UCERF2). Nevertheless, UCERF3 removes the apparent UCERF2 overprediction of

Robust seismicity forecasting based on Bayesian parameter estimation for epidemiological spatio-temporal aftershock clustering models.

Science.gov (United States)

Ebrahimian, Hossein; Jalayer, Fatemeh

2017-08-29

In the immediate aftermath of a strong earthquake and in the presence of an ongoing aftershock sequence, scientific advisories in terms of seismicity forecasts play quite a crucial role in emergency decision-making and risk mitigation. Epidemic Type Aftershock Sequence (ETAS) models are frequently used for forecasting the spatio-temporal evolution of seismicity in the short-term. We propose robust forecasting of seismicity based on ETAS model, by exploiting the link between Bayesian inference and Markov Chain Monte Carlo Simulation. The methodology considers the uncertainty not only in the model parameters, conditioned on the available catalogue of events occurred before the forecasting interval, but also the uncertainty in the sequence of events that are going to happen during the forecasting interval. We demonstrate the methodology by retrospective early forecasting of seismicity associated with the 2016 Amatrice seismic sequence activities in central Italy. We provide robust spatio-temporal short-term seismicity forecasts with various time intervals in the first few days elapsed after each of the three main events within the sequence, which can predict the seismicity within plus/minus two standard deviations from the mean estimate within the few hours elapsed after the main event.

Long-term earthquake forecasts based on the epidemic-type aftershock sequence (ETAS model for short-term clustering

Directory of Open Access Journals (Sweden)

Jiancang Zhuang

2012-07-01

Full Text Available Based on the ETAS (epidemic-type aftershock sequence model, which is used for describing the features of short-term clustering of earthquake occurrence, this paper presents some theories and techniques related to evaluating the probability distribution of the maximum magnitude in a given space-time window, where the Gutenberg-Richter law for earthquake magnitude distribution cannot be directly applied. It is seen that the distribution of the maximum magnitude in a given space-time volume is determined in the longterm by the background seismicity rate and the magnitude distribution of the largest events in each earthquake cluster. The techniques introduced were applied to the seismicity in the Japan region in the period from 1926 to 2009. It was found that the regions most likely to have big earthquakes are along the Tohoku (northeastern Japan Arc and the Kuril Arc, both with much higher probabilities than the offshore Nankai and Tokai regions.

Earthquake hazard assessment in the Zagros Orogenic Belt of Iran using a fuzzy rule-based model

Science.gov (United States)

Farahi Ghasre Aboonasr, Sedigheh; Zamani, Ahmad; Razavipour, Fatemeh; Boostani, Reza

2017-08-01

Producing accurate seismic hazard map and predicting hazardous areas is necessary for risk mitigation strategies. In this paper, a fuzzy logic inference system is utilized to estimate the earthquake potential and seismic zoning of Zagros Orogenic Belt. In addition to the interpretability, fuzzy predictors can capture both nonlinearity and chaotic behavior of data, where the number of data is limited. In this paper, earthquake pattern in the Zagros has been assessed for the intervals of 10 and 50 years using fuzzy rule-based model. The Molchan statistical procedure has been used to show that our forecasting model is reliable. The earthquake hazard maps for this area reveal some remarkable features that cannot be observed on the conventional maps. Regarding our achievements, some areas in the southern (Bandar Abbas), southwestern (Bandar Kangan) and western (Kermanshah) parts of Iran display high earthquake severity even though they are geographically far apart.

Expanding the Delivery of Rapid Earthquake Information and Warnings for Response and Recovery

Science.gov (United States)

Blanpied, M. L.; McBride, S.; Hardebeck, J.; Michael, A. J.; van der Elst, N.

2017-12-01

Scientific organizations like the United States Geological Survey (USGS) release information to support effective responses during an earthquake crisis. Information is delivered to the White House, the National Command Center, the Departments of Defense, Homeland Security (including FEMA), Transportation, Energy, and Interior. Other crucial stakeholders include state officials and decision makers, emergency responders, numerous public and private infrastructure management centers (e.g., highways, railroads and pipelines), the media, and the public. To meet the diverse information requirements of these users, rapid earthquake notifications have been developed to be delivered by e-mail and text message, as well as a suite of earthquake information resources such as ShakeMaps, Did You Feel It?, PAGER impact estimates, and data are delivered via the web. The ShakeAlert earthquake early warning system being developed for the U.S. West Coast will identify and characterize an earthquake a few seconds after it begins, estimate the likely intensity of ground shaking, and deliver brief but critically important warnings to people and infrastructure in harm's way. Currently the USGS is also developing a capability to deliver Operational Earthquake Forecasts (OEF). These provide estimates of potential seismic behavior after large earthquakes and during evolving aftershock sequences. Similar work is underway in New Zealand, Japan, and Italy. In the development of OEF forecasts, social science research conducted during these sequences indicates that aftershock forecasts are valued for a variety of reasons, from informing critical response and recovery decisions to psychologically preparing for more earthquakes. New tools will allow users to customize map-based, spatiotemporal forecasts to their specific needs. Hazard curves and other advanced information will also be available. For such authoritative information to be understood and used during the pressures of an earthquake

Nowcasting Earthquakes and Tsunamis

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Rundle, J. B.; Turcotte, D. L.

2017-12-01

The term "nowcasting" refers to the estimation of the current uncertain state of a dynamical system, whereas "forecasting" is a calculation of probabilities of future state(s). Nowcasting is a term that originated in economics and finance, referring to the process of determining the uncertain state of the economy or market indicators such as GDP at the current time by indirect means. We have applied this idea to seismically active regions, where the goal is to determine the current state of a system of faults, and its current level of progress through the earthquake cycle (http://onlinelibrary.wiley.com/doi/10.1002/2016EA000185/full). Advantages of our nowcasting method over forecasting models include: 1) Nowcasting is simply data analysis and does not involve a model having parameters that must be fit to data; 2) We use only earthquake catalog data which generally has known errors and characteristics; and 3) We use area-based analysis rather than fault-based analysis, meaning that the methods work equally well on land and in subduction zones. To use the nowcast method to estimate how far the fault system has progressed through the "cycle" of large recurring earthquakes, we use the global catalog of earthquakes, using "small" earthquakes to determine the level of hazard from "large" earthquakes in the region. We select a "small" region in which the nowcast is to be made, and compute the statistics of a much larger region around the small region. The statistics of the large region are then applied to the small region. For an application, we can define a small region around major global cities, for example a "small" circle of radius 150 km and a depth of 100 km, as well as a "large" earthquake magnitude, for example M6.0. The region of influence of such earthquakes is roughly 150 km radius x 100 km depth, which is the reason these values were selected. We can then compute and rank the seismic risk of the world's major cities in terms of their relative seismic risk

Ergodicity and Phase Transitions and Their Implications for Earthquake Forecasting.

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Klein, W.

2017-12-01

Forecasting earthquakes or even predicting the statistical distribution of events on a given fault is extremely difficult. One reason for this difficulty is the large number of fault characteristics that can affect the distribution and timing of events. The range of stress transfer, the level of noise, and the nature of the friction force all influence the type of the events and the values of these parameters can vary from fault to fault and also vary with time. In addition, the geometrical structure of the faults and the correlation of events on different faults plays an important role in determining the event size and their distribution. Another reason for the difficulty is that the important fault characteristics are not easily measured. The noise level, fault structure, stress transfer range, and the nature of the friction force are extremely difficult, if not impossible to ascertain. Given this lack of information, one of the most useful approaches to understanding the effect of fault characteristics and the way they interact is to develop and investigate models of faults and fault systems.In this talk I will present results obtained from a series of models of varying abstraction and compare them with data from actual faults. We are able to provide a physical basis for several observed phenomena such as the earthquake cycle, thefact that some faults display Gutenburg-Richter scaling and others do not, and that some faults exhibit quasi-periodic characteristic events and others do not. I will also discuss some surprising results such as the fact that some faults are in thermodynamic equilibrium depending on the stress transfer range and the noise level. An example of an important conclusion that can be drawn from this work is that the statistical distribution of earthquake events can vary from fault to fault and that an indication of an impending large event such as accelerating moment release may be relevant on some faults but not on others.

Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast

Directory of Open Access Journals (Sweden)

Jinyin Ye

2016-01-01

Full Text Available TIGGE (THORPEX International Grand Global Ensemble was a major part of the THORPEX (Observing System Research and Predictability Experiment. It integrates ensemble precipitation products from all the major forecast centers in the world and provides systematic evaluation on the multimodel ensemble prediction system. Development of meteorologic-hydrologic coupled flood forecasting model and early warning model based on the TIGGE precipitation ensemble forecast can provide flood probability forecast, extend the lead time of the flood forecast, and gain more time for decision-makers to make the right decision. In this study, precipitation ensemble forecast products from ECMWF, NCEP, and CMA are used to drive distributed hydrologic model TOPX. We focus on Yi River catchment and aim to build a flood forecast and early warning system. The results show that the meteorologic-hydrologic coupled model can satisfactorily predict the flow-process of four flood events. The predicted occurrence time of peak discharges is close to the observations. However, the magnitude of the peak discharges is significantly different due to various performances of the ensemble prediction systems. The coupled forecasting model can accurately predict occurrence of the peak time and the corresponding risk probability of peak discharge based on the probability distribution of peak time and flood warning, which can provide users a strong theoretical foundation and valuable information as a promising new approach.

Real-time forecasting of the April 11, 2012 Sumatra tsunami

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Wang, Dailin; Becker, Nathan C.; Walsh, David; Fryer, Gerard J.; Weinstein, Stuart A.; McCreery, Charles S.; ,

2012-01-01

The April 11, 2012, magnitude 8.6 earthquake off the northern coast of Sumatra generated a tsunami that was recorded at sea-level stations as far as 4800 km from the epicenter and at four ocean bottom pressure sensors (DARTs) in the Indian Ocean. The governments of India, Indonesia, Sri Lanka, Thailand, and Maldives issued tsunami warnings for their coastlines. The United States' Pacific Tsunami Warning Center (PTWC) issued an Indian Ocean-wide Tsunami Watch Bulletin in its role as an Interim Service Provider for the region. Using an experimental real-time tsunami forecast model (RIFT), PTWC produced a series of tsunami forecasts during the event that were based on rapidly derived earthquake parameters, including initial location and Mwp magnitude estimates and the W-phase centroid moment tensor solutions (W-phase CMTs) obtained at PTWC and at the U. S. Geological Survey (USGS). We discuss the real-time forecast methodology and how successive, real-time tsunami forecasts using the latest W-phase CMT solutions improved the accuracy of the forecast.

Fractals and Forecasting in Earthquakes and Finance

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Rundle, J. B.; Holliday, J. R.; Turcotte, D. L.

2011-12-01

It is now recognized that Benoit Mandelbrot's fractals play a critical role in describing a vast range of physical and social phenomena. Here we focus on two systems, earthquakes and finance. Since 1942, earthquakes have been characterized by the Gutenberg-Richter magnitude-frequency relation, which in more recent times is often written as a moment-frequency power law. A similar relation can be shown to hold for financial markets. Moreover, a recent New York Times article, titled "A Richter Scale for the Markets" [1] summarized the emerging viewpoint that stock market crashes can be described with similar ideas as large and great earthquakes. The idea that stock market crashes can be related in any way to earthquake phenomena has its roots in Mandelbrot's 1963 work on speculative prices in commodities markets such as cotton [2]. He pointed out that Gaussian statistics did not account for the excessive number of booms and busts that characterize such markets. Here we show that both earthquakes and financial crashes can both be described by a common Landau-Ginzburg-type free energy model, involving the presence of a classical limit of stability, or spinodal. These metastable systems are characterized by fractal statistics near the spinodal. For earthquakes, the independent ("order") parameter is the slip deficit along a fault, whereas for the financial markets, it is financial leverage in place. For financial markets, asset values play the role of a free energy. In both systems, a common set of techniques can be used to compute the probabilities of future earthquakes or crashes. In the case of financial models, the probabilities are closely related to implied volatility, an important component of Black-Scholes models for stock valuations. [2] B. Mandelbrot, The variation of certain speculative prices, J. Business, 36, 294 (1963)

Long‐term time‐dependent probabilities for the third Uniform California Earthquake Rupture Forecast (UCERF3)

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Field, Edward; Biasi, Glenn P.; Bird, Peter; Dawson, Timothy E.; Felzer, Karen R.; Jackson, David A.; Johnson, Kaj M.; Jordan, Thomas H.; Madden, Christopher; Michael, Andrew J.; Milner, Kevin; Page, Morgan T.; Parsons, Thomas E.; Powers, Peter; Shaw, Bruce E.; Thatcher, Wayne R.; Weldon, Ray J.; Zeng, Yuehua

2015-01-01

The 2014 Working Group on California Earthquake Probabilities (WGCEP 2014) presents time-dependent earthquake probabilities for the third Uniform California Earthquake Rupture Forecast (UCERF3). Building on the UCERF3 time-independent model, published previously, renewal models are utilized to represent elastic-rebound-implied probabilities. A new methodology has been developed that solves applicability issues in the previous approach for un-segmented models. The new methodology also supports magnitude-dependent aperiodicity and accounts for the historic open interval on faults that lack a date-of-last-event constraint. Epistemic uncertainties are represented with a logic tree, producing 5,760 different forecasts. Results for a variety of evaluation metrics are presented, including logic-tree sensitivity analyses and comparisons to the previous model (UCERF2). For 30-year M≥6.7 probabilities, the most significant changes from UCERF2 are a threefold increase on the Calaveras fault and a threefold decrease on the San Jacinto fault. Such changes are due mostly to differences in the time-independent models (e.g., fault slip rates), with relaxation of segmentation and inclusion of multi-fault ruptures being particularly influential. In fact, some UCERF2 faults were simply too long to produce M 6.7 sized events given the segmentation assumptions in that study. Probability model differences are also influential, with the implied gains (relative to a Poisson model) being generally higher in UCERF3. Accounting for the historic open interval is one reason. Another is an effective 27% increase in the total elastic-rebound-model weight. The exact factors influencing differences between UCERF2 and UCERF3, as well as the relative importance of logic-tree branches, vary throughout the region, and depend on the evaluation metric of interest. For example, M≥6.7 probabilities may not be a good proxy for other hazard or loss measures. This sensitivity, coupled with the

Dynamic evaluation of seismic hazard and risks based on the Unified Scaling Law for Earthquakes

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Kossobokov, V. G.; Nekrasova, A.

2016-12-01

We continue applying the general concept of seismic risk analysis in a number of seismic regions worldwide by constructing seismic hazard maps based on the Unified Scaling Law for Earthquakes (USLE), i.e. log N(M,L) = A + B•(6 - M) + C•log L, where N(M,L) is the expected annual number of earthquakes of a certain magnitude M within an seismically prone area of linear dimension L, A characterizes the average annual rate of strong (M = 6) earthquakes, B determines the balance between magnitude ranges, and C estimates the fractal dimension of seismic locus in projection to the Earth surface. The parameters A, B, and C of USLE are used to assess, first, the expected maximum magnitude in a time interval at a seismically prone cell of a uniform grid that cover the region of interest, and then the corresponding expected ground shaking parameters. After a rigorous testing against the available seismic evidences in the past (e.g., the historically reported macro-seismic intensity or paleo data), such a seismic hazard map is used to generate maps of specific earthquake risks for population, cities, and infrastructures. The hazard maps for a given territory change dramatically, when the methodology is applied to a certain size moving time window, e.g. about a decade long for an intermediate-term regional assessment or exponentially increasing intervals for a daily local strong aftershock forecasting. The of dynamical seismic hazard and risks assessment is illustrated by applications to the territory of Greater Caucasus and Crimea and the two-year series of aftershocks of the 11 October 2008 Kurchaloy, Chechnya earthquake which case-history appears to be encouraging for further systematic testing as potential short-term forecasting tool.

Probing magma reservoirs to improve volcano forecasts

Science.gov (United States)

Lowenstern, Jacob B.; Sisson, Thomas W.; Hurwitz, Shaul

2017-01-01

When it comes to forecasting eruptions, volcano observatories rely mostly on real-time signals from earthquakes, ground deformation, and gas discharge, combined with probabilistic assessments based on past behavior [Sparks and Cashman, 2017]. There is comparatively less reliance on geophysical and petrological understanding of subsurface magma reservoirs.

Large earthquake rates from geologic, geodetic, and seismological perspectives

Science.gov (United States)

Jackson, D. D.

2017-12-01

Earthquake rate and recurrence information comes primarily from geology, geodesy, and seismology. Geology gives the longest temporal perspective, but it reveals only surface deformation, relatable to earthquakes only with many assumptions. Geodesy is also limited to surface observations, but it detects evidence of the processes leading to earthquakes, again subject to important assumptions. Seismology reveals actual earthquakes, but its history is too short to capture important properties of very large ones. Unfortunately, the ranges of these observation types barely overlap, so that integrating them into a consistent picture adequate to infer future prospects requires a great deal of trust. Perhaps the most important boundary is the temporal one at the beginning of the instrumental seismic era, about a century ago. We have virtually no seismological or geodetic information on large earthquakes before then, and little geological information after. Virtually all-modern forecasts of large earthquakes assume some form of equivalence between tectonic- and seismic moment rates as functions of location, time, and magnitude threshold. That assumption links geology, geodesy, and seismology, but it invokes a host of other assumptions and incurs very significant uncertainties. Questions include temporal behavior of seismic and tectonic moment rates; shape of the earthquake magnitude distribution; upper magnitude limit; scaling between rupture length, width, and displacement; depth dependence of stress coupling; value of crustal rigidity; and relation between faults at depth and their surface fault traces, to name just a few. In this report I'll estimate the quantitative implications for estimating large earthquake rate. Global studies like the GEAR1 project suggest that surface deformation from geology and geodesy best show the geography of very large, rare earthquakes in the long term, while seismological observations of small earthquakes best forecasts moderate earthquakes

A prototype operational earthquake loss model for California based on UCERF3-ETAS – A first look at valuation

Science.gov (United States)

Field, Edward; Porter, Keith; Milner, Kevn

2017-01-01

We present a prototype operational loss model based on UCERF3-ETAS, which is the third Uniform California Earthquake Rupture Forecast with an Epidemic Type Aftershock Sequence (ETAS) component. As such, UCERF3-ETAS represents the first earthquake forecast to relax fault segmentation assumptions and to include multi-fault ruptures, elastic-rebound, and spatiotemporal clustering, all of which seem important for generating realistic and useful aftershock statistics. UCERF3-ETAS is nevertheless an approximation of the system, however, so usefulness will vary and potential value needs to be ascertained in the context of each application. We examine this question with respect to statewide loss estimates, exemplifying how risk can be elevated by orders of magnitude due to triggered events following various scenario earthquakes. Two important considerations are the probability gains, relative to loss likelihoods in the absence of main shocks, and the rapid decay of gains with time. Significant uncertainties and model limitations remain, so we hope this paper will inspire similar analyses with respect to other risk metrics to help ascertain whether operationalization of UCERF3-ETAS would be worth the considerable resources required.

Short-term wind power combined forecasting based on error forecast correction

International Nuclear Information System (INIS)

Liang, Zhengtang; Liang, Jun; Wang, Chengfu; Dong, Xiaoming; Miao, Xiaofeng

2016-01-01

Highlights: • The correlation relationships of short-term wind power forecast errors are studied. • The correlation analysis method of the multi-step forecast errors is proposed. • A strategy selecting the input variables for the error forecast models is proposed. • Several novel combined models based on error forecast correction are proposed. • The combined models have improved the short-term wind power forecasting accuracy. - Abstract: With the increasing contribution of wind power to electric power grids, accurate forecasting of short-term wind power has become particularly valuable for wind farm operators, utility operators and customers. The aim of this study is to investigate the interdependence structure of errors in short-term wind power forecasting that is crucial for building error forecast models with regression learning algorithms to correct predictions and improve final forecasting accuracy. In this paper, several novel short-term wind power combined forecasting models based on error forecast correction are proposed in the one-step ahead, continuous and discontinuous multi-step ahead forecasting modes. First, the correlation relationships of forecast errors of the autoregressive model, the persistence method and the support vector machine model in various forecasting modes have been investigated to determine whether the error forecast models can be established by regression learning algorithms. Second, according to the results of the correlation analysis, the range of input variables is defined and an efficient strategy for selecting the input variables for the error forecast models is proposed. Finally, several combined forecasting models are proposed, in which the error forecast models are based on support vector machine/extreme learning machine, and correct the short-term wind power forecast values. The data collected from a wind farm in Hebei Province, China, are selected as a case study to demonstrate the effectiveness of the proposed

Failures and suggestions in Earthquake forecasting and prediction

Science.gov (United States)

Sacks, S. I.

2013-12-01

Seismologists have had poor success in earthquake prediction. However, wide ranging observations from earlier great earthquakes show that precursory data can exist. In particular, two aspects seem promising. In agreement with simple physical modeling, b-values decrease in highly loaded fault zones for years before failure. Potentially more usefully, in high stress regions, breakdown of dilatant patches leading to failure can yield expelled water-related observations. The volume increase (dilatancy) caused by high shear stresses decreases the pore pressure. Eventually, water flows back in restoring the pore pressure, promoting failure and expelling the extra water. Of course, in a generally stressed region there may be many small patches that fail, such as observed before the 1975 Haicheng earthquake. Only a few days before the major event will most of the dilatancy breakdown occur in the fault zone itself such as for the Tangshan, 1976 destructive event. Observations of 'water release' effects have been observed before the 1923 great Kanto earthquake, the 1984 Yamasaki event, the 1975 Haicheng and the 1976 Tangshan earthquakes and also the 1995 Kobe earthquake. While there are obvious difficulties in water release observations, not least because there is currently no observational network anywhere, historical data does suggest some promise if we broaden our approach to this difficult subject.

CSEP-Japan: The Japanese node of the collaboratory for the study of earthquake predictability

Science.gov (United States)

Yokoi, S.; Tsuruoka, H.; Nanjo, K.; Hirata, N.

2011-12-01

Collaboratory for the Study of Earthquake Predictability (CSEP) is a global project of earthquake predictability research. The final goal of this project is to have a look for the intrinsic predictability of the earthquake rupture process through forecast testing experiments. The Earthquake Research Institute, the University of Tokyo joined the CSEP and started the Japanese testing center called as CSEP-Japan. This testing center constitutes an open access to researchers contributing earthquake forecast models for applied to Japan. A total of 91 earthquake forecast models were submitted on the prospective experiment starting from 1 November 2009. The models are separated into 4 testing classes (1 day, 3 months, 1 year and 3 years) and 3 testing regions covering an area of Japan including sea area, Japanese mainland and Kanto district. We evaluate the performance of the models in the official suite of tests defined by the CSEP. The experiments of 1-day, 3-month, 1-year and 3-year forecasting classes were implemented for 92 rounds, 4 rounds, 1round and 0 round (now in progress), respectively. The results of the 3-month class gave us new knowledge concerning statistical forecasting models. All models showed a good performance for magnitude forecasting. On the other hand, observation is hardly consistent in space-distribution with most models in some cases where many earthquakes occurred at the same spot. Throughout the experiment, it has been clarified that some properties of the CSEP's evaluation tests such as the L-test show strong correlation with the N-test. We are now processing to own (cyber-) infrastructure to support the forecast experiment as follows. (1) Japanese seismicity has changed since the 2011 Tohoku earthquake. The 3rd call for forecasting models was announced in order to promote model improvement for forecasting earthquakes after this earthquake. So, we provide Japanese seismicity catalog maintained by JMA for modelers to study how seismicity

Earthquake data base for Romania

International Nuclear Information System (INIS)

Rizescu, M.; Ghica, D.; Grecu, B.; Popa, M.; Borcia, I. S.

2002-01-01

A new earthquake database for Romania is being constructed, comprising complete earthquake information and being up-to-date, user-friendly and rapidly accessible. One main component of the database consists from the catalog of earthquakes occurred in Romania since 984 up to present. The catalog contains information related to locations and other source parameters, when available, and links to waveforms of important earthquakes. The other very important component is the 'strong motion database', developed for strong intermediate-depth Vrancea earthquakes where instrumental data were recorded. Different parameters to characterize strong motion properties as: effective peak acceleration, effective peak velocity, corner periods T c and T d , global response spectrum based intensities were computed and recorded into this database. Also, information on the recording seismic stations as: maps giving their positioning, photographs of the instruments and site conditions ('free-field or on buildings) are included. By the huge volume and quality of gathered data, also by its friendly user interface, the Romania earthquake data base provides a very useful tool for geosciences and civil engineering in their effort towards reducing seismic risk in Romania. (authors)

A physics-based probabilistic forecasting model for rainfall-induced shallow landslides at regional scale

Science.gov (United States)

Zhang, Shaojie; Zhao, Luqiang; Delgado-Tellez, Ricardo; Bao, Hongjun

2018-03-01

Conventional outputs of physics-based landslide forecasting models are presented as deterministic warnings by calculating the safety factor (Fs) of potentially dangerous slopes. However, these models are highly dependent on variables such as cohesion force and internal friction angle which are affected by a high degree of uncertainty especially at a regional scale, resulting in unacceptable uncertainties of Fs. Under such circumstances, the outputs of physical models are more suitable if presented in the form of landslide probability values. In order to develop such models, a method to link the uncertainty of soil parameter values with landslide probability is devised. This paper proposes the use of Monte Carlo methods to quantitatively express uncertainty by assigning random values to physical variables inside a defined interval. The inequality Fs soil mechanical parameters and is used to create a physics-based probabilistic forecasting model for rainfall-induced shallow landslides. The prediction ability of this model was tested in a case study, in which simulated forecasting of landslide disasters associated with heavy rainfalls on 9 July 2013 in the Wenchuan earthquake region of Sichuan province, China, was performed. The proposed model successfully forecasted landslides in 159 of the 176 disaster points registered by the geo-environmental monitoring station of Sichuan province. Such testing results indicate that the new model can be operated in a highly efficient way and show more reliable results, attributable to its high prediction accuracy. Accordingly, the new model can be potentially packaged into a forecasting system for shallow landslides providing technological support for the mitigation of these disasters at regional scale.

Real Time Earthquake Information System in Japan

Science.gov (United States)

Doi, K.; Kato, T.

2003-12-01

An early earthquake notification system in Japan had been developed by the Japan Meteorological Agency (JMA) as a governmental organization responsible for issuing earthquake information and tsunami forecasts. The system was primarily developed for prompt provision of a tsunami forecast to the public with locating an earthquake and estimating its magnitude as quickly as possible. Years after, a system for a prompt provision of seismic intensity information as indices of degrees of disasters caused by strong ground motion was also developed so that concerned governmental organizations can decide whether it was necessary for them to launch emergency response or not. At present, JMA issues the following kinds of information successively when a large earthquake occurs. 1) Prompt report of occurrence of a large earthquake and major seismic intensities caused by the earthquake in about two minutes after the earthquake occurrence. 2) Tsunami forecast in around three minutes. 3) Information on expected arrival times and maximum heights of tsunami waves in around five minutes. 4) Information on a hypocenter and a magnitude of the earthquake, the seismic intensity at each observation station, the times of high tides in addition to the expected tsunami arrival times in 5-7 minutes. To issue information above, JMA has established; - An advanced nationwide seismic network with about 180 stations for seismic wave observation and about 3,400 stations for instrumental seismic intensity observation including about 2,800 seismic intensity stations maintained by local governments, - Data telemetry networks via landlines and partly via a satellite communication link, - Real-time data processing techniques, for example, the automatic calculation of earthquake location and magnitude, the database driven method for quantitative tsunami estimation, and - Dissemination networks, via computer-to-computer communications and facsimile through dedicated telephone lines. JMA operationally

Small discussion of electromagnetic wave anomalies preceding earthquakes

Energy Technology Data Exchange (ETDEWEB)

1980-01-01

Six brief pieces on various aspects of electromagnetic wave anomalies are presented. They cover: earthquake electromagnetic emanations; the use of magnetic induction information for earthquake forecasting; electromagnetic pulse emissions as pre-earthquake indicators; the use of magnetic sensors to determine medium-wavelength field strength for earthquake prediction purposes; magnetic deviation indicators inside reinforced-concrete buildings; and a discussion of the general physical principles involved.

Combining multiple earthquake models in real time for earthquake early warning

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Minson, Sarah E.; Wu, Stephen; Beck, James L; Heaton, Thomas H.

2017-01-01

The ultimate goal of earthquake early warning (EEW) is to provide local shaking information to users before the strong shaking from an earthquake reaches their location. This is accomplished by operating one or more real‐time analyses that attempt to predict shaking intensity, often by estimating the earthquake’s location and magnitude and then predicting the ground motion from that point source. Other EEW algorithms use finite rupture models or may directly estimate ground motion without first solving for an earthquake source. EEW performance could be improved if the information from these diverse and independent prediction models could be combined into one unified, ground‐motion prediction. In this article, we set the forecast shaking at each location as the common ground to combine all these predictions and introduce a Bayesian approach to creating better ground‐motion predictions. We also describe how this methodology could be used to build a new generation of EEW systems that provide optimal decisions customized for each user based on the user’s individual false‐alarm tolerance and the time necessary for that user to react.

Evaluating spatial and temporal relationships between an earthquake cluster near Entiat, central Washington, and the large December 1872 Entiat earthquake

Science.gov (United States)

Brocher, Thomas M.; Blakely, Richard J.; Sherrod, Brian

2017-01-01

We investigate spatial and temporal relations between an ongoing and prolific seismicity cluster in central Washington, near Entiat, and the 14 December 1872 Entiat earthquake, the largest historic crustal earthquake in Washington. A fault scarp produced by the 1872 earthquake lies within the Entiat cluster; the locations and areas of both the cluster and the estimated 1872 rupture surface are comparable. Seismic intensities and the 1–2 m of coseismic displacement suggest a magnitude range between 6.5 and 7.0 for the 1872 earthquake. Aftershock forecast models for (1) the first several hours following the 1872 earthquake, (2) the largest felt earthquakes from 1900 to 1974, and (3) the seismicity within the Entiat cluster from 1976 through 2016 are also consistent with this magnitude range. Based on this aftershock modeling, most of the current seismicity in the Entiat cluster could represent aftershocks of the 1872 earthquake. Other earthquakes, especially those with long recurrence intervals, have long‐lived aftershock sequences, including the Mw">MwMw 7.5 1891 Nobi earthquake in Japan, with aftershocks continuing 100 yrs after the mainshock. Although we do not rule out ongoing tectonic deformation in this region, a long‐lived aftershock sequence can account for these observations.

Earthquake forecast for the Wasatch Front region of the Intermountain West

Science.gov (United States)

DuRoss, Christopher B.

2016-04-18

The Working Group on Utah Earthquake Probabilities has assessed the probability of large earthquakes in the Wasatch Front region. There is a 43 percent probability of one or more magnitude 6.75 or greater earthquakes and a 57 percent probability of one or more magnitude 6.0 or greater earthquakes in the region in the next 50 years. These results highlight the threat of large earthquakes in the region.

Data base pertinent to earthquake design basis

International Nuclear Information System (INIS)

Sharma, R.D.

1988-01-01

Mitigation of earthquake risk from impending strong earthquakes is possible provided the hazard can be assessed, and translated into appropriate design inputs. This requires defining the seismic risk problem, isolating the risk factors and quantifying risk in terms of physical parameters, which are suitable for application in design. Like all other geological phenomena, past earthquakes hold the key to the understanding of future ones. Quantificatio n of seismic risk at a site calls for investigating the earthquake aspects of the site region and building a data base. The scope of such investigations is il lustrated in Figure 1 and 2. A more detailed definition of the earthquake problem in engineering design is given elsewhere (Sharma, 1987). The present document discusses the earthquake data base, which is required to support a seismic risk evaluation programme in the context of the existing state of the art. (author). 8 tables, 10 figs., 54 refs

The 1985 central chile earthquake: a repeat of previous great earthquakes in the region?

Science.gov (United States)

Comte, D; Eisenberg, A; Lorca, E; Pardo, M; Ponce, L; Saragoni, R; Singh, S K; Suárez, G

1986-07-25

A great earthquake (surface-wave magnitude, 7.8) occurred along the coast of central Chile on 3 March 1985, causing heavy damage to coastal towns. Intense foreshock activity near the epicenter of the main shock occurred for 11 days before the earthquake. The aftershocks of the 1985 earthquake define a rupture area of 170 by 110 square kilometers. The earthquake was forecast on the basis of the nearly constant repeat time (83 +/- 9 years) of great earthquakes in this region. An analysis of previous earthquakes suggests that the rupture lengths of great shocks in the region vary by a factor of about 3. The nearly constant repeat time and variable rupture lengths cannot be reconciled with time- or slip-predictable models of earthquake recurrence. The great earthquakes in the region seem to involve a variable rupture mode and yet, for unknown reasons, remain periodic. Historical data suggest that the region south of the 1985 rupture zone should now be considered a gap of high seismic potential that may rupture in a great earthquake in the next few tens of years.

Action-based flood forecasting for triggering humanitarian action

Science.gov (United States)

Coughlan de Perez, Erin; van den Hurk, Bart; van Aalst, Maarten K.; Amuron, Irene; Bamanya, Deus; Hauser, Tristan; Jongma, Brenden; Lopez, Ana; Mason, Simon; Mendler de Suarez, Janot; Pappenberger, Florian; Rueth, Alexandra; Stephens, Elisabeth; Suarez, Pablo; Wagemaker, Jurjen; Zsoter, Ervin

2016-09-01

Too often, credible scientific early warning information of increased disaster risk does not result in humanitarian action. With financial resources tilted heavily towards response after a disaster, disaster managers have limited incentive and ability to process complex scientific data, including uncertainties. These incentives are beginning to change, with the advent of several new forecast-based financing systems that provide funding based on a forecast of an extreme event. Given the changing landscape, here we demonstrate a method to select and use appropriate forecasts for specific humanitarian disaster prevention actions, even in a data-scarce location. This action-based forecasting methodology takes into account the parameters of each action, such as action lifetime, when verifying a forecast. Forecasts are linked with action based on an understanding of (1) the magnitude of previous flooding events and (2) the willingness to act "in vain" for specific actions. This is applied in the context of the Uganda Red Cross Society forecast-based financing pilot project, with forecasts from the Global Flood Awareness System (GloFAS). Using this method, we define the "danger level" of flooding, and we select the probabilistic forecast triggers that are appropriate for specific actions. Results from this methodology can be applied globally across hazards and fed into a financing system that ensures that automatic, pre-funded early action will be triggered by forecasts.

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)

Earthquake stand-by instruction device for nuclear power plant

International Nuclear Information System (INIS)

Nakagawa, Masaki; Ijima, Tadashi

1998-01-01

The magnitude of earthquakes is forecast at a high accuracy by disposing seismic detectors to a plurality of points distant from an object plant. The accuracy of the judgement for the magnitude of earthquakes can be improved by processing seismic movements by using seismic movements observed along with elapse of time. The measured seismic waveforms are always stored even during the processing time. With such procedures, when one processing is completed, processing can be conducted successively by using stored data, by which processing can be conducted by using all the data from the occurrence of the earthquakes. Then, the seismic movements can be estimated from an early stage of the occurrence of the earthquakes, and since the seismic movement can be judged based on a great amount of data with lapse of time, an appropriate stand-by instruction can be provided. (N.H.)

A review on remotely sensed land surface temperature anomaly as an earthquake precursor

Science.gov (United States)

Bhardwaj, Anshuman; Singh, Shaktiman; Sam, Lydia; Joshi, P. K.; Bhardwaj, Akanksha; Martín-Torres, F. Javier; Kumar, Rajesh

2017-12-01

The low predictability of earthquakes and the high uncertainty associated with their forecasts make earthquakes one of the worst natural calamities, capable of causing instant loss of life and property. Here, we discuss the studies reporting the observed anomalies in the satellite-derived Land Surface Temperature (LST) before an earthquake. We compile the conclusions of these studies and evaluate the use of remotely sensed LST anomalies as precursors of earthquakes. The arrival times and the amplitudes of the anomalies vary widely, thus making it difficult to consider them as universal markers to issue earthquake warnings. Based on the randomness in the observations of these precursors, we support employing a global-scale monitoring system to detect statistically robust anomalous geophysical signals prior to earthquakes before considering them as definite precursors.

A physics-based probabilistic forecasting model for rainfall-induced shallow landslides at regional scale

Directory of Open Access Journals (Sweden)

S. Zhang

2018-03-01

Full Text Available Conventional outputs of physics-based landslide forecasting models are presented as deterministic warnings by calculating the safety factor (Fs of potentially dangerous slopes. However, these models are highly dependent on variables such as cohesion force and internal friction angle which are affected by a high degree of uncertainty especially at a regional scale, resulting in unacceptable uncertainties of Fs. Under such circumstances, the outputs of physical models are more suitable if presented in the form of landslide probability values. In order to develop such models, a method to link the uncertainty of soil parameter values with landslide probability is devised. This paper proposes the use of Monte Carlo methods to quantitatively express uncertainty by assigning random values to physical variables inside a defined interval. The inequality Fs < 1 is tested for each pixel in n simulations which are integrated in a unique parameter. This parameter links the landslide probability to the uncertainties of soil mechanical parameters and is used to create a physics-based probabilistic forecasting model for rainfall-induced shallow landslides. The prediction ability of this model was tested in a case study, in which simulated forecasting of landslide disasters associated with heavy rainfalls on 9 July 2013 in the Wenchuan earthquake region of Sichuan province, China, was performed. The proposed model successfully forecasted landslides in 159 of the 176 disaster points registered by the geo-environmental monitoring station of Sichuan province. Such testing results indicate that the new model can be operated in a highly efficient way and show more reliable results, attributable to its high prediction accuracy. Accordingly, the new model can be potentially packaged into a forecasting system for shallow landslides providing technological support for the mitigation of these disasters at regional scale.

Spatial Distribution of the Coefficient of Variation for the Paleo-Earthquakes in Japan

Science.gov (United States)

Nomura, S.; Ogata, Y.

2015-12-01

Renewal processes, point prccesses in which intervals between consecutive events are independently and identically distributed, are frequently used to describe this repeating earthquake mechanism and forecast the next earthquakes. However, one of the difficulties in applying recurrent earthquake models is the scarcity of the historical data. Most studied fault segments have few, or only one observed earthquake that often have poorly constrained historic and/or radiocarbon ages. The maximum likelihood estimate from such a small data set can have a large bias and error, which tends to yield high probability for the next event in a very short time span when the recurrence intervals have similar lengths. On the other hand, recurrence intervals at a fault depend on the long-term slip rate caused by the tectonic motion in average. In addition, recurrence times are also fluctuated by nearby earthquakes or fault activities which encourage or discourage surrounding seismicity. These factors have spatial trends due to the heterogeneity of tectonic motion and seismicity. Thus, this paper introduces a spatial structure on the key parameters of renewal processes for recurrent earthquakes and estimates it by using spatial statistics. Spatial variation of mean and variance parameters of recurrence times are estimated in Bayesian framework and the next earthquakes are forecasted by Bayesian predictive distributions. The proposal model is applied for recurrent earthquake catalog in Japan and its result is compared with the current forecast adopted by the Earthquake Research Committee of Japan.

Research in historical earthquakes in the Korean peninsula and its circumferential regions

Institute of Scientific and Technical Information of China (English)

翟文杰; 吴戈; 韩绍欣

2004-01-01

@@ The historical earthquake data is one of the important foundations for seismic monitoring, earthquake fore-cast and seismic safety evaluation. However, the recognition of earthquake is limited by the scientific and techno-logical level. Therefore, the earthquake can only be described using perfect earthquake catalogue after the seismo-graph is invented. Before this time, the earthquake parameters were determined according to the earthquake disas-ter on the surface and the written records in history, and the earthquake level was measured using earthquake in-tensity.

A parimutuel gambling perspective to compare probabilistic seismicity forecasts

Science.gov (United States)

Zechar, J. Douglas; Zhuang, Jiancang

2014-10-01

Using analogies to gaming, we consider the problem of comparing multiple probabilistic seismicity forecasts. To measure relative model performance, we suggest a parimutuel gambling perspective which addresses shortcomings of other methods such as likelihood ratio, information gain and Molchan diagrams. We describe two variants of the parimutuel approach for a set of forecasts: head-to-head, in which forecasts are compared in pairs, and round table, in which all forecasts are compared simultaneously. For illustration, we compare the 5-yr forecasts of the Regional Earthquake Likelihood Models experiment for M4.95+ seismicity in California.

Geodetic Finite-Fault-based Earthquake Early Warning Performance for Great Earthquakes Worldwide

Science.gov (United States)

Ruhl, C. J.; Melgar, D.; Grapenthin, R.; Allen, R. M.

2017-12-01

GNSS-based earthquake early warning (EEW) algorithms estimate fault-finiteness and unsaturated moment magnitude for the largest, most damaging earthquakes. Because large events are infrequent, algorithms are not regularly exercised and insufficiently tested on few available datasets. The Geodetic Alarm System (G-larmS) is a GNSS-based finite-fault algorithm developed as part of the ShakeAlert EEW system in the western US. Performance evaluations using synthetic earthquakes offshore Cascadia showed that G-larmS satisfactorily recovers magnitude and fault length, providing useful alerts 30-40 s after origin time and timely warnings of ground motion for onshore urban areas. An end-to-end test of the ShakeAlert system demonstrated the need for GNSS data to accurately estimate ground motions in real-time. We replay real data from several subduction-zone earthquakes worldwide to demonstrate the value of GNSS-based EEW for the largest, most damaging events. We compare predicted ground acceleration (PGA) from first-alert-solutions with those recorded in major urban areas. In addition, where applicable, we compare observed tsunami heights to those predicted from the G-larmS solutions. We show that finite-fault inversion based on GNSS-data is essential to achieving the goals of EEW.

Time-dependent earthquake probability calculations for southern Kanto after the 2011 M9.0 Tohoku earthquake

Science.gov (United States)

Nanjo, K. Z.; Sakai, S.; Kato, A.; Tsuruoka, H.; Hirata, N.

2013-05-01

Seismicity in southern Kanto activated with the 2011 March 11 Tohoku earthquake of magnitude M9.0, but does this cause a significant difference in the probability of more earthquakes at the present or in the To? future answer this question, we examine the effect of a change in the seismicity rate on the probability of earthquakes. Our data set is from the Japan Meteorological Agency earthquake catalogue, downloaded on 2012 May 30. Our approach is based on time-dependent earthquake probabilistic calculations, often used for aftershock hazard assessment, and are based on two statistical laws: the Gutenberg-Richter (GR) frequency-magnitude law and the Omori-Utsu (OU) aftershock-decay law. We first confirm that the seismicity following a quake of M4 or larger is well modelled by the GR law with b ˜ 1. Then, there is good agreement with the OU law with p ˜ 0.5, which indicates that the slow decay was notably significant. Based on these results, we then calculate the most probable estimates of future M6-7-class events for various periods, all with a starting date of 2012 May 30. The estimates are higher than pre-quake levels if we consider a period of 3-yr duration or shorter. However, for statistics-based forecasting such as this, errors that arise from parameter estimation must be considered. Taking into account the contribution of these errors to the probability calculations, we conclude that any increase in the probability of earthquakes is insignificant. Although we try to avoid overstating the change in probability, our observations combined with results from previous studies support the likelihood that afterslip (fault creep) in southern Kanto will slowly relax a stress step caused by the Tohoku earthquake. This afterslip in turn reminds us of the potential for stress redistribution to the surrounding regions. We note the importance of varying hazards not only in time but also in space to improve the probabilistic seismic hazard assessment for southern Kanto.

Earthquake outlook for the San Francisco Bay region 2014–2043

Science.gov (United States)

Aagaard, Brad T.; Blair, James Luke; Boatwright, John; Garcia, Susan H.; Harris, Ruth A.; Michael, Andrew J.; Schwartz, David P.; DiLeo, Jeanne S.; Jacques, Kate; Donlin, Carolyn

2016-06-13

Using information from recent earthquakes, improved mapping of active faults, and a new model for estimating earthquake probabilities, the 2014 Working Group on California Earthquake Probabilities updated the 30-year earthquake forecast for California. They concluded that there is a 72 percent probability (or likelihood) of at least one earthquake of magnitude 6.7 or greater striking somewhere in the San Francisco Bay region before 2043. Earthquakes this large are capable of causing widespread damage; therefore, communities in the region should take simple steps to help reduce injuries, damage, and disruption, as well as accelerate recovery from these earthquakes.

Satellite based Ocean Forecasting, the SOFT project

Science.gov (United States)

Stemmann, L.; Tintoré, J.; Moneris, S.

2003-04-01

The knowledge of future oceanic conditions would have enormous impact on human marine related areas. For such reasons, a number of international efforts are being carried out to obtain reliable and manageable ocean forecasting systems. Among the possible techniques that can be used to estimate the near future states of the ocean, an ocean forecasting system based on satellite imagery is developped through the Satelitte based Ocean ForecasTing project (SOFT). SOFT, established by the European Commission, considers the development of a forecasting system of the ocean space-time variability based on satellite data by using Artificial Intelligence techniques. This system will be merged with numerical simulation approaches, via assimilation techniques, to get a hybrid SOFT-numerical forecasting system of improved performance. The results of the project will provide efficient forecasting of sea-surface temperature structures, currents, dynamic height, and biological activity associated to chlorophyll fields. All these quantities could give valuable information on the planning and management of human activities in marine environments such as navigation, fisheries, pollution control, or coastal management. A detailed identification of present or new needs and potential end-users concerned by such an operational tool is being performed. The project would study solutions adapted to these specific needs.

Stigma in science: the case of earthquake prediction.

Science.gov (United States)

Joffe, Helene; Rossetto, Tiziana; Bradley, Caroline; O'Connor, Cliodhna

2018-01-01

This paper explores how earthquake scientists conceptualise earthquake prediction, particularly given the conviction of six earthquake scientists for manslaughter (subsequently overturned) on 22 October 2012 for having given inappropriate advice to the public prior to the L'Aquila earthquake of 6 April 2009. In the first study of its kind, semi-structured interviews were conducted with 17 earthquake scientists and the transcribed interviews were analysed thematically. The scientists primarily denigrated earthquake prediction, showing strong emotive responses and distancing themselves from earthquake 'prediction' in favour of 'forecasting'. Earthquake prediction was regarded as impossible and harmful. The stigmatisation of the subject is discussed in the light of research on boundary work and stigma in science. The evaluation reveals how mitigation becomes the more favoured endeavour, creating a normative environment that disadvantages those who continue to pursue earthquake prediction research. Recommendations are made for communication with the public on earthquake risk, with a focus on how scientists portray uncertainty. © 2018 The Author(s). Disasters © Overseas Development Institute, 2018.

Operational forecasting based on a modified Weather Research and Forecasting model

Energy Technology Data Exchange (ETDEWEB)

Lundquist, J; Glascoe, L; Obrecht, J

2010-03-18

Accurate short-term forecasts of wind resources are required for efficient wind farm operation and ultimately for the integration of large amounts of wind-generated power into electrical grids. Siemens Energy Inc. and Lawrence Livermore National Laboratory, with the University of Colorado at Boulder, are collaborating on the design of an operational forecasting system for large wind farms. The basis of the system is the numerical weather prediction tool, the Weather Research and Forecasting (WRF) model; large-eddy simulations and data assimilation approaches are used to refine and tailor the forecasting system. Representation of the atmospheric boundary layer is modified, based on high-resolution large-eddy simulations of the atmospheric boundary. These large-eddy simulations incorporate wake effects from upwind turbines on downwind turbines as well as represent complex atmospheric variability due to complex terrain and surface features as well as atmospheric stability. Real-time hub-height wind speed and other meteorological data streams from existing wind farms are incorporated into the modeling system to enable uncertainty quantification through probabilistic forecasts. A companion investigation has identified optimal boundary-layer physics options for low-level forecasts in complex terrain, toward employing decadal WRF simulations to anticipate large-scale changes in wind resource availability due to global climate change.

E-DECIDER Decision Support Gateway For Earthquake Disaster Response

Science.gov (United States)

Glasscoe, M. T.; Stough, T. M.; Parker, J. W.; Burl, M. C.; Donnellan, A.; Blom, R. G.; Pierce, M. E.; Wang, J.; Ma, Y.; Rundle, J. B.; Yoder, M. R.

2013-12-01

Earthquake Data Enhanced Cyber-Infrastructure for Disaster Evaluation and Response (E-DECIDER) is a NASA-funded project developing capabilities for decision-making utilizing remote sensing data and modeling software in order to provide decision support for earthquake disaster management and response. E-DECIDER incorporates earthquake forecasting methodology and geophysical modeling tools developed through NASA's QuakeSim project in order to produce standards-compliant map data products to aid in decision-making following an earthquake. Remote sensing and geodetic data, in conjunction with modeling and forecasting tools, help provide both long-term planning information for disaster management decision makers as well as short-term information following earthquake events (i.e. identifying areas where the greatest deformation and damage has occurred and emergency services may need to be focused). E-DECIDER utilizes a service-based GIS model for its cyber-infrastructure in order to produce standards-compliant products for different user types with multiple service protocols (such as KML, WMS, WFS, and WCS). The goal is to make complex GIS processing and domain-specific analysis tools more accessible to general users through software services as well as provide system sustainability through infrastructure services. The system comprises several components, which include: a GeoServer for thematic mapping and data distribution, a geospatial database for storage and spatial analysis, web service APIs, including simple-to-use REST APIs for complex GIS functionalities, and geoprocessing tools including python scripts to produce standards-compliant data products. These are then served to the E-DECIDER decision support gateway (http://e-decider.org), the E-DECIDER mobile interface, and to the Department of Homeland Security decision support middleware UICDS (Unified Incident Command and Decision Support). The E-DECIDER decision support gateway features a web interface that

Earthquake insurance pricing: a risk-based approach.

Science.gov (United States)

Lin, Jeng-Hsiang

2018-04-01

Flat earthquake premiums are 'uniformly' set for a variety of buildings in many countries, neglecting the fact that the risk of damage to buildings by earthquakes is based on a wide range of factors. How these factors influence the insurance premiums is worth being studied further. Proposed herein is a risk-based approach to estimate the earthquake insurance rates of buildings. Examples of application of the approach to buildings located in Taipei city of Taiwan were examined. Then, the earthquake insurance rates for the buildings investigated were calculated and tabulated. To fulfil insurance rating, the buildings were classified into 15 model building types according to their construction materials and building height. Seismic design levels were also considered in insurance rating in response to the effect of seismic zone and construction years of buildings. This paper may be of interest to insurers, actuaries, and private and public sectors of insurance. © 2018 The Author(s). Disasters © Overseas Development Institute, 2018.

Earthquake Prediction in a Big Data World

Science.gov (United States)

Kossobokov, V. G.

2016-12-01

The digital revolution started just about 15 years ago has already surpassed the global information storage capacity of more than 5000 Exabytes (in optimally compressed bytes) per year. Open data in a Big Data World provides unprecedented opportunities for enhancing studies of the Earth System. However, it also opens wide avenues for deceptive associations in inter- and transdisciplinary data and for inflicted misleading predictions based on so-called "precursors". Earthquake prediction is not an easy task that implies a delicate application of statistics. So far, none of the proposed short-term precursory signals showed sufficient evidence to be used as a reliable precursor of catastrophic earthquakes. Regretfully, in many cases of seismic hazard assessment (SHA), from term-less to time-dependent (probabilistic PSHA or deterministic DSHA), and short-term earthquake forecasting (StEF), the claims of a high potential of the method are based on a flawed application of statistics and, therefore, are hardly suitable for communication to decision makers. Self-testing must be done in advance claiming prediction of hazardous areas and/or times. The necessity and possibility of applying simple tools of Earthquake Prediction Strategies, in particular, Error Diagram, introduced by G.M. Molchan in early 1990ies, and Seismic Roulette null-hypothesis as a metric of the alerted space, is evident. The set of errors, i.e. the rates of failure and of the alerted space-time volume, can be easily compared to random guessing, which comparison permits evaluating the SHA method effectiveness and determining the optimal choice of parameters in regard to a given cost-benefit function. These and other information obtained in such a simple testing may supply us with a realistic estimates of confidence and accuracy of SHA predictions and, if reliable but not necessarily perfect, with related recommendations on the level of risks for decision making in regard to engineering design, insurance

Multi-step wind speed forecasting based on a hybrid forecasting architecture and an improved bat algorithm

International Nuclear Information System (INIS)

Xiao, Liye; Qian, Feng; Shao, Wei

2017-01-01

Highlights: • Propose a hybrid architecture based on a modified bat algorithm for multi-step wind speed forecasting. • Improve the accuracy of multi-step wind speed forecasting. • Modify bat algorithm with CG to improve optimized performance. - Abstract: As one of the most promising sustainable energy sources, wind energy plays an important role in energy development because of its cleanliness without causing pollution. Generally, wind speed forecasting, which has an essential influence on wind power systems, is regarded as a challenging task. Analyses based on single-step wind speed forecasting have been widely used, but their results are insufficient in ensuring the reliability and controllability of wind power systems. In this paper, a new forecasting architecture based on decomposing algorithms and modified neural networks is successfully developed for multi-step wind speed forecasting. Four different hybrid models are contained in this architecture, and to further improve the forecasting performance, a modified bat algorithm (BA) with the conjugate gradient (CG) method is developed to optimize the initial weights between layers and thresholds of the hidden layer of neural networks. To investigate the forecasting abilities of the four models, the wind speed data collected from four different wind power stations in Penglai, China, were used as a case study. The numerical experiments showed that the hybrid model including the singular spectrum analysis and general regression neural network with CG-BA (SSA-CG-BA-GRNN) achieved the most accurate forecasting results in one-step to three-step wind speed forecasting.

Temporal distribution of earthquakes using renewal process in the Dasht-e-Bayaz region

Science.gov (United States)

Mousavi, Mehdi; Salehi, Masoud

2018-01-01

Temporal distribution of earthquakes with M w > 6 in the Dasht-e-Bayaz region, eastern Iran has been investigated using time-dependent models. Based on these types of models, it is assumed that the times between consecutive large earthquakes follow a certain statistical distribution. For this purpose, four time-dependent inter-event distributions including the Weibull, Gamma, Lognormal, and the Brownian Passage Time (BPT) are used in this study and the associated parameters are estimated using the method of maximum likelihood estimation. The suitable distribution is selected based on logarithm likelihood function and Bayesian Information Criterion. The probability of the occurrence of the next large earthquake during a specified interval of time was calculated for each model. Then, the concept of conditional probability has been applied to forecast the next major ( M w > 6) earthquake in the site of our interest. The emphasis is on statistical methods which attempt to quantify the probability of an earthquake occurring within a specified time, space, and magnitude windows. According to obtained results, the probability of occurrence of an earthquake with M w > 6 in the near future is significantly high.

Modeling fast and slow earthquakes at various scales.

Science.gov (United States)

Ide, Satoshi

2014-01-01

Earthquake sources represent dynamic rupture within rocky materials at depth and often can be modeled as propagating shear slip controlled by friction laws. These laws provide boundary conditions on fault planes embedded in elastic media. Recent developments in observation networks, laboratory experiments, and methods of data analysis have expanded our knowledge of the physics of earthquakes. Newly discovered slow earthquakes are qualitatively different phenomena from ordinary fast earthquakes and provide independent information on slow deformation at depth. Many numerical simulations have been carried out to model both fast and slow earthquakes, but problems remain, especially with scaling laws. Some mechanisms are required to explain the power-law nature of earthquake rupture and the lack of characteristic length. Conceptual models that include a hierarchical structure over a wide range of scales would be helpful for characterizing diverse behavior in different seismic regions and for improving probabilistic forecasts of earthquakes.

Equipment for the forecast and operating control of the natural catastrophes dynamics

International Nuclear Information System (INIS)

Kazansev, S.I.; Koblik, Yu.N.; Yuldashev, B.S.; Kuzhevskij, B.M.; Nechaev, O.Yu.

2004-01-01

Full text: Necessity of the solution of forecast the natural catastrophes such as: earthquakes, the volcanic activity stirring up, origin of the mud flows, landslides, sudden moving of pulsating glaciers, awesome atmospheric phenomena is realized a long time ago. However numerous attempts to find the solution yet have not yielded desirable result. Representative example to this is the absence to the present time of the physically justified earthquake forecast. Perennial observations of the Earth neutron field with the help of designed by us equipment, both in seismically active regions (Tien - Shan, Pamir, Kamchatka) and in seismically quiet regions (Moscow and Tver regions) demonstrated, that the variations of neutron radiation near the earth crust allow us to elaborate near-term (tens hours, some day) the earthquakes forecast. These researches have also revealed correlation between variations of neutron and magnetic fields of the Earth. In this connection it is represented, that a hardware complex permitting simultaneously to watch variations of a neutron radiation near the earth crust and variation of a magnetic field of the Earth will appear sufficient for the purposes of a short forecast of natural debacles and, besides will allow us to execute the operating control of temporary development of already happened event

Equipment for the forecast and operating control of the natural catastrophes dynamics

International Nuclear Information System (INIS)

Kazantsev, S.I.; Koblik, Yu.N.; Yuldashev, B.S.; Kuzhevskij, B.M.; Nechaev, O.Yu.

2004-01-01

Necessity of the solution of forecast the natural catastrophes such as: earthquakes, the volcanic activity stirring up, origin of the mud flows, landslides, sudden moving of pulsating glaciers, awesome atmospheric phenomena is realized a long time ago. However numerous attempts to find the solution yet have not yielded desirable result. Representative example to this is the absence to the present time of the physically justified earthquake forecast. Perennial observations of the Earth neutron field with the help of designed by us equipment, both in seismically active regions (Tien - Shan, Pamir, Kamchatka) and in seismically quiet regions (Moscow and Tver regions) demonstrated, that the variations of neutron radiation near the earth crust allow us to elaborate near-term (tens hours, some day) the earthquakes forecast. These researches have also revealed correlation between variations of neutron and magnetic fields of the Earth. In this connection it is represented, that a hardware complex permitting simultaneously to watch variations of a neutron radiation near the earth crust and variation of a magnetic field of the Earth will appear sufficient for the purposes of a short forecast of natural debacles and, besides will allow us to execute the operating control of temporary development of already happened event. (author)

Statistics and Analysis of the Relations between Rainstorm Floods and Earthquakes

Directory of Open Access Journals (Sweden)

Baodeng Hou

2016-01-01

Full Text Available The frequent occurrence of geophysical disasters under climate change has drawn Chinese scholars to pay their attention to disaster relations. If the occurrence sequence of disasters could be identified, long-term disaster forecast could be realized. Based on the Earth Degassing Effect (EDE which is valid, this paper took the magnitude, epicenter, and occurrence time of the earthquake, as well as the epicenter and occurrence time of the rainstorm floods as basic factors to establish an integrated model to study the correlation between rainstorm floods and earthquakes. 2461 severe earthquakes occurred in China or within 3000 km from China and the 169 heavy rainstorm floods occurred in China over the past 200+ years as the input data of the model. The computational results showed that although most of the rainstorm floods have nothing to do with the severe earthquakes from a statistical perspective, some floods might relate to earthquakes. This is especially true when the earthquakes happen in the vapor transmission zone where rainstorms lead to abundant water vapors. In this regard, earthquakes are more likely to cause big rainstorm floods. However, many cases of rainstorm floods could be found after severe earthquakes with a large extent of uncertainty.

Earthquake response analysis of a base isolated building

International Nuclear Information System (INIS)

Mazda, T.; Shiojiri, H.; Sawada, Y.; Harada, O.; Kawai, N.; Ontsuka, S.

1989-01-01

Recently, the seismic isolation has become one of the popular methods in the design of important structures or equipments against the earthquakes. However, it is desired to accumulate the demonstration data on reliability of seismically isolated structures and to establish the analysis methods of those structures. Based on the above recognition, the vibration tests of a base isolated building were carried out in Tsukuba Science City. After that, many earthquake records have been obtained at the building. In order to examine the validity of numerical models, earthquake response analyses have been executed by using both lumped mass model, and finite element model

New fault picture points toward San Francisco Bay area earthquakes

Science.gov (United States)

Kerr, R. A.

1989-01-01

Recent earthquakes and a new way of looking at faults suggest that damaging earthquakes are closing in on the San Francisco area. Earthquakes Awareness Week 1989 in northern California started off with a bang on Monday, 3 April, when a magnitude 4.8 earthquake struck 15 kilometers northeast of San Jose. The relatively small shock-its primary damage was the shattering of an air-control tower window-got the immediate attention of three U.S Geological Survey seismologists in Menlo Park near San Francisco. David Oppenheimer, William Bakun, and Allan Lindh had forecast a nearby earthquake in a just completed report, and this, they thought, might be it. 

Generalized statistical mechanics approaches to earthquakes and tectonics

Science.gov (United States)

Papadakis, Giorgos; Michas, Georgios

2016-01-01

Despite the extreme complexity that characterizes the mechanism of the earthquake generation process, simple empirical scaling relations apply to the collective properties of earthquakes and faults in a variety of tectonic environments and scales. The physical characterization of those properties and the scaling relations that describe them attract a wide scientific interest and are incorporated in the probabilistic forecasting of seismicity in local, regional and planetary scales. Considerable progress has been made in the analysis of the statistical mechanics of earthquakes, which, based on the principle of entropy, can provide a physical rationale to the macroscopic properties frequently observed. The scale-invariant properties, the (multi) fractal structures and the long-range interactions that have been found to characterize fault and earthquake populations have recently led to the consideration of non-extensive statistical mechanics (NESM) as a consistent statistical mechanics framework for the description of seismicity. The consistency between NESM and observations has been demonstrated in a series of publications on seismicity, faulting, rock physics and other fields of geosciences. The aim of this review is to present in a concise manner the fundamental macroscopic properties of earthquakes and faulting and how these can be derived by using the notions of statistical mechanics and NESM, providing further insights into earthquake physics and fault growth processes. PMID:28119548

Development of Real-time Tsunami Inundation Forecast Using Ocean Bottom Tsunami Networks along the Japan Trench

Science.gov (United States)

Aoi, S.; Yamamoto, N.; Suzuki, W.; Hirata, K.; Nakamura, H.; Kunugi, T.; Kubo, T.; Maeda, T.

2015-12-01

In the 2011 Tohoku earthquake, in which huge tsunami claimed a great deal of lives, the initial tsunami forecast based on hypocenter information estimated using seismic data on land were greatly underestimated. From this lesson, NIED is now constructing S-net (Seafloor Observation Network for Earthquakes and Tsunamis along the Japan Trench) which consists of 150 ocean bottom observatories with seismometers and pressure gauges (tsunamimeters) linked by fiber optic cables. To take full advantage of S-net, we develop a new methodology of real-time tsunami inundation forecast using ocean bottom observation data and construct a prototype system that implements the developed forecasting method for the Pacific coast of Chiba prefecture (Sotobo area). We employ a database-based approach because inundation is a strongly non-linear phenomenon and its calculation costs are rather heavy. We prepare tsunami scenario bank in advance, by constructing the possible tsunami sources, and calculating the tsunami waveforms at S-net stations, coastal tsunami heights and tsunami inundation on land. To calculate the inundation for target Sotobo area, we construct the 10-m-mesh precise elevation model with coastal structures. Based on the sensitivities analyses, we construct the tsunami scenario bank that efficiently covers possible tsunami scenarios affecting the Sotobo area. A real-time forecast is carried out by selecting several possible scenarios which can well explain real-time tsunami data observed at S-net from tsunami scenario bank. An advantage of our method is that tsunami inundations are estimated directly from the actual tsunami data without any source information, which may have large estimation errors. In addition to the forecast system, we develop Web services, APIs, and smartphone applications and brush them up through social experiments to provide the real-time tsunami observation and forecast information in easy way to understand toward urging people to evacuate.

An interdisciplinary approach to study Pre-Earthquake processes

Science.gov (United States)

Ouzounov, D.; Pulinets, S. A.; Hattori, K.; Taylor, P. T.

2017-12-01

We will summarize a multi-year research effort on wide-ranging observations of pre-earthquake processes. Based on space and ground data we present some new results relevant to the existence of pre-earthquake signals. Over the past 15-20 years there has been a major revival of interest in pre-earthquake studies in Japan, Russia, China, EU, Taiwan and elsewhere. Recent large magnitude earthquakes in Asia and Europe have shown the importance of these various studies in the search for earthquake precursors either for forecasting or predictions. Some new results were obtained from modeling of the atmosphere-ionosphere connection and analyses of seismic records (foreshocks /aftershocks), geochemical, electromagnetic, and thermodynamic processes related to stress changes in the lithosphere, along with their statistical and physical validation. This cross - disciplinary approach could make an impact on our further understanding of the physics of earthquakes and the phenomena that precedes their energy release. We also present the potential impact of these interdisciplinary studies to earthquake predictability. A detail summary of our approach and that of several international researchers will be part of this session and will be subsequently published in a new AGU/Wiley volume. This book is part of the Geophysical Monograph series and is intended to show the variety of parameters seismic, atmospheric, geochemical and historical involved is this important field of research and will bring this knowledge and awareness to a broader geosciences community.

Quasi-periodic recurrence of large earthquakes on the southern San Andreas fault

Science.gov (United States)

Scharer, Katherine M.; Biasi, Glenn P.; Weldon, Ray J.; Fumal, Tom E.

2010-01-01

It has been 153 yr since the last large earthquake on the southern San Andreas fault (California, United States), but the average interseismic interval is only ~100 yr. If the recurrence of large earthquakes is periodic, rather than random or clustered, the length of this period is notable and would generally increase the risk estimated in probabilistic seismic hazard analyses. Unfortunately, robust characterization of a distribution describing earthquake recurrence on a single fault is limited by the brevity of most earthquake records. Here we use statistical tests on a 3000 yr combined record of 29 ground-rupturing earthquakes from Wrightwood, California. We show that earthquake recurrence there is more regular than expected from a Poisson distribution and is not clustered, leading us to conclude that recurrence is quasi-periodic. The observation of unimodal time dependence is persistent across an observationally based sensitivity analysis that critically examines alternative interpretations of the geologic record. The results support formal forecast efforts that use renewal models to estimate probabilities of future earthquakes on the southern San Andreas fault. Only four intervals (15%) from the record are longer than the present open interval, highlighting the current hazard posed by this fault.

Does an inter-flaw length control the accuracy of rupture forecasting in geological materials?

Science.gov (United States)

Vasseur, Jérémie; Wadsworth, Fabian B.; Heap, Michael J.; Main, Ian G.; Lavallée, Yan; Dingwell, Donald B.

2017-10-01

Multi-scale failure of porous materials is an important phenomenon in nature and in material physics - from controlled laboratory tests to rockbursts, landslides, volcanic eruptions and earthquakes. A key unsolved research question is how to accurately forecast the time of system-sized catastrophic failure, based on observations of precursory events such as acoustic emissions (AE) in laboratory samples, or, on a larger scale, small earthquakes. Until now, the length scale associated with precursory events has not been well quantified, resulting in forecasting tools that are often unreliable. Here we test the hypothesis that the accuracy of the forecast failure time depends on the inter-flaw distance in the starting material. We use new experimental datasets for the deformation of porous materials to infer the critical crack length at failure from a static damage mechanics model. The style of acceleration of AE rate prior to failure, and the accuracy of forecast failure time, both depend on whether the cracks can span the inter-flaw length or not. A smooth inverse power-law acceleration of AE rate to failure, and an accurate forecast, occurs when the cracks are sufficiently long to bridge pore spaces. When this is not the case, the predicted failure time is much less accurate and failure is preceded by an exponential AE rate trend. Finally, we provide a quantitative and pragmatic correction for the systematic error in the forecast failure time, valid for structurally isotropic porous materials, which could be tested against larger-scale natural failure events, with suitable scaling for the relevant inter-flaw distances.

Environmental noise forecasting based on support vector machine

Science.gov (United States)

Fu, Yumei; Zan, Xinwu; Chen, Tianyi; Xiang, Shihan

2018-01-01

As an important pollution source, the noise pollution is always the researcher's focus. Especially in recent years, the noise pollution is seriously harmful to the human beings' environment, so the research about the noise pollution is a very hot spot. Some noise monitoring technologies and monitoring systems are applied in the environmental noise test, measurement and evaluation. But, the research about the environmental noise forecasting is weak. In this paper, a real-time environmental noise monitoring system is introduced briefly. This monitoring system is working in Mianyang City, Sichuan Province. It is monitoring and collecting the environmental noise about more than 20 enterprises in this district. Based on the large amount of noise data, the noise forecasting by the Support Vector Machine (SVM) is studied in detail. Compared with the time series forecasting model and the artificial neural network forecasting model, the SVM forecasting model has some advantages such as the smaller data size, the higher precision and stability. The noise forecasting results based on the SVM can provide the important and accuracy reference to the prevention and control of the environmental noise.

Assessing Lay Understanding of Common Presentations of Earthquake Hazard Information

Science.gov (United States)

Thompson, K. J.; Krantz, D. H.

2010-12-01

The Working Group on California Earthquake Probabilities (WGCEP) includes, in its introduction to earthquake rupture forecast maps, the assertion that "In daily living, people are used to making decisions based on probabilities -- from the flip of a coin (50% probability of heads) to weather forecasts (such as a 30% chance of rain) to the annual chance of being killed by lightning (about 0.0003%)." [3] However, psychology research identifies a large gap between lay and expert perception of risk for various hazards [2], and cognitive psychologists have shown in numerous studies [1,4-6] that people neglect, distort, misjudge, or misuse probabilities, even when given strong guidelines about the meaning of numerical or verbally stated probabilities [7]. The gap between lay and expert use of probability needs to be recognized more clearly by scientific organizations such as WGCEP. This study undertakes to determine how the lay public interprets earthquake hazard information, as presented in graphical map form by the Uniform California Earthquake Rupture Forecast (UCERF), compiled by the WGCEP and other bodies including the USGS and CGS. It also explores alternate ways of presenting hazard data, to determine which presentation format most effectively translates information from scientists to public. Participants both from California and from elsewhere in the United States are included, to determine whether familiarity -- either with the experience of an earthquake, or with the geography of the forecast area -- affects people's ability to interpret an earthquake hazards map. We hope that the comparisons between the interpretations by scientific experts and by different groups of laypeople will both enhance theoretical understanding of factors that affect information transmission and assist bodies such as the WGCEP in their laudable attempts to help people prepare themselves and their communities for possible natural hazards. [1] Kahneman, D & Tversky, A (1979). Prospect

The seismic cycles of large Romanian earthquake: The physical foundation, and the next large earthquake in Vrancea

International Nuclear Information System (INIS)

Purcaru, G.

2002-01-01

The occurrence patterns of large/great earthquakes at subduction zone interface and in-slab are complex in the space-time dynamics, and make even long-term forecasts very difficult. For some favourable cases where a predictive (empirical) law was found successful predictions were possible (eg. Aleutians, Kuriles, etc). For the large Romanian events (M > 6.7), occurring in the Vrancea seismic slab below 60 km, Purcaru (1974) first found the law of the occurrence time and magnitude: the law of 'quasicycles' and 'supercycles', for large and largest events (M > 7.25), respectively. The quantitative model of Purcaru with these seismic cycles has three time-bands (periods of large earthquakes)/century, discovered using the earthquake history (1100-1973) (however incomplete) of large Vrancea earthquakes for which M was initially estimated (Purcaru, 1974, 1979). Our long-term prediction model is essentially quasideterministic, it predicts uniquely the time and magnitude; since is not strict deterministic the forecasting is interval valued. It predicted the next large earthquake in 1980 in the 3rd time-band (1970-1990), and which occurred in 1977 (M7.1, M w 7.5). The prediction was successful, in long-term sense. We discuss the unpredicted events in 1986 and 1990. Since the laws are phenomenological, we give their physical foundation based on the large scale of rupture zone (RZ) and subscale of the rupture process (RP). First results show that: (1) the 1940 event (h=122 km) ruptured the lower part of the oceanic slab entirely along strike, and down dip, and similarly for 1977 but its upper part, (2) the RZ of 1977 and 1990 events overlap and the first asperity of 1977 event was rebroken in 1990. This shows the size of the events strongly depends on RZ, asperity size/strength and, thus on the failure stress level (FSL), but not on depth, (3) when FSL of high strength (HS) larger zones is critical largest events (eg. 1802, 1940) occur, thus explaining the supercyles (the 1940

Use of high resolution satellite images for tracking of changes in the lineament structure, caused by earthquakes

OpenAIRE

Arellano-Baeza, A. A.; Garcia, R. V.; Trejo-Soto, M.

2007-01-01

Over the last decades strong efforts have been made to apply new spaceborn technologies to the study and possible forecast of strong earthquakes. In this study we use ASTER/TERRA multispectral satellite images for detection and analysis of changes in the system of lineaments previous to a strong earthquake. A lineament is a straight or a somewhat curved feature in an image, which it is possible to detect by a special processing of images based on directional filtering and or Hough transform. ...

An Earthquake Prediction System Using The Time Series Analyses of Earthquake Property And Crust Motion

International Nuclear Information System (INIS)

Takeda, Fumihide; Takeo, Makoto

2004-01-01

We have developed a short-term deterministic earthquake (EQ) forecasting system similar to those used for Typhoons and Hurricanes, which has been under a test operation at website http://www.tec21.jp/ since June of 2003. We use the focus and crust displacement data recently opened to the public by Japanese seismograph and global positioning system (GPS) networks, respectively. Our system divides the forecasting area into the five regional areas of Japan, each of which is about 5 deg. by 5 deg. We have found that it can forecast the focus, date of occurrence and magnitude (M) of an impending EQ (whose M is larger than about 6), all within narrow limits. We have two examples to describe the system. One is the 2003/09/26 EQ of M 8 in the Hokkaido area, which is of hindsight. Another is a successful rollout of the most recent forecast on the 2004/05/30 EQ of M 6.7 off coast of the southern Kanto (Tokyo) area

Statistical validation of earthquake related observations

Science.gov (United States)

Kossobokov, V. G.

2011-12-01

The confirmed fractal nature of earthquakes and their distribution in space and time implies that many traditional estimations of seismic hazard (from term-less to short-term ones) are usually based on erroneous assumptions of easy tractable or, conversely, delicately-designed models. The widespread practice of deceptive modeling considered as a "reasonable proxy" of the natural seismic process leads to seismic hazard assessment of unknown quality, which errors propagate non-linearly into inflicted estimates of risk and, eventually, into unexpected societal losses of unacceptable level. The studies aimed at forecast/prediction of earthquakes must include validation in the retro- (at least) and, eventually, in prospective tests. In the absence of such control a suggested "precursor/signal" remains a "candidate", which link to target seismic event is a model assumption. Predicting in advance is the only decisive test of forecast/predictions and, therefore, the score-card of any "established precursor/signal" represented by the empirical probabilities of alarms and failures-to-predict achieved in prospective testing must prove statistical significance rejecting the null-hypothesis of random coincidental occurrence in advance target earthquakes. We reiterate suggesting so-called "Seismic Roulette" null-hypothesis as the most adequate undisturbed random alternative accounting for the empirical spatial distribution of earthquakes: (i) Consider a roulette wheel with as many sectors as the number of earthquake locations from a sample catalog representing seismic locus, a sector per each location and (ii) make your bet according to prediction (i.e., determine, which locations are inside area of alarm, and put one chip in each of the corresponding sectors); (iii) Nature turns the wheel; (iv) accumulate statistics of wins and losses along with the number of chips spent. If a precursor in charge of prediction exposes an imperfection of Seismic Roulette then, having in mind

Evaluating real-time air-quality data as earthquake indicator

International Nuclear Information System (INIS)

Hsu, Shih-Chieh; Huang, Yi-Tang; Huang, Jr-Chung; Tu, Jien-Yi; Engling, Guenter; Lin, Chuan-Yao; Lin, Fei-Jan; Huang, Chao-Hao

2010-01-01

A catastrophic earthquake, namely the 921-earthquake, occurred with a magnitude of M L = 7.3 in Taiwan on September 21, 1999, causing severe disaster. The evaluation of real-time air-quality data, obtained by the Taiwan Environmental Protection Administration (EPA), revealed a staggering increase in ambient SO 2 concentrations by more than one order of magnitude across the island several hours prior to the earthquake, particularly at background stations. The abrupt increase in SO 2 concentrations likely resulted from seismic-triggered degassing instead of air pollution. An additional case of a large earthquake (M L = 6.8), occurring on March 31, 2002, was examined to confirm our observations of significantly enhanced SO 2 concentrations in ambient air prior to large earthquakes. The coincidence between large earthquakes and increases in trace gases during the pre-quake period (several hours) indicates the potential of employing air-quality monitoring data to forecast catastrophic earthquakes.

Study of Earthquake Disaster Prediction System of Langfang city Based on GIS

Science.gov (United States)

Huang, Meng; Zhang, Dian; Li, Pan; Zhang, YunHui; Zhang, RuoFei

2017-07-01

In this paper, according to the status of China’s need to improve the ability of earthquake disaster prevention, this paper puts forward the implementation plan of earthquake disaster prediction system of Langfang city based on GIS. Based on the GIS spatial database, coordinate transformation technology, GIS spatial analysis technology and PHP development technology, the seismic damage factor algorithm is used to predict the damage of the city under different intensity earthquake disaster conditions. The earthquake disaster prediction system of Langfang city is based on the B / S system architecture. Degree and spatial distribution and two-dimensional visualization display, comprehensive query analysis and efficient auxiliary decision-making function to determine the weak earthquake in the city and rapid warning. The system has realized the transformation of the city’s earthquake disaster reduction work from static planning to dynamic management, and improved the city’s earthquake and disaster prevention capability.

DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA

Directory of Open Access Journals (Sweden)

J. Rhee

2016-06-01

Full Text Available The reduction of drought impacts may be achieved through sustainable drought management and proactive measures against drought disaster. Accurate and timely provision of drought information is essential. In this study, drought forecasting models to provide high-resolution drought information based on drought indicators for ungauged areas were developed. The developed models predict drought indices of the 6-month Standardized Precipitation Index (SPI6 and the 6-month Standardized Precipitation Evapotranspiration Index (SPEI6. An interpolation method based on multiquadric spline interpolation method as well as three machine learning models were tested. Three machine learning models of Decision Tree, Random Forest, and Extremely Randomized Trees were tested to enhance the provision of drought initial conditions based on remote sensing data, since initial conditions is one of the most important factors for drought forecasting. Machine learning-based methods performed better than interpolation methods for both classification and regression, and the methods using climatology data outperformed the methods using long-range forecast. The model based on climatological data and the machine learning method outperformed overall.

A convection-allowing ensemble forecast based on the breeding growth mode and associated optimization of precipitation forecast

Science.gov (United States)

Li, Xiang; He, Hongrang; Chen, Chaohui; Miao, Ziqing; Bai, Shigang

2017-10-01

A convection-allowing ensemble forecast experiment on a squall line was conducted based on the breeding growth mode (BGM). Meanwhile, the probability matched mean (PMM) and neighborhood ensemble probability (NEP) methods were used to optimize the associated precipitation forecast. The ensemble forecast predicted the precipitation tendency accurately, which was closer to the observation than in the control forecast. For heavy rainfall, the precipitation center produced by the ensemble forecast was also better. The Fractions Skill Score (FSS) results indicated that the ensemble mean was skillful in light rainfall, while the PMM produced better probability distribution of precipitation for heavy rainfall. Preliminary results demonstrated that convection-allowing ensemble forecast could improve precipitation forecast skill through providing valuable probability forecasts. It is necessary to employ new methods, such as the PMM and NEP, to generate precipitation probability forecasts. Nonetheless, the lack of spread and the overprediction of precipitation by the ensemble members are still problems that need to be solved.

Modern earthquake engineering offshore and land-based structures

CERN Document Server

Jia, Junbo

2017-01-01

This book addresses applications of earthquake engineering for both offshore and land-based structures. It is self-contained as a reference work and covers a wide range of topics, including topics related to engineering seismology, geotechnical earthquake engineering, structural engineering, as well as special contents dedicated to design philosophy, determination of ground motions, shock waves, tsunamis, earthquake damage, seismic response of offshore and arctic structures, spatial varied ground motions, simplified and advanced seismic analysis methods, sudden subsidence of offshore platforms, tank liquid impacts during earthquakes, seismic resistance of non-structural elements, and various types of mitigation measures, etc. The target readership includes professionals in offshore and civil engineering, officials and regulators, as well as researchers and students in this field.

Sensitivity of Coulomb stress changes to slip models of source faults: A case study for the 2011 Mw 9.0 Tohoku-oki earthquake

Science.gov (United States)

Wang, J.; Xu, C.; Furlong, K.; Zhong, B.; Xiao, Z.; Yi, L.; Chen, T.

2017-12-01

Although Coulomb stress changes induced by earthquake events have been used to quantify stress transfers and to retrospectively explain stress triggering among earthquake sequences, realistic reliable prospective earthquake forecasting remains scarce. To generate a robust Coulomb stress map for earthquake forecasting, uncertainties in Coulomb stress changes associated with the source fault, receiver fault and friction coefficient and Skempton's coefficient need to be exhaustively considered. In this paper, we specifically explore the uncertainty in slip models of the source fault of the 2011 Mw 9.0 Tohoku-oki earthquake as a case study. This earthquake was chosen because of its wealth of finite-fault slip models. Based on the wealth of those slip models, we compute the coseismic Coulomb stress changes induced by this mainshock. Our results indicate that nearby Coulomb stress changes for each slip model can be quite different, both for the Coulomb stress map at a given depth and on the Pacific subducting slab. The triggering rates for three months of aftershocks of the mainshock, with and without considering the uncertainty in slip models, differ significantly, decreasing from 70% to 18%. Reliable Coulomb stress changes in the three seismogenic zones of Nanki, Tonankai and Tokai are insignificant, approximately only 0.04 bar. By contrast, the portions of the Pacific subducting slab at a depth of 80 km and beneath Tokyo received a positive Coulomb stress change of approximately 0.2 bar. The standard errors of the seismicity rate and earthquake probability based on the Coulomb rate-and-state model (CRS) decay much faster with elapsed time in stress triggering zones than in stress shadows, meaning that the uncertainties in Coulomb stress changes in stress triggering zones would not drastically affect assessments of the seismicity rate and earthquake probability based on the CRS in the intermediate to long term.

Combination of biased forecasts: Bias correction or bias based weights?

OpenAIRE

Wenzel, Thomas

1999-01-01

Most of the literature on combination of forecasts deals with the assumption of unbiased individual forecasts. Here, we consider the case of biased forecasts and discuss two different combination techniques resulting in an unbiased forecast. On the one hand we correct the individual forecasts, and on the other we calculate bias based weights. A simulation study gives some insight in the situations where we should use the different methods.

New geological perspectives on earthquake recurrence models

International Nuclear Information System (INIS)

Schwartz, D.P.

1997-01-01

In most areas of the world the record of historical seismicity is too short or uncertain to accurately characterize the future distribution of earthquakes of different sizes in time and space. Most faults have not ruptured once, let alone repeatedly. Ultimately, the ability to correctly forecast the magnitude, location, and probability of future earthquakes depends on how well one can quantify the past behavior of earthquake sources. Paleoseismological trenching of active faults, historical surface ruptures, liquefaction features, and shaking-induced ground deformation structures provides fundamental information on the past behavior of earthquake sources. These studies quantify (a) the timing of individual past earthquakes and fault slip rates, which lead to estimates of recurrence intervals and the development of recurrence models and (b) the amount of displacement during individual events, which allows estimates of the sizes of past earthquakes on a fault. When timing and slip per event are combined with information on fault zone geometry and structure, models that define individual rupture segments can be developed. Paleoseismicity data, in the form of timing and size of past events, provide a window into the driving mechanism of the earthquake engine--the cycle of stress build-up and release

Observation-based Quantitative Uncertainty Estimation for Realtime Tsunami Inundation Forecast using ABIC and Ensemble Simulation

Science.gov (United States)

Takagawa, T.

2016-12-01

An ensemble forecasting scheme for tsunami inundation is presented. The scheme consists of three elemental methods. The first is a hierarchical Bayesian inversion using Akaike's Bayesian Information Criterion (ABIC). The second is Montecarlo sampling from a probability density function of multidimensional normal distribution. The third is ensamble analysis of tsunami inundation simulations with multiple tsunami sources. Simulation based validation of the model was conducted. A tsunami scenario of M9.1 Nankai earthquake was chosen as a target of validation. Tsunami inundation around Nagoya Port was estimated by using synthetic tsunami waveforms at offshore GPS buoys. The error of estimation of tsunami inundation area was about 10% even if we used only ten minutes observation data. The estimation accuracy of waveforms on/off land and spatial distribution of maximum tsunami inundation depth is demonstrated.

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method

Energy Technology Data Exchange (ETDEWEB)

Wang, Qin [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Florita, Anthony R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnan, Venkat K [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, Brian S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Cui, Mingjian [University of Texas at Dallas; Feng, Cong [University of Texas at Dallas; Wang, Zhenke [University of Texas at Dallas; Zhang, Jie [University of Texas at Dallas

2018-02-01

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power and currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start-time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.

Short-Term Forecasting of Taiwanese Earthquakes Using a Universal Model of Fusion-Fission Processes

Science.gov (United States)

Cheong, Siew Ann; Tan, Teck Liang; Chen, Chien-Chih; Chang, Wu-Lung; Liu, Zheng; Chew, Lock Yue; Sloot, Peter M. A.; Johnson, Neil F.

2014-01-01

Predicting how large an earthquake can be, where and when it will strike remains an elusive goal in spite of the ever-increasing volume of data collected by earth scientists. In this paper, we introduce a universal model of fusion-fission processes that can be used to predict earthquakes starting from catalog data. We show how the equilibrium dynamics of this model very naturally explains the Gutenberg-Richter law. Using the high-resolution earthquake catalog of Taiwan between Jan 1994 and Feb 2009, we illustrate how out-of-equilibrium spatio-temporal signatures in the time interval between earthquakes and the integrated energy released by earthquakes can be used to reliably determine the times, magnitudes, and locations of large earthquakes, as well as the maximum numbers of large aftershocks that would follow. PMID:24406467

A stochastic HMM-based forecasting model for fuzzy time series.

Science.gov (United States)

Li, Sheng-Tun; Cheng, Yi-Chung

2010-10-01

Recently, fuzzy time series have attracted more academic attention than traditional time series due to their capability of dealing with the uncertainty and vagueness inherent in the data collected. The formulation of fuzzy relations is one of the key issues affecting forecasting results. Most of the present works adopt IF-THEN rules for relationship representation, which leads to higher computational overhead and rule redundancy. Sullivan and Woodall proposed a Markov-based formulation and a forecasting model to reduce computational overhead; however, its applicability is limited to handling one-factor problems. In this paper, we propose a novel forecasting model based on the hidden Markov model by enhancing Sullivan and Woodall's work to allow handling of two-factor forecasting problems. Moreover, in order to make the nature of conjecture and randomness of forecasting more realistic, the Monte Carlo method is adopted to estimate the outcome. To test the effectiveness of the resulting stochastic model, we conduct two experiments and compare the results with those from other models. The first experiment consists of forecasting the daily average temperature and cloud density in Taipei, Taiwan, and the second experiment is based on the Taiwan Weighted Stock Index by forecasting the exchange rate of the New Taiwan dollar against the U.S. dollar. In addition to improving forecasting accuracy, the proposed model adheres to the central limit theorem, and thus, the result statistically approximates to the real mean of the target value being forecast.

Earthquake: Game-based learning for 21st century STEM education

Science.gov (United States)

Perkins, Abigail Christine

To play is to learn. A lack of empirical research within game-based learning literature, however, has hindered educational stakeholders to make informed decisions about game-based learning for 21st century STEM education. In this study, I modified a research and development (R&D) process to create a collaborative-competitive educational board game illuminating elements of earthquake engineering. I oriented instruction- and game-design principles around 21st century science education to adapt the R&D process to develop the educational game, Earthquake. As part of the R&D, I evaluated Earthquake for empirical evidence to support the claim that game-play results in student gains in critical thinking, scientific argumentation, metacognitive abilities, and earthquake engineering content knowledge. I developed Earthquake with the aid of eight focus groups with varying levels of expertise in science education research, teaching, administration, and game-design. After developing a functional prototype, I pilot-tested Earthquake with teacher-participants (n=14) who engaged in semi-structured interviews after their game-play. I analyzed teacher interviews with constant comparison methodology. I used teachers' comments and feedback from content knowledge experts to integrate game modifications, implementing results to improve Earthquake. I added player roles, simplified phrasing on cards, and produced an introductory video. I then administered the modified Earthquake game to two groups of high school student-participants (n = 6), who played twice. To seek evidence documenting support for my knowledge claim, I analyzed videotapes of students' game-play using a game-based learning checklist. My assessment of learning gains revealed increases in all categories of students' performance: critical thinking, metacognition, scientific argumentation, and earthquake engineering content knowledge acquisition. Players in both student-groups improved mostly in critical thinking, having

Leveraging geodetic data to reduce losses from earthquakes

Science.gov (United States)

Murray, Jessica R.; Roeloffs, Evelyn A.; Brooks, Benjamin A.; Langbein, John O.; Leith, William S.; Minson, Sarah E.; Svarc, Jerry L.; Thatcher, Wayne R.

2018-04-23

Seismic hazard assessments that are based on a variety of data and the best available science, coupled with rapid synthesis of real-time information from continuous monitoring networks to guide post-earthquake response, form a solid foundation for effective earthquake loss reduction. With this in mind, the Earthquake Hazards Program (EHP) of the U.S. Geological Survey (USGS) Natural Hazards Mission Area (NHMA) engages in a variety of undertakings, both established and emergent, in order to provide high quality products that enable stakeholders to take action in advance of and in response to earthquakes. Examples include the National Seismic Hazard Model (NSHM), development of tools for improved situational awareness such as earthquake early warning (EEW) and operational earthquake forecasting (OEF), research about induced seismicity, and new efforts to advance comprehensive subduction zone science and monitoring. Geodetic observations provide unique and complementary information directly relevant to advancing many aspects of these efforts (fig. 1). EHP scientists have long leveraged geodetic data for a range of influential studies, and they continue to develop innovative observation and analysis methods that push the boundaries of the field of geodesy as applied to natural hazards research. Given the ongoing, rapid improvement in availability, variety, and precision of geodetic measurements, considering ways to fully utilize this observational resource for earthquake loss reduction is timely and essential. This report presents strategies, and the underlying scientific rationale, by which the EHP could achieve the following outcomes: The EHP is an authoritative source for the interpretation of geodetic data and its use for earthquake loss reduction throughout the United States and its territories.The USGS consistently provides timely, high quality geodetic data to stakeholders.Significant earthquakes are better characterized by incorporating geodetic data into USGS

Probabilistic approach to earthquake prediction.

Directory of Open Access Journals (Sweden)

G. D'Addezio

2002-06-01

Full Text Available The evaluation of any earthquake forecast hypothesis requires the application of rigorous statistical methods. It implies a univocal definition of the model characterising the concerned anomaly or precursor, so as it can be objectively recognised in any circumstance and by any observer.A valid forecast hypothesis is expected to maximise successes and minimise false alarms. The probability gain associated to a precursor is also a popular way to estimate the quality of the predictions based on such precursor. Some scientists make use of a statistical approach based on the computation of the likelihood of an observed realisation of seismic events, and on the comparison of the likelihood obtained under different hypotheses. This method can be extended to algorithms that allow the computation of the density distribution of the conditional probability of earthquake occurrence in space, time and magnitude. Whatever method is chosen for building up a new hypothesis, the final assessment of its validity should be carried out by a test on a new and independent set of observations. The implementation of this test could, however, be problematic for seismicity characterised by long-term recurrence intervals. Even using the historical record, that may span time windows extremely variable between a few centuries to a few millennia, we have a low probability to catch more than one or two events on the same fault. Extending the record of earthquakes of the past back in time up to several millennia, paleoseismology represents a great opportunity to study how earthquakes recur through time and thus provide innovative contributions to time-dependent seismic hazard assessment. Sets of paleoseimologically dated earthquakes have been established for some faults in the Mediterranean area: the Irpinia fault in Southern Italy, the Fucino fault in Central Italy, the El Asnam fault in Algeria and the Skinos fault in Central Greece. By using the age of the

Evaluating the RELM Test Results

Directory of Open Access Journals (Sweden)

Michael K. Sachs

2012-01-01

Full Text Available We consider implications of the Regional Earthquake Likelihood Models (RELM test results with regard to earthquake forecasting. Prospective forecasts were solicited for M≥4.95 earthquakes in California during the period 2006–2010. During this period 31 earthquakes occurred in the test region with M≥4.95. We consider five forecasts that were submitted for the test. We compare the forecasts utilizing forecast verification methodology developed in the atmospheric sciences, specifically for tornadoes. We utilize a “skill score” based on the forecast scores λfi of occurrence of the test earthquakes. A perfect forecast would have λfi=1, and a random (no skill forecast would have λfi=2.86×10-3. The best forecasts (largest value of λfi for the 31 earthquakes had values of λfi=1.24×10-1 to λfi=5.49×10-3. The best mean forecast for all earthquakes was λ̅f=2.84×10-2. The best forecasts are about an order of magnitude better than random forecasts. We discuss the earthquakes, the forecasts, and alternative methods of evaluation of the performance of RELM forecasts. We also discuss the relative merits of alarm-based versus probability-based forecasts.

Strong motions observed by K-NET and KiK-net during the 2016 Kumamoto earthquake sequence

Science.gov (United States)

Suzuki, Wataru; Aoi, Shin; Kunugi, Takashi; Kubo, Hisahiko; Morikawa, Nobuyuki; Nakamura, Hiromitsu; Kimura, Takeshi; Fujiwara, Hiroyuki

2017-01-01

The nationwide strong-motion seismograph network of K-NET and KiK-net in Japan successfully recorded the strong ground motions of the 2016 Kumamoto earthquake sequence, which show the several notable characteristics. For the first large earthquake with a JMA magnitude of 6.5 (21:26, April 14, 2016, JST), the large strong motions are concentrated near the epicenter and the strong-motion attenuations are well predicted by the empirical relation for crustal earthquakes with a moment magnitude of 6.1. For the largest earthquake of the sequence with a JMA magnitude of 7.3 (01:25, April 16, 2016, JST), the large peak ground accelerations and velocities extend from the epicentral area to the northeast direction. The attenuation feature of peak ground accelerations generally follows the empirical relation, whereas that for velocities deviates from the empirical relation for stations with the epicentral distance of greater than 200 km, which can be attributed to the large Love wave having a dominant period around 10 s. The large accelerations were observed at stations even in Oita region, more than 70 km northeast from the epicenter. They are attributed to the local induced earthquake in Oita region, whose moment magnitude is estimated to be 5.5 by matching the amplitudes of the corresponding phases with the empirical attenuation relation. The real-time strong-motion observation has a potential for contributing to the mitigation of the ongoing earthquake disasters. We test a methodology to forecast the regions to be exposed to the large shaking in real time, which has been developed based on the fact that the neighboring stations are already shaken, for the largest event of the Kumamoto earthquakes, and demonstrate that it is simple but effective to quickly make warning. We also shows that the interpolation of the strong motions in real time is feasible, which will be utilized for the real-time forecast of ground motions based on the observed shakings.[Figure not available

Web-based Tsunami Early Warning System: a case study of the 2010 Kepulaunan Mentawai Earthquake and Tsunami

Directory of Open Access Journals (Sweden)

E. Ulutas

2012-06-01

Full Text Available This study analyzes the response of the Global Disasters Alerts and Coordination System (GDACS in relation to a case study: the Kepulaunan Mentawai earthquake and related tsunami, which occurred on 25 October 2010. The GDACS, developed by the European Commission Joint Research Center, combines existing web-based disaster information management systems with the aim to alert the international community in case of major disasters. The tsunami simulation system is an integral part of the GDACS. In more detail, the study aims to assess the tsunami hazard on the Mentawai and Sumatra coasts: the tsunami heights and arrival times have been estimated employing three propagation models based on the long wave theory. The analysis was performed in three stages: (1 pre-calculated simulations by using the tsunami scenario database for that region, used by the GDACS system to estimate the alert level; (2 near-real-time simulated tsunami forecasts, automatically performed by the GDACS system whenever a new earthquake is detected by the seismological data providers; and (3 post-event tsunami calculations using GCMT (Global Centroid Moment Tensor fault mechanism solutions proposed by US Geological Survey (USGS for this event. The GDACS system estimates the alert level based on the first type of calculations and on that basis sends alert messages to its users; the second type of calculations is available within 30–40 min after the notification of the event but does not change the estimated alert level. The third type of calculations is performed to improve the initial estimations and to have a better understanding of the extent of the possible damage. The automatic alert level for the earthquake was given between Green and Orange Alert, which, in the logic of GDACS, means no need or moderate need of international humanitarian assistance; however, the earthquake generated 3 to 9 m tsunami run-up along southwestern coasts of the Pagai Islands where 431 people died

Visible Earthquakes: a web-based tool for visualizing and modeling InSAR earthquake data

Science.gov (United States)

Funning, G. J.; Cockett, R.

2012-12-01

InSAR (Interferometric Synthetic Aperture Radar) is a technique for measuring the deformation of the ground using satellite radar data. One of the principal applications of this method is in the study of earthquakes; in the past 20 years over 70 earthquakes have been studied in this way, and forthcoming satellite missions promise to enable the routine and timely study of events in the future. Despite the utility of the technique and its widespread adoption by the research community, InSAR does not feature in the teaching curricula of most university geoscience departments. This is, we believe, due to a lack of accessibility to software and data. Existing tools for the visualization and modeling of interferograms are often research-oriented, command line-based and/or prohibitively expensive. Here we present a new web-based interactive tool for comparing real InSAR data with simple elastic models. The overall design of this tool was focused on ease of access and use. This tool should allow interested nonspecialists to gain a feel for the use of such data and greatly facilitate integration of InSAR into upper division geoscience courses, giving students practice in comparing actual data to modeled results. The tool, provisionally named 'Visible Earthquakes', uses web-based technologies to instantly render the displacement field that would be observable using InSAR for a given fault location, geometry, orientation, and slip. The user can adjust these 'source parameters' using a simple, clickable interface, and see how these affect the resulting model interferogram. By visually matching the model interferogram to a real earthquake interferogram (processed separately and included in the web tool) a user can produce their own estimates of the earthquake's source parameters. Once satisfied with the fit of their models, users can submit their results and see how they compare with the distribution of all other contributed earthquake models, as well as the mean and median

Building the Southern California Earthquake Center

Science.gov (United States)

Jordan, T. H.; Henyey, T.; McRaney, J. K.

2004-12-01

Kei Aki was the founding director of the Southern California Earthquake Center (SCEC), a multi-institutional collaboration formed in 1991 as a Science and Technology Center (STC) under the National Science Foundation (NSF) and the U. S. Geological Survey (USGS). Aki and his colleagues articulated a system-level vision for the Center: investigations by disciplinary working groups would be woven together into a "Master Model" for Southern California. In this presentation, we will outline how the Master-Model concept has evolved and how SCEC's structure has adapted to meet scientific challenges of system-level earthquake science. In its first decade, SCEC conducted two regional imaging experiments (LARSE I & II); published the "Phase-N" reports on (1) the Landers earthquake, (2) a new earthquake rupture forecast for Southern California, and (3) new models for seismic attenuation and site effects; it developed two prototype "Community Models" (the Crustal Motion Map and Community Velocity Model) and, perhaps most important, sustained a long-term, multi-institutional, interdisciplinary collaboration. The latter fostered pioneering numerical simulations of earthquake ruptures, fault interactions, and wave propagation. These accomplishments provided the impetus for a successful proposal in 2000 to reestablish SCEC as a "stand alone" center under NSF/USGS auspices. SCEC remains consistent with the founders' vision: it continues to advance seismic hazard analysis through a system-level synthesis that is based on community models and an ever expanding array of information technology. SCEC now represents a fully articulated "collaboratory" for earthquake science, and many of its features are extensible to other active-fault systems and other system-level collaborations. We will discuss the implications of the SCEC experience for EarthScope, the USGS's program in seismic hazard analysis, NSF's nascent Cyberinfrastructure Initiative, and other large collaboratory programs.

E-DECIDER Rapid Response to the M 6.0 South Napa Earthquake

Science.gov (United States)

Glasscoe, M. T.; Parker, J. W.; Pierce, M. E.; Wang, J.; Eguchi, R. T.; Huyck, C. K.; Hu, Z.; Chen, Z.; Yoder, M. R.; Rundle, J. B.; Rosinski, A.

2014-12-01

E-DECIDER initiated rapid response mode when the California Earthquake Clearinghouse was activated the morning following the M6 Napa earthquake. Data products, including: 1) rapid damage and loss estimates, 2) deformation magnitude and slope change maps, and 3) aftershock forecasts were provided to the Clearinghouse partners within 24 hours of the event via XchangeCore Web Service Data Orchestration sharing. NASA data products were provided to end-users via XchangeCore, EERI and Clearinghouse websites, and ArcGIS online for Napa response, reaching a wide response audience. The E-DECIDER team helped facilitate rapid delivery of NASA products to stakeholders and participated in Clearinghouse Napa earthquake briefings to update stakeholders on product information. Rapid response products from E-DECIDER can be used to help prioritize response efforts shortly after the event has occurred. InLET (Internet Loss Estimation Tool) post-event damage and casualty estimates were generated quickly after the Napa earthquake. InLET provides immediate post-event estimates of casualties and building damage by performing loss/impact simulations using USGS ground motion data and FEMA HAZUS damage estimation technology. These results were provided to E-DECIDER by their collaborators, ImageCat, Inc. and the Community Stakeholder Network (CSN). Strain magnitude and slope change maps were automatically generated when the Napa earthquake appeared on the USGS feed. These maps provide an early estimate of where the deformation has occurred and where damage may be localized. Using E-DECIDER critical infrastructure overlays with damage estimates, decision makers can direct response effort that can be verified later with field reconnaissance and remote sensing-based observations. Earthquake aftershock forecast maps were produced within hours of the event. These maps highlight areas where aftershocks are likely to occur and can also be coupled with infrastructure overlays to help direct response

Generalization of information-based concepts in forecast verification

Science.gov (United States)

Tödter, J.; Ahrens, B.

2012-04-01

This work deals with information-theoretical methods in probabilistic forecast verification. Recent findings concerning the Ignorance Score are shortly reviewed, then the generalization to continuous forecasts is shown. For ensemble forecasts, the presented measures can be calculated exactly. The Brier Score (BS) and its generalizations to the multi-categorical Ranked Probability Score (RPS) and to the Continuous Ranked Probability Score (CRPS) are the prominent verification measures for probabilistic forecasts. Particularly, their decompositions into measures quantifying the reliability, resolution and uncertainty of the forecasts are attractive. Information theory sets up the natural framework for forecast verification. Recently, it has been shown that the BS is a second-order approximation of the information-based Ignorance Score (IGN), which also contains easily interpretable components and can also be generalized to a ranked version (RIGN). Here, the IGN, its generalizations and decompositions are systematically discussed in analogy to the variants of the BS. Additionally, a Continuous Ranked IGN (CRIGN) is introduced in analogy to the CRPS. The applicability and usefulness of the conceptually appealing CRIGN is illustrated, together with an algorithm to evaluate its components reliability, resolution, and uncertainty for ensemble-generated forecasts. This is also directly applicable to the more traditional CRPS.

M≥7 Earthquake rupture forecast and time-dependent probability for the Sea of Marmara region, Turkey

Science.gov (United States)

Murru, Maura; Akinci, Aybige; Falcone, Guiseppe; Pucci, Stefano; Console, Rodolfo; Parsons, Thomas E.

2016-01-01

We forecast time-independent and time-dependent earthquake ruptures in the Marmara region of Turkey for the next 30 years using a new fault-segmentation model. We also augment time-dependent Brownian Passage Time (BPT) probability with static Coulomb stress changes (ΔCFF) from interacting faults. We calculate Mw > 6.5 probability from 26 individual fault sources in the Marmara region. We also consider a multisegment rupture model that allows higher-magnitude ruptures over some segments of the Northern branch of the North Anatolian Fault Zone (NNAF) beneath the Marmara Sea. A total of 10 different Mw=7.0 to Mw=8.0 multisegment ruptures are combined with the other regional faults at rates that balance the overall moment accumulation. We use Gaussian random distributions to treat parameter uncertainties (e.g., aperiodicity, maximum expected magnitude, slip rate, and consequently mean recurrence time) of the statistical distributions associated with each fault source. We then estimate uncertainties of the 30-year probability values for the next characteristic event obtained from three different models (Poisson, BPT, and BPT+ΔCFF) using a Monte Carlo procedure. The Gerede fault segment located at the eastern end of the Marmara region shows the highest 30-yr probability, with a Poisson value of 29%, and a time-dependent interaction probability of 48%. We find an aggregated 30-yr Poisson probability of M >7.3 earthquakes at Istanbul of 35%, which increases to 47% if time dependence and stress transfer are considered. We calculate a 2-fold probability gain (ratio time-dependent to time-independent) on the southern strands of the North Anatolian Fault Zone.

Fractal analysis of the spatial distribution of earthquakes along the Hellenic Subduction Zone

Science.gov (United States)

Papadakis, Giorgos; Vallianatos, Filippos; Sammonds, Peter

2014-05-01

The Hellenic Subduction Zone (HSZ) is the most seismically active region in Europe. Many destructive earthquakes have taken place along the HSZ in the past. The evolution of such active regions is expressed through seismicity and is characterized by complex phenomenology. The understanding of the tectonic evolution process and the physical state of subducting regimes is crucial in earthquake prediction. In recent years, there is a growing interest concerning an approach to seismicity based on the science of complex systems (Papadakis et al., 2013; Vallianatos et al., 2012). In this study we calculate the fractal dimension of the spatial distribution of earthquakes along the HSZ and we aim to understand the significance of the obtained values to the tectonic and geodynamic evolution of this area. We use the external seismic sources provided by Papaioannou and Papazachos (2000) to create a dataset regarding the subduction zone. According to the aforementioned authors, we define five seismic zones. Then, we structure an earthquake dataset which is based on the updated and extended earthquake catalogue for Greece and the adjacent areas by Makropoulos et al. (2012), covering the period 1976-2009. The fractal dimension of the spatial distribution of earthquakes is calculated for each seismic zone and for the HSZ as a unified system using the box-counting method (Turcotte, 1997; Robertson et al., 1995; Caneva and Smirnov, 2004). Moreover, the variation of the fractal dimension is demonstrated in different time windows. These spatiotemporal variations could be used as an additional index to inform us about the physical state of each seismic zone. As a precursor in earthquake forecasting, the use of the fractal dimension appears to be a very interesting future work. Acknowledgements Giorgos Papadakis wish to acknowledge the Greek State Scholarships Foundation (IKY). References Caneva, A., Smirnov, V., 2004. Using the fractal dimension of earthquake distributions and the

Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration

Energy Technology Data Exchange (ETDEWEB)

Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-08-23

In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operator can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.

Research on light rail electric load forecasting based on ARMA model

Science.gov (United States)

Huang, Yifan

2018-04-01

The article compares a variety of time series models and combines the characteristics of power load forecasting. Then, a light load forecasting model based on ARMA model is established. Based on this model, a light rail system is forecasted. The prediction results show that the accuracy of the model prediction is high.

Rethinking earthquake-related DC-ULF electromagnetic phenomena: towards a physics-based approach

Directory of Open Access Journals (Sweden)

Q. Huang

2011-11-01

Full Text Available Numerous electromagnetic changes possibly related with earthquakes have been independently reported and have even been attempted to apply to short-term prediction of earthquakes. However, there are active debates on the above issue because the seismogenic process is rather complicated and the studies have been mainly empirical (i.e. a kind of experience-based approach. Thus, a physics-based study would be helpful for understanding earthquake-related electromagnetic phenomena and strengthening their applications. As a potential physics-based approach, I present an integrated research scheme, taking into account the interaction among observation, methodology, and physical model. For simplicity, this work focuses only on the earthquake-related DC-ULF electromagnetic phenomena. The main approach includes the following key problems: (1 how to perform a reliable and appropriate observation with some clear physical quantities; (2 how to develop a robust methodology to reveal weak earthquake-related electromagnetic signals from noisy background; and (3 how to develop plausible physical models based on theoretical analyses and/or laboratory experiments for the explanation of the earthquake-related electromagnetic signals observed in the field conditions.

Energy demand forecasting method based on international statistical data

International Nuclear Information System (INIS)

Glanc, Z.; Kerner, A.

1997-01-01

Poland is in a transition phase from a centrally planned to a market economy; data collected under former economic conditions do not reflect a market economy. Final energy demand forecasts are based on the assumption that the economic transformation in Poland will gradually lead the Polish economy, technologies and modes of energy use, to the same conditions as mature market economy countries. The starting point has a significant influence on the future energy demand and supply structure: final energy consumption per capita in 1992 was almost half the average of OECD countries; energy intensity, based on Purchasing Power Parities (PPP) and referred to GDP, is more than 3 times higher in Poland. A method of final energy demand forecasting based on regression analysis is described in this paper. The input data are: output of macroeconomic and population growth forecast; time series 1970-1992 of OECD countries concerning both macroeconomic characteristics and energy consumption; and energy balance of Poland for the base year of the forecast horizon. (author). 1 ref., 19 figs, 4 tabs

Energy demand forecasting method based on international statistical data

Energy Technology Data Exchange (ETDEWEB)

Glanc, Z; Kerner, A [Energy Information Centre, Warsaw (Poland)

1997-09-01

Poland is in a transition phase from a centrally planned to a market economy; data collected under former economic conditions do not reflect a market economy. Final energy demand forecasts are based on the assumption that the economic transformation in Poland will gradually lead the Polish economy, technologies and modes of energy use, to the same conditions as mature market economy countries. The starting point has a significant influence on the future energy demand and supply structure: final energy consumption per capita in 1992 was almost half the average of OECD countries; energy intensity, based on Purchasing Power Parities (PPP) and referred to GDP, is more than 3 times higher in Poland. A method of final energy demand forecasting based on regression analysis is described in this paper. The input data are: output of macroeconomic and population growth forecast; time series 1970-1992 of OECD countries concerning both macroeconomic characteristics and energy consumption; and energy balance of Poland for the base year of the forecast horizon. (author). 1 ref., 19 figs, 4 tabs.

ANALYSIS OF REGULARITIES IN DISTRIBUTION OF EARTHQUAKES BY FOCAL DISPLACEMENT IN THE KURIL-OKHOTSK REGION BEFORE THE CATASTROPHIC SIMUSHIR EARTHQUAKE OF 15 NOVEMBER 2006

Directory of Open Access Journals (Sweden)

Timofei K. Zlobin

2012-01-01

Full Text Available The catastrophic Simushir earthquake occurred on 15 November 2006 in the Kuril-Okhotsk region in the Middle Kuril Islands which is a transition zone between the Eurasian continent and the Pacific Ocean. It was followed by numerous strong earthquakes. It is established that the catastrophic earthquake was prepared on a site characterized by increased relative effective pressures which is located at the border of the low-pressure area (Figure 1.Based on data from GlobalCMT (Harvard, earthquake focal mechanisms were reconstructed, and tectonic stresses, the seismotectonic setting and the earthquakes distribution pattern were studied for analysis of the field of stresses in the region before to the Simushir earthquake (Figures 2 and 3; Table 1.Five areas of various types of movement were determined. Three of them are stretched along the Kuril Islands. It is established that seismodislocations in earthquake focal areas are regularly distributed. In each of the determined areas, displacements of a specific type (shear or reverse shear are concentrated and give evidence of the alteration and change of zones characterized by horizontal stretching and compression.The presence of the horizontal stretching and compression zones can be explained by a model of subduction (Figure 4. Detailed studies of the state of stresses of the Kuril region confirm such zones (Figure 5. Recent GeodynamicsThe established specific features of tectonic stresses before the catastrophic Simushir earthquake of 15 November 2006 contribute to studies of earthquake forecasting problems. The state of stresses and the geodynamic conditions suggesting occurrence of new earthquakes can be assessed from the data on the distribution of horizontal compression, stretching and shear areas of the Earth’s crust and the upper mantle in the Kuril region.

Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint

Energy Technology Data Exchange (ETDEWEB)

Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-07-26

In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution system operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.

Short-term load forecasting by a neuro-fuzzy based approach

Energy Technology Data Exchange (ETDEWEB)

Ruey-Hsun Liang; Ching-Chi Cheng [National Yunlin University of Science and Technology (China). Dept. of Electrical Engineering

2002-02-01

An approach based on an artificial neural network (ANN) combined with a fuzzy system is proposed for short-term load forecasting. This approach was developed in order to reach the desired short-term load forecasting in an efficient manner. Over the past few years, ANNs have attained the ability to manage a great deal of system complexity and are now being proposed as powerful computational tools. In order to select the appropriate load as the input for the desired forecasting, the Pearson analysis method is first applied to choose two historical record load patterns that are similar to the forecasted load pattern. These two load patterns and the required weather parameters are then fuzzified and input into a neural network for training or testing the network. The back-propagation (BP) neural network is applied to determine the preliminary forecasted load. In addition, the rule base for the fuzzy inference machine contains important linguistic membership function terms with knowledge in the form of fuzzy IF-THEN rules. This produces the load correction inference from the historical information and past forecasted load errors to obtain an inferred load error. Adding the inferred load error to the preliminary forecasted load, we can obtain the finial forecasted load. The effectiveness of the proposed approach to the short-term load-forecasting problem is demonstrated using practical data from the Taiwan Power Company (TPC). (Author)

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

Directory of Open Access Journals (Sweden)

Yuqi Dong

2016-12-01

Full Text Available Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people’s livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the characteristics of time series, which will have a great impact on the forecasting accuracy. For that reason, this paper proposes a hybrid model based on data decomposition considering periodicity, trend and randomness of the original electrical load time series data. Through preprocessing and analyzing the original time series, the generalized regression neural network optimized by genetic algorithm is used to forecast the short-term electrical load. The experimental results demonstrate that the proposed hybrid model can not only achieve a good fitting ability, but it can also approximate the actual values when dealing with non-linear time series data with periodicity, trend and randomness.

Visualizing Uncertainty for Probabilistic Weather Forecasting based on Reforecast Analogs

Science.gov (United States)

Pelorosso, Leandro; Diehl, Alexandra; Matković, Krešimir; Delrieux, Claudio; Ruiz, Juan; Gröeller, M. Eduard; Bruckner, Stefan

2016-04-01

Numerical weather forecasts are prone to uncertainty coming from inaccuracies in the initial and boundary conditions and lack of precision in numerical models. Ensemble of forecasts partially addresses these problems by considering several runs of the numerical model. Each forecast is generated with different initial and boundary conditions and different model configurations [GR05]. The ensembles can be expressed as probabilistic forecasts, which have proven to be very effective in the decision-making processes [DE06]. The ensemble of forecasts represents only some of the possible future atmospheric states, usually underestimating the degree of uncertainty in the predictions [KAL03, PH06]. Hamill and Whitaker [HW06] introduced the "Reforecast Analog Regression" (RAR) technique to overcome the limitations of ensemble forecasting. This technique produces probabilistic predictions based on the analysis of historical forecasts and observations. Visual analytics provides tools for processing, visualizing, and exploring data to get new insights and discover hidden information patterns in an interactive exchange between the user and the application [KMS08]. In this work, we introduce Albero, a visual analytics solution for probabilistic weather forecasting based on the RAR technique. Albero targets at least two different type of users: "forecasters", who are meteorologists working in operational weather forecasting and "researchers", who work in the construction of numerical prediction models. Albero is an efficient tool for analyzing precipitation forecasts, allowing forecasters to make and communicate quick decisions. Our solution facilitates the analysis of a set of probabilistic forecasts, associated statistical data, observations and uncertainty. A dashboard with small-multiples of probabilistic forecasts allows the forecasters to analyze at a glance the distribution of probabilities as a function of time, space, and magnitude. It provides the user with a more

Forecast combinations

OpenAIRE

Aiolfi, Marco; Capistrán, Carlos; Timmermann, Allan

2010-01-01

We consider combinations of subjective survey forecasts and model-based forecasts from linear and non-linear univariate specifications as well as multivariate factor-augmented models. Empirical results suggest that a simple equal-weighted average of survey forecasts outperform the best model-based forecasts for a majority of macroeconomic variables and forecast horizons. Additional improvements can in some cases be gained by using a simple equal-weighted average of survey and model-based fore...

Staged decision making based on probabilistic forecasting

Science.gov (United States)

Booister, Nikéh; Verkade, Jan; Werner, Micha; Cranston, Michael; Cumiskey, Lydia; Zevenbergen, Chris

2016-04-01

Flood forecasting systems reduce, but cannot eliminate uncertainty about the future. Probabilistic forecasts explicitly show that uncertainty remains. However, as - compared to deterministic forecasts - a dimension is added ('probability' or 'likelihood'), with this added dimension decision making is made slightly more complicated. A technique of decision support is the cost-loss approach, which defines whether or not to issue a warning or implement mitigation measures (risk-based method). With the cost-loss method a warning will be issued when the ratio of the response costs to the damage reduction is less than or equal to the probability of the possible flood event. This cost-loss method is not widely used, because it motivates based on only economic values and is a technique that is relatively static (no reasoning, yes/no decision). Nevertheless it has high potential to improve risk-based decision making based on probabilistic flood forecasting because there are no other methods known that deal with probabilities in decision making. The main aim of this research was to explore the ways of making decision making based on probabilities with the cost-loss method better applicable in practice. The exploration began by identifying other situations in which decisions were taken based on uncertain forecasts or predictions. These cases spanned a range of degrees of uncertainty: from known uncertainty to deep uncertainty. Based on the types of uncertainties, concepts of dealing with situations and responses were analysed and possible applicable concepts where chosen. Out of this analysis the concepts of flexibility and robustness appeared to be fitting to the existing method. Instead of taking big decisions with bigger consequences at once, the idea is that actions and decisions are cut-up into smaller pieces and finally the decision to implement is made based on economic costs of decisions and measures and the reduced effect of flooding. The more lead-time there is in

Radar Based Flow and Water Level Forecasting in Sewer Systems

DEFF Research Database (Denmark)

Thorndahl, Søren; Rasmussen, Michael R.; Grum, M.

2009-01-01

This paper describes the first radar based forecast of flow and/or water level in sewer systems in Denmark. The rainfall is successfully forecasted with a lead time of 1-2 hours, and flow/levels are forecasted an additional ½-1½ hours using models describing the behaviour of the sewer system. Bot...

Probabilistic Wind Power Ramp Forecasting Based on a Scenario Generation Method: Preprint

Energy Technology Data Exchange (ETDEWEB)

Wang, Qin [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Florita, Anthony R [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnan, Venkat K [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Hodge, Brian S [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Cui, Mingjian [Univ. of Texas-Dallas, Richardson, TX (United States); Feng, Cong [Univ. of Texas-Dallas, Richardson, TX (United States); Wang, Zhenke [Univ. of Texas-Dallas, Richardson, TX (United States); Zhang, Jie [Univ. of Texas-Dallas, Richardson, TX (United States)

2017-08-31

Wind power ramps (WPRs) are particularly important in the management and dispatch of wind power, and they are currently drawing the attention of balancing authorities. With the aim to reduce the impact of WPRs for power system operations, this paper develops a probabilistic ramp forecasting method based on a large number of simulated scenarios. An ensemble machine learning technique is first adopted to forecast the basic wind power forecasting scenario and calculate the historical forecasting errors. A continuous Gaussian mixture model (GMM) is used to fit the probability distribution function (PDF) of forecasting errors. The cumulative distribution function (CDF) is analytically deduced. The inverse transform method based on Monte Carlo sampling and the CDF is used to generate a massive number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The probabilistic forecasting results of ramp duration and start time are generated based on all scenarios. Numerical simulations on publicly available wind power data show that within a predefined tolerance level, the developed probabilistic wind power ramp forecasting method is able to predict WPRs with a high level of sharpness and accuracy.

Short-term data forecasting based on wavelet transformation and chaos theory

Science.gov (United States)

Wang, Yi; Li, Cunbin; Zhang, Liang

2017-09-01

A sketch of wavelet transformation and its application was given. Concerning the characteristics of time sequence, Haar wavelet was used to do data reduction. After processing, the effect of “data nail” on forecasting was reduced. Chaos theory was also introduced, a new chaos time series forecasting flow based on wavelet transformation was proposed. The largest Lyapunov exponent was larger than zero from small data sets, it verified the data change behavior still met chaotic behavior. Based on this, chaos time series to forecast short-term change behavior could be used. At last, the example analysis of the price from a real electricity market showed that the forecasting method increased the precision of the forecasting more effectively and steadily.

On practical challenges of decomposition-based hybrid forecasting algorithms for wind speed and solar irradiation

International Nuclear Information System (INIS)

Wang, Yamin; Wu, Lei

2016-01-01

This paper presents a comprehensive analysis on practical challenges of empirical mode decomposition (EMD) based algorithms on wind speed and solar irradiation forecasts that have been largely neglected in literature, and proposes an alternative approach to mitigate such challenges. Specifically, the challenges are: (1) Decomposed sub-series are very sensitive to the original time series data. That is, sub-series of the new time series, consisting of the original one plus a limit number of new data samples, may significantly differ from those used in training forecasting models. In turn, forecasting models established by original sub-series may not be suitable for newly decomposed sub-series and have to be trained more frequently; and (2) Key environmental factors usually play a critical role in non-decomposition based methods for forecasting wind speed and solar irradiation. However, it is difficult to incorporate such critical environmental factors into forecasting models of individual decomposed sub-series, because the correlation between the original data and environmental factors is lost after decomposition. Numerical case studies on wind speed and solar irradiation forecasting show that the performance of existing EMD-based forecasting methods could be worse than the non-decomposition based forecasting model, and are not effective in practical cases. Finally, the approximated forecasting model based on EMD is proposed to mitigate the challenges and achieve better forecasting results than existing EMD-based forecasting algorithms and the non-decomposition based forecasting models on practical wind speed and solar irradiation forecasting cases. - Highlights: • Two challenges of existing EMD-based forecasting methods are discussed. • Significant changes of sub-series in each step of the rolling forecast procedure. • Difficulties in incorporating environmental factors into sub-series forecasting models. • The approximated forecasting method is proposed to

Predictability of Landslide Timing From Quasi-Periodic Precursory Earthquakes

Science.gov (United States)

Bell, Andrew F.

2018-02-01

Accelerating rates of geophysical signals are observed before a range of material failure phenomena. They provide insights into the physical processes controlling failure and the basis for failure forecasts. However, examples of accelerating seismicity before landslides are rare, and their behavior and forecasting potential are largely unknown. Here I use a Bayesian methodology to apply a novel gamma point process model to investigate a sequence of quasiperiodic repeating earthquakes preceding a large landslide at Nuugaatsiaq in Greenland in June 2017. The evolution in earthquake rate is best explained by an inverse power law increase with time toward failure, as predicted by material failure theory. However, the commonly accepted power law exponent value of 1.0 is inconsistent with the data. Instead, the mean posterior value of 0.71 indicates a particularly rapid acceleration toward failure and suggests that only relatively short warning times may be possible for similar landslides in future.

Earthquake Hazard Assessment: an Independent Review

Science.gov (United States)

Kossobokov, Vladimir

2016-04-01

Seismic hazard assessment (SHA), from term-less (probabilistic PSHA or deterministic DSHA) to time-dependent (t-DASH) including short-term earthquake forecast/prediction (StEF), is not an easy task that implies a delicate application of statistics to data of limited size and different accuracy. Regretfully, in many cases of SHA, t-DASH, and StEF, the claims of a high potential and efficiency of the methodology are based on a flawed application of statistics and hardly suitable for communication to decision makers. The necessity and possibility of applying the modified tools of Earthquake Prediction Strategies, in particular, the Error Diagram, introduced by G.M. Molchan in early 1990ies for evaluation of SHA, and the Seismic Roulette null-hypothesis as a measure of the alerted space, is evident, and such a testing must be done in advance claiming hazardous areas and/or times. The set of errors, i.e. the rates of failure and of the alerted space-time volume, compared to those obtained in the same number of random guess trials permits evaluating the SHA method effectiveness and determining the optimal choice of the parameters in regard to specified cost-benefit functions. These and other information obtained in such a testing may supply us with a realistic estimate of confidence in SHA results and related recommendations on the level of risks for decision making in regard to engineering design, insurance, and emergency management. These basics of SHA evaluation are exemplified with a few cases of misleading "seismic hazard maps", "precursors", and "forecast/prediction methods".

Where was the 1898 Mare Island Earthquake? Insights from the 2014 South Napa Earthquake

Science.gov (United States)

Hough, S. E.

2014-12-01

The 2014 South Napa earthquake provides an opportunity to reconsider the Mare Island earthquake of 31 March 1898, which caused severe damage to buildings at a Navy yard on the island. Revising archival accounts of the 1898 earthquake, I estimate a lower intensity magnitude, 5.8, than the value in the current Uniform California Earthquake Rupture Forecast (UCERF) catalog (6.4). However, I note that intensity magnitude can differ from Mw by upwards of half a unit depending on stress drop, which for a historical earthquake is unknowable. In the aftermath of the 2014 earthquake, there has been speculation that apparently severe effects on Mare Island in 1898 were due to the vulnerability of local structures. No surface rupture has ever been identified from the 1898 event, which is commonly associated with the Hayward-Rodgers Creek fault system, some 10 km west of Mare Island (e.g., Parsons et al., 2003). Reconsideration of detailed archival accounts of the 1898 earthquake, together with a comparison of the intensity distributions for the two earthquakes, points to genuinely severe, likely near-field ground motions on Mare Island. The 2014 earthquake did cause significant damage to older brick buildings on Mare Island, but the level of damage does not match the severity of documented damage in 1898. The high intensity files for the two earthquakes are more over spatially shifted, with the centroid of the 2014 distribution near the town of Napa and that of the 1898 distribution near Mare Island, east of the Hayward-Rodgers Creek system. I conclude that the 1898 Mare Island earthquake was centered on or near Mare Island, possibly involving rupture of one or both strands of the Franklin fault, a low-slip-rate fault sub-parallel to the Rodgers Creek fault to the west and the West Napa fault to the east. I estimate Mw5.8 assuming an average stress drop; data are also consistent with Mw6.4 if stress drop was a factor of ≈3 lower than average for California earthquakes. I

Application and analysis of debris-flow early warning system in Wenchuan earthquake-affected area

Science.gov (United States)

Liu, D. L.; Zhang, S. J.; Yang, H. J.; Zhao, L. Q.; Jiang, Y. H.; Tang, D.; Leng, X. P.

2016-02-01

The activities of debris flow (DF) in the Wenchuan earthquake-affected area significantly increased after the earthquake on 12 May 2008. The safety of the lives and property of local people is threatened by DFs. A physics-based early warning system (EWS) for DF forecasting was developed and applied in this earthquake area. This paper introduces an application of the system in the Wenchuan earthquake-affected area and analyzes the prediction results via a comparison to the DF events triggered by the strong rainfall events reported by the local government. The prediction accuracy and efficiency was first compared with a contribution-factor-based system currently used by the weather bureau of Sichuan province. The storm on 17 August 2012 was used as a case study for this comparison. The comparison shows that the false negative rate and false positive rate of the new system is, respectively, 19 and 21 % lower than the system based on the contribution factors. Consequently, the prediction accuracy is obviously higher than the system based on the contribution factors with a higher operational efficiency. On the invitation of the weather bureau of Sichuan province, the authors upgraded their prediction system of DF by using this new system before the monsoon of Wenchuan earthquake-affected area in 2013. Two prediction cases on 9 July 2013 and 10 July 2014 were chosen to further demonstrate that the new EWS has high stability, efficiency, and prediction accuracy.

A Crowdsourcing-based Taiwan Scientific Earthquake Reporting System

Science.gov (United States)

Liang, W. T.; Lee, J. C.; Lee, C. F.

2017-12-01

To collect immediately field observations for any earthquake-induced ground damages, such as surface fault rupture, landslide, rock fall, liquefaction, and landslide-triggered dam or lake, etc., we are developing an earthquake damage reporting system which particularly relies on school teachers as volunteers after taking a series of training courses organized by this project. This Taiwan Scientific Earthquake Reporting (TSER) system is based on the Ushahidi mapping platform, which has been widely used for crowdsourcing on different purposes. Participants may add an app-like icon for mobile devices to this website at https://ies-tser.iis.sinica.edu.tw. Right after a potential damaging earthquake occurred in the Taiwan area, trained volunteers will be notified/dispatched to the source area to carry out field surveys and to describe the ground damages through this system. If the internet is available, they may also upload some relevant images in the field right away. This collected information will be shared with all public after a quick screen by the on-duty scientists. To prepare for the next strong earthquake, we set up a specific project on TSER for sharing spectacular/remarkable geologic features wherever possible. This is to help volunteers get used to this system and share any teachable material on this platform. This experimental, science-oriented crowdsourcing system was launched early this year. Together with a DYFI-like intensity reporting system, Taiwan Quake-Catcher Network, and some online games and teaching materials, the citizen seismology has been much improved in Taiwan in the last decade. All these constructed products are now either operated or promoted at the Taiwan Earthquake Research Center (TEC). With these newly developed platforms and materials, we are aiming not only to raise the earthquake awareness and preparedness, but also to encourage public participation in earthquake science in Taiwan.

ECONOMIC FORECASTS BASED ON ECONOMETRIC MODELS USING EViews 5

Directory of Open Access Journals (Sweden)

Cornelia TomescuDumitrescu,

2009-05-01

Full Text Available The forecast of evolution of economic phenomena represent on the most the final objective of econometrics. It withal represent a real attempt of validity elaborate model. Unlike the forecasts based on the study of temporal series which have an recognizable inertial character the forecasts generated by econometric model with simultaneous equations are after to contour the future of ones of important economic variables toward the direct and indirect influences bring the bear on their about exogenous variables. For the relief of the calculus who the realization of the forecasts based on the econometric models its suppose is indicate the use of the specialized informatics programs. One of this is the EViews which is applied because it reduces significant the time who is destined of the econometric analysis and it assure a high accuracy of calculus and of the interpretation of results.

Using adaptive network based fuzzy inference system to forecast regional electricity loads

International Nuclear Information System (INIS)

Ying, L.-C.; Pan, M.-C.

2008-01-01

Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads

Using adaptive network based fuzzy inference system to forecast regional electricity loads

Energy Technology Data Exchange (ETDEWEB)

Ying, Li-Chih [Department of Marketing Management, Central Taiwan University of Science and Technology, 11, Pu-tzu Lane, Peitun, Taichung City 406 (China); Pan, Mei-Chiu [Graduate Institute of Management Sciences, Nanhua University, 32, Chung Keng Li, Dalin, Chiayi 622 (China)

2008-02-15

Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads. (author)

Excel, Earthquakes, and Moneyball: exploring Cascadia earthquake probabilities using spreadsheets and baseball analogies

Science.gov (United States)

Campbell, M. R.; Salditch, L.; Brooks, E. M.; Stein, S.; Spencer, B. D.

2017-12-01

Much recent media attention focuses on Cascadia's earthquake hazard. A widely cited magazine article starts "An earthquake will destroy a sizable portion of the coastal Northwest. The question is when." Stories include statements like "a massive earthquake is overdue", "in the next 50 years, there is a 1-in-10 chance a "really big one" will erupt," or "the odds of the big Cascadia earthquake happening in the next fifty years are roughly one in three." These lead students to ask where the quoted probabilities come from and what they mean. These probability estimates involve two primary choices: what data are used to describe when past earthquakes happened and what models are used to forecast when future earthquakes will happen. The data come from a 10,000-year record of large paleoearthquakes compiled from subsidence data on land and turbidites, offshore deposits recording submarine slope failure. Earthquakes seem to have happened in clusters of four or five events, separated by gaps. Earthquakes within a cluster occur more frequently and regularly than in the full record. Hence the next earthquake is more likely if we assume that we are in the recent cluster that started about 1700 years ago, than if we assume the cluster is over. Students can explore how changing assumptions drastically changes probability estimates using easy-to-write and display spreadsheets, like those shown below. Insight can also come from baseball analogies. The cluster issue is like deciding whether to assume that a hitter's performance in the next game is better described by his lifetime record, or by the past few games, since he may be hitting unusually well or in a slump. The other big choice is whether to assume that the probability of an earthquake is constant with time, or is small immediately after one occurs and then grows with time. This is like whether to assume that a player's performance is the same from year to year, or changes over their career. Thus saying "the chance of

Development of optimization-based probabilistic earthquake scenarios for the city of Tehran

Science.gov (United States)

Zolfaghari, M. R.; Peyghaleh, E.

2016-01-01

This paper presents the methodology and practical example for the application of optimization process to select earthquake scenarios which best represent probabilistic earthquake hazard in a given region. The method is based on simulation of a large dataset of potential earthquakes, representing the long-term seismotectonic characteristics in a given region. The simulation process uses Monte-Carlo simulation and regional seismogenic source parameters to generate a synthetic earthquake catalogue consisting of a large number of earthquakes, each characterized with magnitude, location, focal depth and fault characteristics. Such catalogue provides full distributions of events in time, space and size; however, demands large computation power when is used for risk assessment, particularly when other sources of uncertainties are involved in the process. To reduce the number of selected earthquake scenarios, a mixed-integer linear program formulation is developed in this study. This approach results in reduced set of optimization-based probabilistic earthquake scenario, while maintaining shape of hazard curves and full probabilistic picture by minimizing the error between hazard curves driven by full and reduced sets of synthetic earthquake scenarios. To test the model, the regional seismotectonic and seismogenic characteristics of northern Iran are used to simulate a set of 10,000-year worth of events consisting of some 84,000 earthquakes. The optimization model is then performed multiple times with various input data, taking into account probabilistic seismic hazard for Tehran city as the main constrains. The sensitivity of the selected scenarios to the user-specified site/return period error-weight is also assessed. The methodology could enhance run time process for full probabilistic earthquake studies like seismic hazard and risk assessment. The reduced set is the representative of the contributions of all possible earthquakes; however, it requires far less

Geomagnetic Dst index forecast based on IMF data only

Directory of Open Access Journals (Sweden)

G. Pallocchia

2006-05-01

Full Text Available In the past years several operational Dst forecasting algorithms, based on both IMF and solar wind plasma parameters, have been developed and used. We describe an Artificial Neural Network (ANN algorithm which calculates the Dst index on the basis of IMF data only and discuss its performance for several individual storms. Moreover, we briefly comment on the physical grounds which allow the Dst forecasting based on IMF only.

Earthquake chemical precursors in groundwater: a review

Science.gov (United States)

Paudel, Shukra Raj; Banjara, Sushant Prasad; Wagle, Amrita; Freund, Friedemann T.

2018-03-01

We review changes in groundwater chemistry as precursory signs for earthquakes. In particular, we discuss pH, total dissolved solids (TDS), electrical conductivity, and dissolved gases in relation to their significance for earthquake prediction or forecasting. These parameters are widely believed to vary in response to seismic and pre-seismic activity. However, the same parameters also vary in response to non-seismic processes. The inability to reliably distinguish between changes caused by seismic or pre-seismic activities from changes caused by non-seismic activities has impeded progress in earthquake science. Short-term earthquake prediction is unlikely to be achieved, however, by pH, TDS, electrical conductivity, and dissolved gas measurements alone. On the other hand, the production of free hydroxyl radicals (•OH), subsequent reactions such as formation of H2O2 and oxidation of As(III) to As(V) in groundwater, have distinctive precursory characteristics. This study deviates from the prevailing mechanical mantra. It addresses earthquake-related non-seismic mechanisms, but focused on the stress-induced electrification of rocks, the generation of positive hole charge carriers and their long-distance propagation through the rock column, plus on electrochemical processes at the rock-water interface.

Tidal controls on earthquake size-frequency statistics

Science.gov (United States)

Ide, S.; Yabe, S.; Tanaka, Y.

2016-12-01

The possibility that tidal stresses can trigger earthquakes is a long-standing issue in seismology. Except in some special cases, a causal relationship between seismicity and the phase of tidal stress has been rejected on the basis of studies using many small events. However, recently discovered deep tectonic tremors are highly sensitive to tidal stress levels, with the relationship being governed by a nonlinear law according to which the tremor rate increases exponentially with increasing stress; thus, slow deformation (and the probability of earthquakes) may be enhanced during periods of large tidal stress. Here, we show the influence of tidal stress on seismicity by calculating histories of tidal shear stress during the 2-week period before earthquakes. Very large earthquakes tend to occur near the time of maximum tidal stress, but this tendency is not obvious for small earthquakes. Rather, we found that tidal stress controls the earthquake size-frequency statistics; i.e., the fraction of large events increases (i.e. the b-value of the Gutenberg-Richter relation decreases) as the tidal shear stress increases. This correlation is apparent in data from the global catalog and in relatively homogeneous regional catalogues of earthquakes in Japan. The relationship is also reasonable, considering the well-known relationship between stress and the b-value. Our findings indicate that the probability of a tiny rock failure expanding to a gigantic rupture increases with increasing tidal stress levels. This finding has clear implications for probabilistic earthquake forecasting.

Non-seismic tsunamis: filling the forecast gap

Science.gov (United States)

Moore, C. W.; Titov, V. V.; Spillane, M. C.

2015-12-01

Earthquakes are the generation mechanism in over 85% of tsunamis. However, non-seismic tsunamis, including those generated by meteorological events, landslides, volcanoes, and asteroid impacts, can inundate significant area and have a large far-field effect. The current National Oceanographic and Atmospheric Administration (NOAA) tsunami forecast system falls short in detecting these phenomena. This study attempts to classify the range of effects possible from these non-seismic threats, and to investigate detection methods appropriate for use in a forecast system. Typical observation platforms are assessed, including DART bottom pressure recorders and tide gauges. Other detection paths include atmospheric pressure anomaly algorithms for detecting meteotsunamis and the early identification of asteroids large enough to produce a regional hazard. Real-time assessment of observations for forecast use can provide guidance to mitigate the effects of a non-seismic tsunami.

The ordered network structure and prediction summary for M ≥ 7 earthquakes in Xinjiang region of China

International Nuclear Information System (INIS)

Men, Ke-Pei; Zhao, Kai

2014-01-01

M ≥ 7 earthquakes have showed an obvious commensurability and orderliness in Xinjiang of China and its adjacent region since 1800. The main orderly values are 30 a x k (k = 1, 2, 3), 11 ∝ 12 a, 41 ∝ 43 a, 18 ∝ 19 a, and 5 ∝ 6 a. In the guidance of the information forecasting theory of Wen-Bo Weng, based on previous research results, combining ordered network structure analysis with complex network technology, we focus on the prediction summary of M ≥ 7 earthquakes by using the ordered network structure, and add new information to further optimize network, hence construct the 2D- and 3D-ordered network structure of M ≥ 7 earthquakes. In this paper, the network structure revealed fully the regularity of seismic activity of M ≥ 7 earthquakes in the study region during the past 210 years. Based on this, the Karakorum M7.1 earthquake in 1996, the M7.9 earthquake on the frontier of Russia, Mongol, and China in 2003, and two Yutian M7.3 earthquakes in 2008 and 2014 were predicted successfully. At the same time, a new prediction opinion is presented that the future two M ≥ 7 earthquakes will probably occur around 2019-2020 and 2025-2026 in this region. The results show that large earthquake occurred in defined region can be predicted. The method of ordered network structure analysis produces satisfactory results for the mid-and-long term prediction of M ≥ 7 earthquakes.

Electricity Price Forecasting Based on AOSVR and Outlier Detection

Institute of Scientific and Technical Information of China (English)

Zhou Dianmin; Gao Lin; Gao Feng

2005-01-01

Electricity price is of the first consideration for all the participants in electric power market and its characteristics are related to both market mechanism and variation in the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability; and because there are outliers in the price data, they should be detected and filtrated in training the forecasting model by regression method. In view of these points, this paper presents an electricity price forecasting method based on accurate on-line support vector regression (AOSVR) and outlier detection. Numerical testing results show that the method is effective in forecasting the electricity prices in electric power market.

Fuzzy forecasting based on fuzzy-trend logical relationship groups.

Science.gov (United States)

Chen, Shyi-Ming; Wang, Nai-Yi

2010-10-01

In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.

Earthquakes clustering based on the magnitude and the depths in Molluca Province

International Nuclear Information System (INIS)

Wattimanela, H. J.; Pasaribu, U. S.; Indratno, S. W.; Puspito, A. N. T.

2015-01-01

In this paper, we present a model to classify the earthquakes occurred in Molluca Province. We use K-Means clustering method to classify the earthquake based on the magnitude and the depth of the earthquake. The result can be used for disaster mitigation and for designing evacuation route in Molluca Province

Earthquakes clustering based on the magnitude and the depths in Molluca Province

Energy Technology Data Exchange (ETDEWEB)

Wattimanela, H. J., E-mail: hwattimaela@yahoo.com [Pattimura University, Ambon (Indonesia); Institute of Technology Bandung, Bandung (Indonesia); Pasaribu, U. S.; Indratno, S. W.; Puspito, A. N. T. [Institute of Technology Bandung, Bandung (Indonesia)

2015-12-22

In this paper, we present a model to classify the earthquakes occurred in Molluca Province. We use K-Means clustering method to classify the earthquake based on the magnitude and the depth of the earthquake. The result can be used for disaster mitigation and for designing evacuation route in Molluca Province.

Evaluation of induced seismicity forecast models in the Induced Seismicity Test Bench

Science.gov (United States)

Király, Eszter; Gischig, Valentin; Zechar, Jeremy; Doetsch, Joseph; Karvounis, Dimitrios; Wiemer, Stefan

2016-04-01

Induced earthquakes often accompany fluid injection, and the seismic hazard they pose threatens various underground engineering projects. Models to monitor and control induced seismic hazard with traffic light systems should be probabilistic, forward-looking, and updated as new data arrive. Here, we propose an Induced Seismicity Test Bench to test and rank such models. We apply the test bench to data from the Basel 2006 and Soultz-sous-Forêts 2004 geothermal stimulation projects, and we assess forecasts from two models that incorporate a different mix of physical understanding and stochastic representation of the induced sequences: Shapiro in Space (SiS) and Hydraulics and Seismics (HySei). SiS is based on three pillars: the seismicity rate is computed with help of the seismogenic index and a simple exponential decay of the seismicity; the magnitude distribution follows the Gutenberg-Richter relation; and seismicity is distributed in space based on smoothing seismicity during the learning period with 3D Gaussian kernels. The HySei model describes seismicity triggered by pressure diffusion with irreversible permeability enhancement. Our results show that neither model is fully superior to the other. HySei forecasts the seismicity rate well, but is only mediocre at forecasting the spatial distribution. On the other hand, SiS forecasts the spatial distribution well but not the seismicity rate. The shut-in phase is a difficult moment for both models in both reservoirs: the models tend to underpredict the seismicity rate around, and shortly after, shut-in. Ensemble models that combine HySei's rate forecast with SiS's spatial forecast outperform each individual model.

Aggregated wind power generation probabilistic forecasting based on particle filter

International Nuclear Information System (INIS)

Li, Pai; Guan, Xiaohong; Wu, Jiang

2015-01-01

Highlights: • A new method for probabilistic forecasting of aggregated wind power generation. • A dynamic system is established based on a numerical weather prediction model. • The new method handles the non-Gaussian and time-varying wind power uncertainties. • Particle filter is applied to forecast predictive densities of wind generation. - Abstract: Probability distribution of aggregated wind power generation in a region is one of important issues for power system daily operation. This paper presents a novel method to forecast the predictive densities of the aggregated wind power generation from several geographically distributed wind farms, considering the non-Gaussian and non-stationary characteristics in wind power uncertainties. Based on a mesoscale numerical weather prediction model, a dynamic system is established to formulate the relationship between the atmospheric and near-surface wind fields of geographically distributed wind farms. A recursively backtracking framework based on the particle filter is applied to estimate the atmospheric state with the near-surface wind power generation measurements, and to forecast the possible samples of the aggregated wind power generation. The predictive densities of the aggregated wind power generation are then estimated based on these predicted samples by a kernel density estimator. In case studies, the new method presented is tested on a 9 wind farms system in Midwestern United States. The testing results that the new method can provide competitive interval forecasts for the aggregated wind power generation with conventional statistical based models, which validates the effectiveness of the new method

Ratio-based lengths of intervals to improve fuzzy time series forecasting.

Science.gov (United States)

Huarng, Kunhuang; Yu, Tiffany Hui-Kuang

2006-04-01

The objective of this study is to explore ways of determining the useful lengths of intervals in fuzzy time series. It is suggested that ratios, instead of equal lengths of intervals, can more properly represent the intervals among observations. Ratio-based lengths of intervals are, therefore, proposed to improve fuzzy time series forecasting. Algebraic growth data, such as enrollments and the stock index, and exponential growth data, such as inventory demand, are chosen as the forecasting targets, before forecasting based on the various lengths of intervals is performed. Furthermore, sensitivity analyses are also carried out for various percentiles. The ratio-based lengths of intervals are found to outperform the effective lengths of intervals, as well as the arbitrary ones in regard to the different statistical measures. The empirical analysis suggests that the ratio-based lengths of intervals can also be used to improve fuzzy time series forecasting.

Short-term solar irradiation forecasting based on Dynamic Harmonic Regression

International Nuclear Information System (INIS)

Trapero, Juan R.; Kourentzes, Nikolaos; Martin, A.

2015-01-01

Solar power generation is a crucial research area for countries that have high dependency on fossil energy sources and is gaining prominence with the current shift to renewable sources of energy. In order to integrate the electricity generated by solar energy into the grid, solar irradiation must be reasonably well forecasted, where deviations of the forecasted value from the actual measured value involve significant costs. The present paper proposes a univariate Dynamic Harmonic Regression model set up in a State Space framework for short-term (1–24 h) solar irradiation forecasting. Time series hourly aggregated as the Global Horizontal Irradiation and the Direct Normal Irradiation will be used to illustrate the proposed approach. This method provides a fast automatic identification and estimation procedure based on the frequency domain. Furthermore, the recursive algorithms applied offer adaptive predictions. The good forecasting performance is illustrated with solar irradiance measurements collected from ground-based weather stations located in Spain. The results show that the Dynamic Harmonic Regression achieves the lowest relative Root Mean Squared Error; about 30% and 47% for the Global and Direct irradiation components, respectively, for a forecast horizon of 24 h ahead. - Highlights: • Solar irradiation forecasts at short-term are required to operate solar power plants. • This paper assesses the Dynamic Harmonic Regression to forecast solar irradiation. • Models are evaluated using hourly GHI and DNI data collected in Spain. • The results show that forecasting accuracy is improved by using the model proposed

SOFT project: a new forecasting system based on satellite data

Science.gov (United States)

Pascual, Ananda; Orfila, A.; Alvarez, Alberto; Hernandez, E.; Gomis, D.; Barth, Alexander; Tintore, Joaquim

2002-01-01

The aim of the SOFT project is to develop a new ocean forecasting system by using a combination of satellite dat, evolutionary programming and numerical ocean models. To achieve this objective two steps are proved: (1) to obtain an accurate ocean forecasting system using genetic algorithms based on satellite data; and (2) to integrate the above new system into existing deterministic numerical models. Evolutionary programming will be employed to build 'intelligent' systems that, learning form the past ocean variability and considering the present ocean state, will be able to infer near future ocean conditions. Validation of the forecast skill will be carried out by comparing the forecasts fields with satellite and in situ observations. Validation with satellite observations will provide the expected errors in the forecasting system. Validation with in situ data will indicate the capabilities of the satellite based forecast information to improve the performance of the numerical ocean models. This later validation will be accomplished considering in situ measurements in a specific oceanographic area at two different periods of time. The first set of observations will be employed to feed the hybrid systems while the second set will be used to validate the hybrid and traditional numerical model results.

Analysis of the Earthquake Impact towards water-based fire extinguishing system

Science.gov (United States)

Lee, J.; Hur, M.; Lee, K.

2015-09-01

Recently, extinguishing system installed in the building when the earthquake occurred at a separate performance requirements. Before the building collapsed during the earthquake, as a function to maintain a fire extinguishing. In particular, the automatic sprinkler fire extinguishing equipment, such as after a massive earthquake without damage to piping also must maintain confidentiality. In this study, an experiment installed in the building during the earthquake, the water-based fire extinguishing saw grasp the impact of the pipe. Experimental structures for water-based fire extinguishing seismic construction step by step, and then applied to the seismic experiment, the building appears in the extinguishing of the earthquake response of the pipe was measured. Construction of acceleration caused by vibration being added to the size and the size of the displacement is measured and compared with the data response of the pipe from the table, thereby extinguishing water piping need to enhance the seismic analysis. Define the seismic design category (SDC) for the four groups in the building structure with seismic criteria (KBC2009) designed according to the importance of the group and earthquake seismic intensity. The event of a real earthquake seismic analysis of Category A and Category B for the seismic design of buildings, the current fire-fighting facilities could have also determined that the seismic performance. In the case of seismic design categories C and D are installed in buildings to preserve the function of extinguishing the required level of seismic retrofit design is determined.

Combined time-varying forecast based on the proper scoring approach for wind power generation

DEFF Research Database (Denmark)

Chen, Xingying; Jiang, Yu; Yu, Kun

2017-01-01

Compared with traditional point forecasts, combined forecast have been proposed as an effective method to provide more accurate forecasts than individual model. However, the literature and research focus on wind-power combined forecasts are relatively limited. Here, based on forecasting error...... distribution, a proper scoring approach is applied to combine plausible models to form an overall time-varying model for the next day forecasts, rather than weights-based combination. To validate the effectiveness of the proposed method, real data of 3 years were used for testing. Simulation results...... demonstrate that the proposed method improves the accuracy of overall forecasts, even compared with a numerical weather prediction....

Knowledge base about earthquakes as a tool to minimize strong events consequences

Science.gov (United States)

Frolova, Nina; Bonnin, Jean; Larionov, Valery; Ugarov, Alexander; Kijko, Andrzej

2017-04-01

The paper describes the structure and content of the knowledge base on physical and socio-economical consequences of damaging earthquakes, which may be used for calibration of near real-time loss assessment systems based on simulation models for shaking intensity, damage to buildings and casualties estimates. Such calibration allows to compensate some factors which influence on reliability of expected damage and loss assessment in "emergency" mode. The knowledge base contains the description of past earthquakes' consequences for the area under study. It also includes the current distribution of built environment and population at the time of event occurrence. Computer simulation of the recorded in knowledge base events allow to determine the sets of regional calibration coefficients, including rating of seismological surveys, peculiarities of shaking intensity attenuation and changes in building stock and population distribution, in order to provide minimum error of damaging earthquakes loss estimations in "emergency" mode. References 1. Larionov, V., Frolova, N: Peculiarities of seismic vulnerability estimations. In: Natural Hazards in Russia, volume 6: Natural Risks Assessment and Management, Publishing House "Kruk", Moscow, 120-131, 2003. 2. Frolova, N., Larionov, V., Bonnin, J.: Data Bases Used In Worlwide Systems For Earthquake Loss Estimation In Emergency Mode: Wenchuan Earthquake. In Proc. TIEMS2010 Conference, Beijing, China, 2010. 3. Frolova N. I., Larionov V. I., Bonnin J., Sushchev S. P., Ugarov A. N., Kozlov M. A. Loss Caused by Earthquakes: Rapid Estimates. Natural Hazards Journal of the International Society for the Prevention and Mitigation of Natural Hazards, vol.84, ISSN 0921-030, Nat Hazards DOI 10.1007/s11069-016-2653

Three-dimensional visualization of ensemble weather forecasts - Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

Science.gov (United States)

Rautenhaus, M.; Grams, C. M.; Schäfler, A.; Westermann, R.

2015-07-01

We present the application of interactive three-dimensional (3-D) visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 (THORPEX - North Atlantic Waveguide and Downstream Impact Experiment) campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts (WCBs) has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the European Centre for Medium Range Weather Forecasts (ECMWF) ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and grid spacing of the forecast wind field. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (3 to 7 days before take-off).

3-D visualization of ensemble weather forecasts - Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

Science.gov (United States)

Rautenhaus, M.; Grams, C. M.; Schäfler, A.; Westermann, R.

2015-02-01

We present the application of interactive 3-D visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the ECMWF ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and forecast wind field resolution. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (three to seven days before take-off).

Development of Physics and Control of Multiple Forcing Mechanisms for the Alaska Tsunami Forecast Model

Science.gov (United States)

Bahng, B.; Whitmore, P.; Macpherson, K. A.; Knight, W. R.

2016-12-01

The Alaska Tsunami Forecast Model (ATFM) is a numerical model used to forecast propagation and inundation of tsunamis generated by earthquakes or other mechanisms in either the Pacific Ocean, Atlantic Ocean or Gulf of Mexico. At the U.S. National Tsunami Warning Center (NTWC), the use of the model has been mainly for tsunami pre-computation due to earthquakes. That is, results for hundreds of hypothetical events are computed before alerts, and are accessed and calibrated with observations during tsunamis to immediately produce forecasts. The model has also been used for tsunami hindcasting due to submarine landslides and due to atmospheric pressure jumps, but in a very case-specific and somewhat limited manner. ATFM uses the non-linear, depth-averaged, shallow-water equations of motion with multiply nested grids in two-way communications between domains of each parent-child pair as waves approach coastal waters. The shallow-water wave physics is readily applicable to all of the above tsunamis as well as to tides. Recently, the model has been expanded to include multiple forcing mechanisms in a systematic fashion, and to enhance the model physics for non-earthquake events.ATFM is now able to handle multiple source mechanisms, either individually or jointly, which include earthquake, submarine landslide, meteo-tsunami and tidal forcing. As for earthquakes, the source can be a single unit source or multiple, interacting source blocks. Horizontal slip contribution can be added to the sea-floor displacement. The model now includes submarine landslide physics, modeling the source either as a rigid slump, or as a viscous fluid. Additional shallow-water physics have been implemented for the viscous submarine landslides. With rigid slumping, any trajectory can be followed. As for meteo-tsunami, the forcing mechanism is capable of following any trajectory shape. Wind stress physics has also been implemented for the meteo-tsunami case, if required. As an example of multiple

A Novel Flood Forecasting Method Based on Initial State Variable Correction

Directory of Open Access Journals (Sweden)

Kuang Li

2017-12-01

Full Text Available The influence of initial state variables on flood forecasting accuracy by using conceptual hydrological models is analyzed in this paper and a novel flood forecasting method based on correction of initial state variables is proposed. The new method is abbreviated as ISVC (Initial State Variable Correction. The ISVC takes the residual between the measured and forecasted flows during the initial period of the flood event as the objective function, and it uses a particle swarm optimization algorithm to correct the initial state variables, which are then used to drive the flood forecasting model. The historical flood events of 11 watersheds in south China are forecasted and verified, and important issues concerning the ISVC application are then discussed. The study results show that the ISVC is effective and applicable in flood forecasting tasks. It can significantly improve the flood forecasting accuracy in most cases.

Quantitative Earthquake Prediction on Global and Regional Scales

International Nuclear Information System (INIS)

Kossobokov, Vladimir G.

2006-01-01

The Earth is a hierarchy of volumes of different size. Driven by planetary convection these volumes are involved into joint and relative movement. The movement is controlled by a wide variety of processes on and around the fractal mesh of boundary zones, and does produce earthquakes. This hierarchy of movable volumes composes a large non-linear dynamical system. Prediction of such a system in a sense of extrapolation of trajectory into the future is futile. However, upon coarse-graining the integral empirical regularities emerge opening possibilities of prediction in a sense of the commonly accepted consensus definition worked out in 1976 by the US National Research Council. Implications of the understanding hierarchical nature of lithosphere and its dynamics based on systematic monitoring and evidence of its unified space-energy similarity at different scales help avoiding basic errors in earthquake prediction claims. They suggest rules and recipes of adequate earthquake prediction classification, comparison and optimization. The approach has already led to the design of reproducible intermediate-term middle-range earthquake prediction technique. Its real-time testing aimed at prediction of the largest earthquakes worldwide has proved beyond any reasonable doubt the effectiveness of practical earthquake forecasting. In the first approximation, the accuracy is about 1-5 years and 5-10 times the anticipated source dimension. Further analysis allows reducing spatial uncertainty down to 1-3 source dimensions, although at a cost of additional failures-to-predict. Despite of limited accuracy a considerable damage could be prevented by timely knowledgeable use of the existing predictions and earthquake prediction strategies. The December 26, 2004 Indian Ocean Disaster seems to be the first indication that the methodology, designed for prediction of M8.0+ earthquakes can be rescaled for prediction of both smaller magnitude earthquakes (e.g., down to M5.5+ in Italy) and

Quantitative Earthquake Prediction on Global and Regional Scales

Science.gov (United States)

Kossobokov, Vladimir G.

2006-03-01

The Earth is a hierarchy of volumes of different size. Driven by planetary convection these volumes are involved into joint and relative movement. The movement is controlled by a wide variety of processes on and around the fractal mesh of boundary zones, and does produce earthquakes. This hierarchy of movable volumes composes a large non-linear dynamical system. Prediction of such a system in a sense of extrapolation of trajectory into the future is futile. However, upon coarse-graining the integral empirical regularities emerge opening possibilities of prediction in a sense of the commonly accepted consensus definition worked out in 1976 by the US National Research Council. Implications of the understanding hierarchical nature of lithosphere and its dynamics based on systematic monitoring and evidence of its unified space-energy similarity at different scales help avoiding basic errors in earthquake prediction claims. They suggest rules and recipes of adequate earthquake prediction classification, comparison and optimization. The approach has already led to the design of reproducible intermediate-term middle-range earthquake prediction technique. Its real-time testing aimed at prediction of the largest earthquakes worldwide has proved beyond any reasonable doubt the effectiveness of practical earthquake forecasting. In the first approximation, the accuracy is about 1-5 years and 5-10 times the anticipated source dimension. Further analysis allows reducing spatial uncertainty down to 1-3 source dimensions, although at a cost of additional failures-to-predict. Despite of limited accuracy a considerable damage could be prevented by timely knowledgeable use of the existing predictions and earthquake prediction strategies. The December 26, 2004 Indian Ocean Disaster seems to be the first indication that the methodology, designed for prediction of M8.0+ earthquakes can be rescaled for prediction of both smaller magnitude earthquakes (e.g., down to M5.5+ in Italy) and

Robust Building Energy Load Forecasting Using Physically-Based Kernel Models

Directory of Open Access Journals (Sweden)

Anand Krishnan Prakash

2018-04-01

Full Text Available Robust and accurate building energy load forecasting is important for helping building managers and utilities to plan, budget, and strategize energy resources in advance. With recent prevalent adoption of smart-meters in buildings, a significant amount of building energy consumption data became available. Many studies have developed physics-based white box models and data-driven black box models to predict building energy consumption; however, they require extensive prior knowledge about building system, need a large set of training data, or lack robustness to different forecasting scenarios. In this paper, we introduce a new building energy forecasting method based on Gaussian Process Regression (GPR that incorporates physical insights about load data characteristics to improve accuracy while reducing training requirements. The GPR is a non-parametric regression method that models the data as a joint Gaussian distribution with mean and covariance functions and forecast using the Bayesian updating. We model the covariance function of the GPR to reflect the data patterns in different forecasting horizon scenarios, as prior knowledge. Our method takes advantage of the modeling flexibility and computational efficiency of the GPR while benefiting from the physical insights to further improve the training efficiency and accuracy. We evaluate our method with three field datasets from two university campuses (Carnegie Mellon University and Stanford University for both short- and long-term load forecasting. The results show that our method performs more accurately, especially when the training dataset is small, compared to other state-of-the-art forecasting models (up to 2.95 times smaller prediction error.

Characterization of tsunamigenic earthquake in Java region based on seismic wave calculation

Energy Technology Data Exchange (ETDEWEB)

Pribadi, Sugeng, E-mail: sugengpribadimsc@gmail.com [Badan Meteorologi Klimatologi Geofisika, Jl Angkasa I No. 2 Jakarta (Indonesia); Afnimar,; Puspito, Nanang T.; Ibrahim, Gunawan [Institut Teknologi Bandung, Jl. Ganesha 10, Bandung 40132 (Indonesia)

2014-03-24

This study is to characterize the source mechanism of tsunamigenic earthquake based on seismic wave calculation. The source parameter used are the ratio (Θ) between the radiated seismic energy (E) and seismic moment (M{sub o}), moment magnitude (M{sub W}), rupture duration (T{sub o}) and focal mechanism. These determine the types of tsunamigenic earthquake and tsunami earthquake. We calculate the formula using the teleseismic wave signal processing with the initial phase of P wave with bandpass filter 0.001 Hz to 5 Hz. The amount of station is 84 broadband seismometer with far distance of 30° to 90°. The 2 June 1994 Banyuwangi earthquake with M{sub W}=7.8 and the 17 July 2006 Pangandaran earthquake with M{sub W}=7.7 include the criteria as a tsunami earthquake which distributed about ratio Θ=−6.1, long rupture duration To>100 s and high tsunami H>7 m. The 2 September 2009 Tasikmalaya earthquake with M{sub W}=7.2, Θ=−5.1 and To=27 s which characterized as a small tsunamigenic earthquake.

Characterization of tsunamigenic earthquake in Java region based on seismic wave calculation

International Nuclear Information System (INIS)

Pribadi, Sugeng; Afnimar,; Puspito, Nanang T.; Ibrahim, Gunawan

2014-01-01

This study is to characterize the source mechanism of tsunamigenic earthquake based on seismic wave calculation. The source parameter used are the ratio (Θ) between the radiated seismic energy (E) and seismic moment (M o ), moment magnitude (M W ), rupture duration (T o ) and focal mechanism. These determine the types of tsunamigenic earthquake and tsunami earthquake. We calculate the formula using the teleseismic wave signal processing with the initial phase of P wave with bandpass filter 0.001 Hz to 5 Hz. The amount of station is 84 broadband seismometer with far distance of 30° to 90°. The 2 June 1994 Banyuwangi earthquake with M W =7.8 and the 17 July 2006 Pangandaran earthquake with M W =7.7 include the criteria as a tsunami earthquake which distributed about ratio Θ=−6.1, long rupture duration To>100 s and high tsunami H>7 m. The 2 September 2009 Tasikmalaya earthquake with M W =7.2, Θ=−5.1 and To=27 s which characterized as a small tsunamigenic earthquake

Response of base isolated structure during strong ground motions beyond design earthquakes

International Nuclear Information System (INIS)

Yabana, Shuichi; Ishida, Katsuhiko; Shiojiri, Hiroo

1991-01-01

In Japan, some base isolated structures for fast breeder reactors (FBR) are tried to design. When a base isolated structure are designed, the relative displacement of isolators are generally limited so sa to be remain in linear state of those during design earthquakes. But to estimate safety margin of a base isolated structure, the response of that until the failure must be obtained experimentally to analytically during strong ground motions of beyond design earthquake. The aim of this paper is to investigate the response of a base isolated structure when the stiffness of the isolators hardens and to simulate the response during strong ground motions of beyond design earthquakes. The optimum characteristics of isolators, with which the margin of the structure are increased, are discussed. (author)

Ionospheric earthquake effects detection based on Total Electron Content (TEC) GPS Correlation

Science.gov (United States)

Sunardi, Bambang; Muslim, Buldan; Eka Sakya, Andi; Rohadi, Supriyanto; Sulastri; Murjaya, Jaya

2018-03-01

Advances in science and technology showed that ground-based GPS receiver was able to detect ionospheric Total Electron Content (TEC) disturbances caused by various natural phenomena such as earthquakes. One study of Tohoku (Japan) earthquake, March 11, 2011, magnitude M 9.0 showed TEC fluctuations observed from GPS observation network spread around the disaster area. This paper discussed the ionospheric earthquake effects detection using TEC GPS data. The case studies taken were Kebumen earthquake, January 25, 2014, magnitude M 6.2, Sumba earthquake, February 12, 2016, M 6.2 and Halmahera earthquake, February 17, 2016, M 6.1. TEC-GIM (Global Ionosphere Map) correlation methods for 31 days were used to monitor TEC anomaly in ionosphere. To ensure the geomagnetic disturbances due to solar activity, we also compare with Dst index in the same time window. The results showed anomalous ratio of correlation coefficient deviation to its standard deviation upon occurrences of Kebumen and Sumba earthquake, but not detected a similar anomaly for the Halmahera earthquake. It was needed a continous monitoring of TEC GPS data to detect the earthquake effects in ionosphere. This study giving hope in strengthening the earthquake effect early warning system using TEC GPS data. The method development of continuous TEC GPS observation derived from GPS observation network that already exists in Indonesia is needed to support earthquake effects early warning systems.

Scientific, Engineering, and Financial Factors of the 1989 Human-Triggered Newcastle Earthquake in Australia

Science.gov (United States)

Klose, C. D.

2006-12-01

This presentation emphasizes the dualism of natural resources exploitation and economic growth versus geomechanical pollution and risks of human-triggered earthquakes. Large-scale geoengineering activities, e.g., mining, reservoir impoundment, oil/gas production, water exploitation or fluid injection, alter pre-existing lithostatic stress states in the earth's crust and are anticipated to trigger earthquakes. Such processes of in- situ stress alteration are termed geomechanical pollution. Moreover, since the 19th century more than 200 earthquakes have been documented worldwide with a seismic moment magnitude of 4.5losses of triggered earthquakes. An hazard assessment, based on a geomechanical crust model, shows that only four deep coal mines were responsible for triggering this severe earthquake. A small-scale economic risk assessment identifies that the financial loss due to earthquake damage has reduced mining profits that have been re-invested in the Newcastle region for over two centuries beginning in 1801. Furthermore, large-scale economic risk assessment reveals that the financial loss is equivalent to 26% of the Australian Gross Domestic Product (GDP) growth in 1988/89. These costs account for 13% of the total costs of all natural disasters (e.g., flooding, drought, wild fires) and 94% of the costs of all earthquakes recorded in Australia between 1967 and 1999. In conclusion, the increasing number and size of geoengineering activities, such as coal mining near Newcastle or planned carbon dioxide Geosequestration initiatives, represent a growing hazard potential, which can negatively affect socio-economic growth and sustainable development. Finally, hazard and risk degrees, based on geomechanical-mathematical models, can be forecasted in space and over time for urban planning in order to prevent economic losses of human-triggered earthquakes in the future.

Amplitude of foreshocks as a possible seismic precursor to earthquakes

Science.gov (United States)

Lindh, A.G.

1978-01-01

In recent years, we have made significant progress in being able to recognize the long-range pattern of events that precede large earthquakes. For example, in a recent issue of the Earthquake Information Bulletin, we saw how the pioneering work of S.A. Fedotov of the U.S.S.R in the Kamchatka-Kurile Islands region has been applied worldwide to forecast where large, shallow earthquakes might occur in the next decades. Indeed, such a "seismic gap" off the coast of Alaska was filled by the 1972 Sitka earthquake. Promising results are slowly accumulating from other techniques that suggest that intermediate-term precursors might also be seen: among these are tilt and geomagnetic anomalies and anomalous land uplift. But the crucial point remains that short-term precursors (days to hours) will be needed in many cases if there is to be a significant saving of lives. 

Urban MEMS based seismic network for post-earthquakes rapid disaster assessment

Science.gov (United States)

D'Alessandro, Antonino; Luzio, Dario; D'Anna, Giuseppe

2014-05-01

worship. The waveforms recorded could be promptly used to determine ground-shaking parameters, like peak ground acceleration/velocity/displacement, Arias and Housner intensity, that could be all used to create, few seconds after a strong earthquakes, shaking maps at urban scale. These shaking maps could allow to quickly identify areas of the town center that have had the greatest earthquake resentment. When a strong seismic event occur, the beginning of the ground motion observed at the site could be used to predict the ensuing ground motion at the same site and so to realize a short term earthquake early warning system. The data acquired after a moderate magnitude earthquake, would provide valuable information for the detail seismic microzonation of the area based on direct earthquake shaking observations rather than from a model-based or indirect methods. In this work, we evaluate the feasibility and effectiveness of such seismic network taking in to account both technological, scientific and economic issues. For this purpose, we have simulated the creation of a MEMS based urban seismic network in a medium size city. For the selected town, taking into account the instrumental specifics, the array geometry and the environmental noise, we investigated the ability of the planned network to detect and measure earthquakes of different magnitude generated from realistic near seismogentic sources.

Tsunami simulation method initiated from waveforms observed by ocean bottom pressure sensors for real-time tsunami forecast; Applied for 2011 Tohoku Tsunami

Science.gov (United States)

Tanioka, Yuichiro

2017-04-01

After tsunami disaster due to the 2011 Tohoku-oki great earthquake, improvement of the tsunami forecast has been an urgent issue in Japan. National Institute of Disaster Prevention is installing a cable network system of earthquake and tsunami observation (S-NET) at the ocean bottom along the Japan and Kurile trench. This cable system includes 125 pressure sensors (tsunami meters) which are separated by 30 km. Along the Nankai trough, JAMSTEC already installed and operated the cable network system of seismometers and pressure sensors (DONET and DONET2). Those systems are the most dense observation network systems on top of source areas of great underthrust earthquakes in the world. Real-time tsunami forecast has depended on estimation of earthquake parameters, such as epicenter, depth, and magnitude of earthquakes. Recently, tsunami forecast method has been developed using the estimation of tsunami source from tsunami waveforms observed at the ocean bottom pressure sensors. However, when we have many pressure sensors separated by 30km on top of the source area, we do not need to estimate the tsunami source or earthquake source to compute tsunami. Instead, we can initiate a tsunami simulation from those dense tsunami observed data. Observed tsunami height differences with a time interval at the ocean bottom pressure sensors separated by 30 km were used to estimate tsunami height distribution at a particular time. In our new method, tsunami numerical simulation was initiated from those estimated tsunami height distribution. In this paper, the above method is improved and applied for the tsunami generated by the 2011 Tohoku-oki great earthquake. Tsunami source model of the 2011 Tohoku-oki great earthquake estimated using observed tsunami waveforms, coseimic deformation observed by GPS and ocean bottom sensors by Gusman et al. (2012) is used in this study. The ocean surface deformation is computed from the source model and used as an initial condition of tsunami

A new approach on seismic mortality estimations based on average population density

Science.gov (United States)

Zhu, Xiaoxin; Sun, Baiqing; Jin, Zhanyong

2016-12-01

This study examines a new methodology to predict the final seismic mortality from earthquakes in China. Most studies established the association between mortality estimation and seismic intensity without considering the population density. In China, however, the data are not always available, especially when it comes to the very urgent relief situation in the disaster. And the population density varies greatly from region to region. This motivates the development of empirical models that use historical death data to provide the path to analyze the death tolls for earthquakes. The present paper employs the average population density to predict the final death tolls in earthquakes using a case-based reasoning model from realistic perspective. To validate the forecasting results, historical data from 18 large-scale earthquakes occurred in China are used to estimate the seismic morality of each case. And a typical earthquake case occurred in the northwest of Sichuan Province is employed to demonstrate the estimation of final death toll. The strength of this paper is that it provides scientific methods with overall forecast errors lower than 20 %, and opens the door for conducting final death forecasts with a qualitative and quantitative approach. Limitations and future research are also analyzed and discussed in the conclusion.

Pollutant forecasting error based on persistence of wind direction

International Nuclear Information System (INIS)

Cooper, R.E.

1978-01-01

The purpose of this report is to provide a means of estimating the reliability of forecasts of downwind pollutant concentrations from atmospheric puff releases. These forecasts are based on assuming the persistence of wind direction as determined at the time of release. This initial forecast will be used to deploy survey teams, to predict population centers that may be affected, and to estimate the amount of time available for emergency response. Reliability of forecasting is evaluated by developing a cumulative probability distribution of error as a function of lapsed time following an assumed release. The cumulative error is determined by comparing the forecast pollutant concentration with the concentration measured by sampling along the real-time meteorological trajectory. It may be concluded that the assumption of meteorological persistence for emergency response is not very good for periods longer than 3 hours. Even within this period, the possibiity for large error exists due to wind direction shifts. These shifts could affect population areas totally different from those areas first indicated

Demand forecast model based on CRM

Science.gov (United States)

Cai, Yuancui; Chen, Lichao

2006-11-01

With interiorizing day by day management thought that regarding customer as the centre, forecasting customer demand becomes more and more important. In the demand forecast of customer relationship management, the traditional forecast methods have very great limitation because much uncertainty of the demand, these all require new modeling to meet the demands of development. In this paper, the notion is that forecasting the demand according to characteristics of the potential customer, then modeling by it. The model first depicts customer adopting uniform multiple indexes. Secondly, the model acquires characteristic customers on the basis of data warehouse and the technology of data mining. The last, there get the most similar characteristic customer by their comparing and forecast the demands of new customer by the most similar characteristic customer.

Groundwater electrical conductivity and soil radon gas monitoring for earthquake precursory studies in Koyna, India

International Nuclear Information System (INIS)

Reddy, D.V.; Nagabhushanam, P.

2011-01-01

Research highlights: → It is the first hydrochemical precursory study in the Koyna region, India. → Discrete conductivity measurements indicated progressive increase for 4 years. → Strong precursory EC change observed 40 h before the M 5.1 earthquake. → Precursory increase of soil Rn gas 20 days earlier than earthquakes M 4.7 and 5.1. → On-line monitoring of these parameters may help in earthquake forecast. - Abstract: Hourly monitoring of electrical conductivity (EC) of groundwater along with groundwater levels in the 210 m deep boreholes (specially drilled for pore pressure/earthquake studies) and soil Rn gas at 60 cm below ground level in real time, in the Koyna-Warna region (characterized by basaltic rocks, >1500 m thick, and dotted with several sets of fault systems), western India, provided strong precursory signatures in response to two earthquakes (M 4.7 on 14/11/09, and M 5.1 on 12/12/09) that occurred in the study region. The EC measured in Govare well water showed precursory perturbations about 40 h prior to the M 5.1 earthquake and continued further for about 20 h after the earthquake. In response to the M 4.7 earthquake, there were EC perturbations 8 days after the earthquake. In another well (Koyna) which is located 4 km north of Govare well, no precursory signatures were found for the M 4.7 earthquake, while for M 5.1 earthquake, post-seismic precursors were found 18 days after the earthquake. Increased porosity and reduced pressure head accompanied by mixing of a freshwater component from the top zone due to earthquakes are the suggested mechanisms responsible for the observed anomalies in EC. Another parameter, soil Rn gas showed relatively proportional strength signals corresponding to these two earthquakes. In both the cases, the pre-seismic increase in Rn concentration started about 20 days in advance. The co-seismic drop in Rn levels was less by 30% from its peak value for the M 4.7 earthquake and 50% for the M 5.1 earthquake. The Rn

Geological and historical evidence of irregular recurrent earthquakes in Japan.

Science.gov (United States)

Satake, Kenji

2015-10-28

Great (M∼8) earthquakes repeatedly occur along the subduction zones around Japan and cause fault slip of a few to several metres releasing strains accumulated from decades to centuries of plate motions. Assuming a simple 'characteristic earthquake' model that similar earthquakes repeat at regular intervals, probabilities of future earthquake occurrence have been calculated by a government committee. However, recent studies on past earthquakes including geological traces from giant (M∼9) earthquakes indicate a variety of size and recurrence interval of interplate earthquakes. Along the Kuril Trench off Hokkaido, limited historical records indicate that average recurrence interval of great earthquakes is approximately 100 years, but the tsunami deposits show that giant earthquakes occurred at a much longer interval of approximately 400 years. Along the Japan Trench off northern Honshu, recurrence of giant earthquakes similar to the 2011 Tohoku earthquake with an interval of approximately 600 years is inferred from historical records and tsunami deposits. Along the Sagami Trough near Tokyo, two types of Kanto earthquakes with recurrence interval of a few hundred years and a few thousand years had been recognized, but studies show that the recent three Kanto earthquakes had different source extents. Along the Nankai Trough off western Japan, recurrence of great earthquakes with an interval of approximately 100 years has been identified from historical literature, but tsunami deposits indicate that the sizes of the recurrent earthquakes are variable. Such variability makes it difficult to apply a simple 'characteristic earthquake' model for the long-term forecast, and several attempts such as use of geological data for the evaluation of future earthquake probabilities or the estimation of maximum earthquake size in each subduction zone are being conducted by government committees. © 2015 The Author(s).

Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning

Directory of Open Access Journals (Sweden)

Ya’nan Wang

2016-01-01

Full Text Available Fuzzy sets theory cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. In this regard, an intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to divide the universe of discourse into unequal intervals, and a more objective technique for ascertaining the membership function and nonmembership function of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on intuitionistic fuzzy approximate reasoning are established. At last, contrast experiments on the enrollments of the University of Alabama and the Taiwan Stock Exchange Capitalization Weighted Stock Index are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.

Deep Neural Network Based Demand Side Short Term Load Forecasting

Directory of Open Access Journals (Sweden)

Seunghyoung Ryu

2016-12-01

Full Text Available In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN, a double seasonal Holt–Winters (DSHW model and the autoregressive integrated moving average (ARIMA. The mean absolute percentage error (MAPE and relative root mean square error (RRMSE are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.

Engineering Seismic Base Layer for Defining Design Earthquake Motion

International Nuclear Information System (INIS)

Yoshida, Nozomu

2008-01-01

Engineer's common sense that incident wave is common in a widespread area at the engineering seismic base layer is shown not to be correct. An exhibiting example is first shown, which indicates that earthquake motion at the ground surface evaluated by the analysis considering the ground from a seismic bedrock to a ground surface simultaneously (continuous analysis) is different from the one by the analysis in which the ground is separated at the engineering seismic base layer and analyzed separately (separate analysis). The reason is investigated by several approaches. Investigation based on eigen value problem indicates that the first predominant period in the continuous analysis cannot be found in the separate analysis, and predominant period at higher order does not match in the upper and lower ground in the separate analysis. The earthquake response analysis indicates that reflected wave at the engineering seismic base layer is not zero, which indicates that conventional engineering seismic base layer does not work as expected by the term ''base''. All these results indicate that wave that goes down to the deep depths after reflecting in the surface layer and again reflects at the seismic bedrock cannot be neglected in evaluating the response at the ground surface. In other words, interaction between the surface layer and/or layers between seismic bedrock and engineering seismic base layer cannot be neglected in evaluating the earthquake motion at the ground surface

Short-Term Forecasting of Taiwanese Earthquakes Using a Universal Model of Fusion-Fission Processes

NARCIS (Netherlands)

Cheong, S.A.; Tan, T.L.; Chen, C.-C.; Chang, W.-L.; Liu, Z.; Chew, L.Y.; Sloot, P.M.A.; Johnson, N.F.

2014-01-01

Predicting how large an earthquake can be, where and when it will strike remains an elusive goal in spite of the ever-increasing volume of data collected by earth scientists. In this paper, we introduce a universal model of fusion-fission processes that can be used to predict earthquakes starting

An EMD–SARIMA-Based Modeling Approach for Air Traffic Forecasting

Directory of Open Access Journals (Sweden)

Wei Nai

2017-12-01

Full Text Available The ever-increasing air traffic demand in China has brought huge pressure on the planning and management of, and investment in, air terminals as well as airline companies. In this context, accurate and adequate short-term air traffic forecasting is essential for the operations of those entities. In consideration of such a problem, a hybrid air traffic forecasting model based on empirical mode decomposition (EMD and seasonal auto regressive integrated moving average (SARIMA has been proposed in this paper. The model proposed decomposes the original time series into components at first, and models each component with the SARIMA forecasting model, then integrates all the models together to form the final combined forecast result. By using the monthly air cargo and passenger flow data from the years 2006 to 2014 available at the official website of the Civil Aviation Administration of China (CAAC, the effectiveness in forecasting of the model proposed has been demonstrated, and by a horizontal performance comparison between several other widely used forecasting models, the advantage of the proposed model has also been proved.

GIS Based System for Post-Earthquake Crisis Managment Using Cellular Network

Science.gov (United States)

Raeesi, M.; Sadeghi-Niaraki, A.

2013-09-01

Earthquakes are among the most destructive natural disasters. Earthquakes happen mainly near the edges of tectonic plates, but they may happen just about anywhere. Earthquakes cannot be predicted. Quick response after disasters, like earthquake, decreases loss of life and costs. Massive earthquakes often cause structures to collapse, trapping victims under dense rubble for long periods of time. After the earthquake and destroyed some areas, several teams are sent to find the location of the destroyed areas. The search and rescue phase usually is maintained for many days. Time reduction for surviving people is very important. A Geographical Information System (GIS) can be used for decreasing response time and management in critical situations. Position estimation in short period of time time is important. This paper proposes a GIS based system for post-earthquake disaster management solution. This system relies on several mobile positioning methods such as cell-ID and TA method, signal strength method, angel of arrival method, time of arrival method and time difference of arrival method. For quick positioning, the system can be helped by any person who has a mobile device. After positioning and specifying the critical points, the points are sent to a central site for managing the procedure of quick response for helping. This solution establishes a quick way to manage the post-earthquake crisis.

GIS BASED SYSTEM FOR POST-EARTHQUAKE CRISIS MANAGMENT USING CELLULAR NETWORK

Directory of Open Access Journals (Sweden)

M. Raeesi

2013-09-01

Full Text Available Earthquakes are among the most destructive natural disasters. Earthquakes happen mainly near the edges of tectonic plates, but they may happen just about anywhere. Earthquakes cannot be predicted. Quick response after disasters, like earthquake, decreases loss of life and costs. Massive earthquakes often cause structures to collapse, trapping victims under dense rubble for long periods of time. After the earthquake and destroyed some areas, several teams are sent to find the location of the destroyed areas. The search and rescue phase usually is maintained for many days. Time reduction for surviving people is very important. A Geographical Information System (GIS can be used for decreasing response time and management in critical situations. Position estimation in short period of time time is important. This paper proposes a GIS based system for post–earthquake disaster management solution. This system relies on several mobile positioning methods such as cell-ID and TA method, signal strength method, angel of arrival method, time of arrival method and time difference of arrival method. For quick positioning, the system can be helped by any person who has a mobile device. After positioning and specifying the critical points, the points are sent to a central site for managing the procedure of quick response for helping. This solution establishes a quick way to manage the post–earthquake crisis.

Forecast Combinations

OpenAIRE

Timmermann, Allan G

2005-01-01

Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex-ante best individual forecasting model. Moreover, simple combinations that ignore correlations between forecast errors often dominate more refined combination schemes aimed at estimating the theoretically optimal combination weights. In this paper we analyse theoretically the factors that determine the advantages from combining forecasts (for example, the d...

Short-term Power Load Forecasting Based on Balanced KNN

Science.gov (United States)

Lv, Xianlong; Cheng, Xingong; YanShuang; Tang, Yan-mei

2018-03-01

To improve the accuracy of load forecasting, a short-term load forecasting model based on balanced KNN algorithm is proposed; According to the load characteristics, the historical data of massive power load are divided into scenes by the K-means algorithm; In view of unbalanced load scenes, the balanced KNN algorithm is proposed to classify the scene accurately; The local weighted linear regression algorithm is used to fitting and predict the load; Adopting the Apache Hadoop programming framework of cloud computing, the proposed algorithm model is parallelized and improved to enhance its ability of dealing with massive and high-dimension data. The analysis of the household electricity consumption data for a residential district is done by 23-nodes cloud computing cluster, and experimental results show that the load forecasting accuracy and execution time by the proposed model are the better than those of traditional forecasting algorithm.

Fuzzy logic-based analogue forecasting and hybrid modelling of horizontal visibility

Science.gov (United States)

Tuba, Zoltán; Bottyán, Zsolt

2018-04-01

Forecasting visibility is one of the greatest challenges in aviation meteorology. At the same time, high accuracy visibility forecasts can significantly reduce or make avoidable weather-related risk in aviation as well. To improve forecasting visibility, this research links fuzzy logic-based analogue forecasting and post-processed numerical weather prediction model outputs in hybrid forecast. Performance of analogue forecasting model was improved by the application of Analytic Hierarchy Process. Then, linear combination of the mentioned outputs was applied to create ultra-short term hybrid visibility prediction which gradually shifts the focus from statistical to numerical products taking their advantages during the forecast period. It gives the opportunity to bring closer the numerical visibility forecast to the observations even it is wrong initially. Complete verification of categorical forecasts was carried out; results are available for persistence and terminal aerodrome forecasts (TAF) as well in order to compare. The average value of Heidke Skill Score (HSS) of examined airports of analogue and hybrid forecasts shows very similar results even at the end of forecast period where the rate of analogue prediction in the final hybrid output is 0.1-0.2 only. However, in case of poor visibility (1000-2500 m), hybrid (0.65) and analogue forecasts (0.64) have similar average of HSS in the first 6 h of forecast period, and have better performance than persistence (0.60) or TAF (0.56). Important achievement that hybrid model takes into consideration physics and dynamics of the atmosphere due to the increasing part of the numerical weather prediction. In spite of this, its performance is similar to the most effective visibility forecasting methods and does not follow the poor verification results of clearly numerical outputs.

Spatial forecast of landslides in three gorges based on spatial data mining.

Science.gov (United States)

Wang, Xianmin; Niu, Ruiqing

2009-01-01

The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc). China-Brazil Earth Resources Satellite (Cbers) images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County) in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods.

Short-Term State Forecasting-Based Optimal Voltage Regulation in Distribution Systems: Preprint

Energy Technology Data Exchange (ETDEWEB)

Yang, Rui; Jiang, Huaiguang; Zhang, Yingchen

2017-05-17

A novel short-term state forecasting-based optimal power flow (OPF) approach for distribution system voltage regulation is proposed in this paper. An extreme learning machine (ELM) based state forecaster is developed to accurately predict system states (voltage magnitudes and angles) in the near future. Based on the forecast system states, a dynamically weighted three-phase AC OPF problem is formulated to minimize the voltage violations with higher penalization on buses which are forecast to have higher voltage violations in the near future. By solving the proposed OPF problem, the controllable resources in the system are optimally coordinated to alleviate the potential severe voltage violations and improve the overall voltage profile. The proposed approach has been tested in a 12-bus distribution system and simulation results are presented to demonstrate the performance of the proposed approach.

Operational hydrological forecasting in Bavaria. Part II: Ensemble forecasting

Science.gov (United States)

Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

2009-04-01

In part I of this study, the operational flood forecasting system in Bavaria and an approach to identify and quantify forecast uncertainty was introduced. The approach is split into the calculation of an empirical 'overall error' from archived forecasts and the calculation of an empirical 'model error' based on hydrometeorological forecast tests, where rainfall observations were used instead of forecasts. The 'model error' can especially in upstream catchments where forecast uncertainty is strongly dependent on the current predictability of the atrmosphere be superimposed on the spread of a hydrometeorological ensemble forecast. In Bavaria, two meteorological ensemble prediction systems are currently tested for operational use: the 16-member COSMO-LEPS forecast and a poor man's ensemble composed of DWD GME, DWD Cosmo-EU, NCEP GFS, Aladin-Austria, MeteoSwiss Cosmo-7. The determination of the overall forecast uncertainty is dependent on the catchment characteristics: 1. Upstream catchment with high influence of weather forecast a) A hydrological ensemble forecast is calculated using each of the meteorological forecast members as forcing. b) Corresponding to the characteristics of the meteorological ensemble forecast, each resulting forecast hydrograph can be regarded as equally likely. c) The 'model error' distribution, with parameters dependent on hydrological case and lead time, is added to each forecast timestep of each ensemble member d) For each forecast timestep, the overall (i.e. over all 'model error' distribution of each ensemble member) error distribution is calculated e) From this distribution, the uncertainty range on a desired level (here: the 10% and 90% percentile) is extracted and drawn as forecast envelope. f) As the mean or median of an ensemble forecast does not necessarily exhibit meteorologically sound temporal evolution, a single hydrological forecast termed 'lead forecast' is chosen and shown in addition to the uncertainty bounds. This can be

The Geological Susceptibility of Induced Earthquakes in the Duvernay Play

Science.gov (United States)

Pawley, Steven; Schultz, Ryan; Playter, Tiffany; Corlett, Hilary; Shipman, Todd; Lyster, Steven; Hauck, Tyler

2018-02-01

Presently, consensus on the incorporation of induced earthquakes into seismic hazard has yet to be established. For example, the nonstationary, spatiotemporal nature of induced earthquakes is not well understood. Specific to the Western Canada Sedimentary Basin, geological bias in seismogenic activation potential has been suggested to control the spatial distribution of induced earthquakes regionally. In this paper, we train a machine learning algorithm to systemically evaluate tectonic, geomechanical, and hydrological proxies suspected to control induced seismicity. Feature importance suggests that proximity to basement, in situ stress, proximity to fossil reef margins, lithium concentration, and rate of natural seismicity are among the strongest model predictors. Our derived seismogenic potential map faithfully reproduces the current distribution of induced seismicity and is suggestive of other regions which may be prone to induced earthquakes. The refinement of induced seismicity geological susceptibility may become an important technique to identify significant underlying geological features and address induced seismic hazard forecasting issues.

GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting

Directory of Open Access Journals (Sweden)

Lintao Yang

2018-01-01

Full Text Available With the development of smart power grids, communication network technology and sensor technology, there has been an exponential growth in complex electricity load data. Irregular electricity load fluctuations caused by the weather and holiday factors disrupt the daily operation of the power companies. To deal with these challenges, this paper investigates a day-ahead electricity peak load interval forecasting problem. It transforms the conventional continuous forecasting problem into a novel interval forecasting problem, and then further converts the interval forecasting problem into the classification forecasting problem. In addition, an indicator system influencing the electricity load is established from three dimensions, namely the load series, calendar data, and weather data. A semi-supervised feature selection algorithm is proposed to address an electricity load classification forecasting issue based on the group method of data handling (GMDH technology. The proposed algorithm consists of three main stages: (1 training the basic classifier; (2 selectively marking the most suitable samples from the unclassified label data, and adding them to an initial training set; and (3 training the classification models on the final training set and classifying the test samples. An empirical analysis of electricity load dataset from four Chinese cities is conducted. Results show that the proposed model can address the electricity load classification forecasting problem more efficiently and effectively than the FW-Semi FS (forward semi-supervised feature selection and GMDH-U (GMDH-based semi-supervised feature selection for customer classification models.

Three-dimensional visualization of ensemble weather forecasts – Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

Directory of Open Access Journals (Sweden)

M. Rautenhaus

2015-07-01

Full Text Available We present the application of interactive three-dimensional (3-D visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 (THORPEX – North Atlantic Waveguide and Downstream Impact Experiment campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts (WCBs has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the European Centre for Medium Range Weather Forecasts (ECMWF ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and grid spacing of the forecast wind field. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (3 to 7 days before take-off.

A travel time forecasting model based on change-point detection method

Science.gov (United States)

LI, Shupeng; GUANG, Xiaoping; QIAN, Yongsheng; ZENG, Junwei

2017-06-01

Travel time parameters obtained from road traffic sensors data play an important role in traffic management practice. A travel time forecasting model is proposed for urban road traffic sensors data based on the method of change-point detection in this paper. The first-order differential operation is used for preprocessing over the actual loop data; a change-point detection algorithm is designed to classify the sequence of large number of travel time data items into several patterns; then a travel time forecasting model is established based on autoregressive integrated moving average (ARIMA) model. By computer simulation, different control parameters are chosen for adaptive change point search for travel time series, which is divided into several sections of similar state.Then linear weight function is used to fit travel time sequence and to forecast travel time. The results show that the model has high accuracy in travel time forecasting.

The SCEC/USGS dynamic earthquake rupture code verification exercise

Science.gov (United States)

Harris, R.A.; Barall, M.; Archuleta, R.; Dunham, E.; Aagaard, Brad T.; Ampuero, J.-P.; Bhat, H.; Cruz-Atienza, Victor M.; Dalguer, L.; Dawson, P.; Day, S.; Duan, B.; Ely, G.; Kaneko, Y.; Kase, Y.; Lapusta, N.; Liu, Yajing; Ma, S.; Oglesby, D.; Olsen, K.; Pitarka, A.; Song, S.; Templeton, E.

2009-01-01

Numerical simulations of earthquake rupture dynamics are now common, yet it has been difficult to test the validity of these simulations because there have been few field observations and no analytic solutions with which to compare the results. This paper describes the Southern California Earthquake Center/U.S. Geological Survey (SCEC/USGS) Dynamic Earthquake Rupture Code Verification Exercise, where codes that simulate spontaneous rupture dynamics in three dimensions are evaluated and the results produced by these codes are compared using Web-based tools. This is the first time that a broad and rigorous examination of numerous spontaneous rupture codes has been performed—a significant advance in this science. The automated process developed to attain this achievement provides for a future where testing of codes is easily accomplished.Scientists who use computer simulations to understand earthquakes utilize a range of techniques. Most of these assume that earthquakes are caused by slip at depth on faults in the Earth, but hereafter the strategies vary. Among the methods used in earthquake mechanics studies are kinematic approaches and dynamic approaches.The kinematic approach uses a computer code that prescribes the spatial and temporal evolution of slip on the causative fault (or faults). These types of simulations are very helpful, especially since they can be used in seismic data inversions to relate the ground motions recorded in the field to slip on the fault(s) at depth. However, these kinematic solutions generally provide no insight into the physics driving the fault slip or information about why the involved fault(s) slipped that much (or that little). In other words, these kinematic solutions may lack information about the physical dynamics of earthquake rupture that will be most helpful in forecasting future events.To help address this issue, some researchers use computer codes to numerically simulate earthquakes and construct dynamic, spontaneous

Irregular recurrence of large earthquakes along the san andreas fault: evidence from trees.

Science.gov (United States)

Jacoby, G C; Sheppard, P R; Sieh, K E

1988-07-08

Old trees growing along the San Andreas fault near Wrightwood, California, record in their annual ring-width patterns the effects of a major earthquake in the fall or winter of 1812 to 1813. Paleoseismic data and historical information indicate that this event was the "San Juan Capistrano" earthquake of 8 December 1812, with a magnitude of 7.5. The discovery that at least 12 kilometers of the Mojave segment of the San Andreas fault ruptured in 1812, only 44 years before the great January 1857 rupture, demonstrates that intervals between large earthquakes on this part of the fault are highly variable. This variability increases the uncertainty of forecasting destructive earthquakes on the basis of past behavior and accentuates the need for a more fundamental knowledge of San Andreas fault dynamics.

Wavelet-based verification of the quantitative precipitation forecast

Science.gov (United States)

Yano, Jun-Ichi; Jakubiak, Bogumil

2016-06-01

This paper explores the use of wavelets for spatial verification of quantitative precipitation forecasts (QPF), and especially the capacity of wavelets to provide both localization and scale information. Two 24-h forecast experiments using the two versions of the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) on 22 August 2010 over Poland are used to illustrate the method. Strong spatial localizations and associated intermittency of the precipitation field make verification of QPF difficult using standard statistical methods. The wavelet becomes an attractive alternative, because it is specifically designed to extract spatially localized features. The wavelet modes are characterized by the two indices for the scale and the localization. Thus, these indices can simply be employed for characterizing the performance of QPF in scale and localization without any further elaboration or tunable parameters. Furthermore, spatially-localized features can be extracted in wavelet space in a relatively straightforward manner with only a weak dependence on a threshold. Such a feature may be considered an advantage of the wavelet-based method over more conventional "object" oriented verification methods, as the latter tend to represent strong threshold sensitivities. The present paper also points out limits of the so-called "scale separation" methods based on wavelets. Our study demonstrates how these wavelet-based QPF verifications can be performed straightforwardly. Possibilities for further developments of the wavelet-based methods, especially towards a goal of identifying a weak physical process contributing to forecast error, are also pointed out.

Near real-time aftershock hazard maps for earthquakes

Science.gov (United States)

McCloskey, J.; Nalbant, S. S.

2009-04-01

Stress interaction modelling is routinely used to explain the spatial relationships between earthquakes and their aftershocks. On 28 October 2008 a M6.4 earthquake occurred near the Pakistan-Afghanistan border killing several hundred and causing widespread devastation. A second M6.4 event occurred 12 hours later 20km to the south east. By making some well supported assumptions concerning the source event and the geometry of any likely triggered event it was possible to map those areas most likely to experience further activity. Using Google earth, it would further have been possible to identify particular settlements in the source area which were particularly at risk and to publish their locations globally within about 3 hours of the first earthquake. Such actions could have significantly focused the initial emergency response management. We argue for routine prospective testing of such forecasts and dialogue between social and physical scientists and emergency response professionals around the practical application of these techniques.

Analytical investigations of the earthquake resistance of the support base of an oil-gas platform

Energy Technology Data Exchange (ETDEWEB)

Glagovskii, V. B.; Kassirova, N. A.; Turchina, O. A.; Finagenov, O. M.; Tsirukhin, N. A. [JSC ' VNIIG im. B. E. Vedeneeva' (Russian Federation)

2012-01-15

In designing stationary oil-gas recovery platforms on the continental shelf, the need arises to compute the estimated strength of their support base during seismic events. This paper is devoted to this estimation. The paper examines a structure consisting of the superstructure of an oil-gas platform and its gravity-type base. It is possible to install earthquake-insulating supports between them. Calculations performed for the design earthquake indicated that the design of the gravity base can resist a seismic effect without special additional measures. During the maximum design earthquake, moreover, significant stresses may develop in the zone of base where the columns are connected to the upper slab of the caisson. In that case, the earthquake insulation considered for the top of the platform becomes critical.

Analytical investigations of the earthquake resistance of the support base of an oil-gas platform

International Nuclear Information System (INIS)

Glagovskii, V. B.; Kassirova, N. A.; Turchina, O. A.; Finagenov, O. M.; Tsirukhin, N. A.

2012-01-01

In designing stationary oil-gas recovery platforms on the continental shelf, the need arises to compute the estimated strength of their support base during seismic events. This paper is devoted to this estimation. The paper examines a structure consisting of the superstructure of an oil-gas platform and its gravity-type base. It is possible to install earthquake-insulating supports between them. Calculations performed for the design earthquake indicated that the design of the gravity base can resist a seismic effect without special additional measures. During the maximum design earthquake, moreover, significant stresses may develop in the zone of base where the columns are connected to the upper slab of the caisson. In that case, the earthquake insulation considered for the top of the platform becomes critical.

Spatial Forecast of Landslides in Three Gorges Based On Spatial Data Mining

Directory of Open Access Journals (Sweden)

Xianmin Wang

2009-03-01

Full Text Available The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc. China-Brazil Earth Resources Satellite (Cbers images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods.

The Challenge of Forecasting Metropolitan Growth: Urban Characteristics Based Models versus Regional Dummy Based Models

OpenAIRE

NA

2005-01-01

This paper presents a study of errors in forecasting the population of Metropolitan Statistical Areas and the Primary MSAs of Consolidated Metropolitan Statistical Areas and New England MAs. The forecasts are for the year 2000 and are based on a semi-structural model estimated by Mills and Lubelle using 1970 to 1990 census data on population, employment and relative real wages. This model allows the testing of regional effects on population and employment growth. The year 2000 forecasts are f...

A Condition Based Maintenance Approach to Forecasting B-1 Aircraft Parts

Science.gov (United States)

2017-03-23

Air Force Institute of Technology AFIT Scholar Theses and Dissertations 3-23-2017 A Condition Based Maintenance Approach to Forecasting B-1 Aircraft...component’s life history where reliability forecasts could be stipulated based on a component’s current condition . One of the major issues their report noted...Engine Condition Monitoring System Specification. Contract Number DOT-CG-80513-A. Grand Prairie, TX. Air Force Materiel Command. (2011) Requirements For

Distributed HUC-based modeling with SUMMA for ensemble streamflow forecasting over large regional domains.

Science.gov (United States)

Saharia, M.; Wood, A.; Clark, M. P.; Bennett, A.; Nijssen, B.; Clark, E.; Newman, A. J.

2017-12-01

Most operational streamflow forecasting systems rely on a forecaster-in-the-loop approach in which some parts of the forecast workflow require an experienced human forecaster. But this approach faces challenges surrounding process reproducibility, hindcasting capability, and extension to large domains. The operational hydrologic community is increasingly moving towards `over-the-loop' (completely automated) large-domain simulations yet recent developments indicate a widespread lack of community knowledge about the strengths and weaknesses of such systems for forecasting. A realistic representation of land surface hydrologic processes is a critical element for improving forecasts, but often comes at the substantial cost of forecast system agility and efficiency. While popular grid-based models support the distributed representation of land surface processes, intermediate-scale Hydrologic Unit Code (HUC)-based modeling could provide a more efficient and process-aligned spatial discretization, reducing the need for tradeoffs between model complexity and critical forecasting requirements such as ensemble methods and comprehensive model calibration. The National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the USACE to implement, assess, and demonstrate real-time, over-the-loop distributed streamflow forecasting for several large western US river basins and regions. In this presentation, we present early results from short to medium range hydrologic and streamflow forecasts for the Pacific Northwest (PNW). We employ a real-time 1/16th degree daily ensemble model forcings as well as downscaled Global Ensemble Forecasting System (GEFS) meteorological forecasts. These datasets drive an intermediate-scale configuration of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model, which represents the PNW using over 11,700 HUCs. The system produces not only streamflow forecasts (using the Mizu

A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method.

Science.gov (United States)

Yang, Jun-He; Cheng, Ching-Hsue; Chan, Chia-Pan

2017-01-01

Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir's water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain

Directory of Open Access Journals (Sweden)

Francesca Gagliardi

2017-07-01

Full Text Available This paper proposes a short-term water demand forecasting method based on the use of the Markov chain. This method provides estimates of future demands by calculating probabilities that the future demand value will fall within pre-assigned intervals covering the expected total variability. More specifically, two models based on homogeneous and non-homogeneous Markov chains were developed and presented. These models, together with two benchmark models (based on artificial neural network and naïve methods, were applied to three real-life case studies for the purpose of forecasting the respective water demands from 1 to 24 h ahead. The results obtained show that the model based on a homogeneous Markov chain provides more accurate short-term forecasts than the one based on a non-homogeneous Markov chain, which is in line with the artificial neural network model. Both Markov chain models enable probabilistic information regarding the stochastic demand forecast to be easily obtained.

Does paleoseismology forecast the historic rates of large earthquakes on the San Andreas fault system?

Science.gov (United States)

Biasi, Glenn; Scharer, Katherine M.; Weldon, Ray; Dawson, Timothy E.

2016-01-01

The 98-year open interval since the most recent ground-rupturing earthquake in the greater San Andreas boundary fault system would not be predicted by the quasi-periodic recurrence statistics from paleoseismic data. We examine whether the current hiatus could be explained by uncertainties in earthquake dating. Using seven independent paleoseismic records, 100 year intervals may have occurred circa 1150, 1400, and 1700 AD, but they occur in a third or less of sample records drawn at random. A second method sampling from dates conditioned on the existence of a gap of varying length suggests century-long gaps occur 3-10% of the time. A combined record with more sites would lead to lower probabilities. Systematic data over-interpretation is considered an unlikely explanation. Instead some form of non-stationary behaviour seems required, perhaps through long-range fault interaction. Earthquake occurrence since 1000 AD is not inconsistent with long-term cyclicity suggested from long runs of earthquake simulators.

Ensemble-based Probabilistic Forecasting at Horns Rev

DEFF Research Database (Denmark)

Pinson, Pierre; Madsen, Henrik

2009-01-01

forecasting methodology. In a first stage, ensemble forecasts of meteorological variables are converted to power through a suitable power curve model. This modelemploys local polynomial regression, and is adoptively estimated with an orthogonal fitting method. The obtained ensemble forecasts of wind power...

Role of the Internet in Anticipating and Mitigating Earthquake Catastrophes, and the Emergence of Personal Risk Management (Invited)

Science.gov (United States)

Rundle, J. B.; Holliday, J. R.; Donnellan, A.; Graves, W.; Tiampo, K. F.; Klein, W.

2009-12-01

Risks from natural and financial catastrophes are currently managed by a combination of large public and private institutions. Public institutions usually are comprised of government agencies that conduct studies, formulate policies and guidelines, enforce regulations, and make “official” forecasts. Private institutions include insurance and reinsurance companies, and financial service companies that underwrite catastrophe (“cat”) bonds, and make private forecasts. Although decisions about allocating resources and developing solutions are made by large institutions, the costs of dealing with catastrophes generally fall for the most part on businesses and the general public. Information on potential risks is generally available to the public for some hazards but not others. For example, in the case of weather, private forecast services are provided by www.weather.com and www.wunderground.com. For earthquakes in California (only), the official forecast is the WGCEP-USGS forecast, but provided in a format that is difficult for the public to use. Other privately made forecasts are currently available, for example by the JPL QuakeSim and Russian groups, but these efforts are limited. As more of the world’s population moves increasingly into major seismic zones, new strategies are needed to allow individuals to manage their personal risk from large and damaging earthquakes. Examples include individual mitigation measures such as retrofitting, as well as microinsurance in both developing and developed countries, as well as other financial strategies. We argue that the “long tail” of the internet offers an ideal, and greatly underutilized mechanism to reach out to consumers and to provide them with the information and tools they need to confront and manage seismic hazard and risk on an individual, personalized basis. Information of this type includes not only global hazard forecasts, which are now possible, but also global risk estimation. Additionally

Development of a remote sensing-based rice yield forecasting model

Energy Technology Data Exchange (ETDEWEB)

Mosleh, M.K.; Hassan, Q.K.; Chowdhury, E.H.

2016-11-01

This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDVI) images at 250 m spatial resolution acquired during the initial (January 1 to January 16) and peak greenness (March 23/24 to April 6/7 depending on leap year) stages in conjunction with secondary datasets (i.e., boro suitability map, and ground-based information) during 2007-2012 period. The method consisted of two components: (i) developing a model for delineating area under rice cultivation before harvesting; and (ii) forecasting rice yield as a function of NDVI. Our results demonstrated strong agreements between the model (i.e., MODIS-based) and ground-based area estimates during 2010-2012 period, i.e., coefficient of determination (R2); root mean square error (RMSE); and relative error (RE) in between 0.93 to 0.95; 30,519 to 37,451 ha; and ±10% respectively at the 23 district-levels. We also found good agreements between forecasted (i.e., MODIS-based) and ground-based yields during 2010-2012 period (R2 between 0.76 and 0.86; RMSE between 0.21 and 0.29 Mton/ha, and RE between -5.45% and 6.65%) at the 23 district-levels. We believe that our developments of forecasting the boro rice yield would be useful for the decision makers in addressing food security in Bangladesh. (Author)

Impact-based earthquake alerts with the U.S. Geological Survey's PAGER system: what's next?

Science.gov (United States)

Wald, D.J.; Jaiswal, K.S.; Marano, K.D.; Garcia, D.; So, E.; Hearne, M.

2012-01-01

In September 2010, the USGS began publicly releasing earthquake alerts for significant earthquakes around the globe based on estimates of potential casualties and economic losses with its Prompt Assessment of Global Earthquakes for Response (PAGER) system. These estimates significantly enhanced the utility of the USGS PAGER system which had been, since 2006, providing estimated population exposures to specific shaking intensities. Quantifying earthquake impacts and communicating estimated losses (and their uncertainties) to the public, the media, humanitarian, and response communities required a new protocol—necessitating the development of an Earthquake Impact Scale—described herein and now deployed with the PAGER system. After two years of PAGER-based impact alerting, we now review operations, hazard calculations, loss models, alerting protocols, and our success rate for recent (2010-2011) events. This review prompts analyses of the strengths, limitations, opportunities, and pressures, allowing clearer definition of future research and development priorities for the PAGER system.

Evaluation of stability of foundation ground during earthquake, (1)

International Nuclear Information System (INIS)

Nishi, Koichi; Kanatani, Mamoru; Matsui, Ietaka; Touma, Jun-ichi

1986-01-01

The Central Research Institute of Electric Power Industry has advanced the research on the new location technology for nuclear power stations in order to cope with the lack of sites expected in near future. The sites on rock mass are usually used for nuclear power stations, but the location on quaternary ground, particularly on gravel ground, is one of such new technology, to which attention has been paid. It has been pointed out that this location has the advantage in the earthquake response of reactor buildings and equipment, and the prospect to ensure the stability by lowering underground water level and pile penetration has been obtained, therefore, the possibility of its realization is high. At present, the research aiming at the establishment of the method of evaluating foundation ground stability is in progress, and it is expected that the quantitative evaluation means for the aseismatic stability of quaternary ground is obtained. In this paper, the analysis techniques for forecasting the amount of subsidence at the time of earthquakes occurring accompanying the generation of excessive pore water pressure and its disappearance are reported. Also the applicability of the forecasting techniques was experimentally verified. (Kako, I.)

Robust forecast comparison

OpenAIRE

Jin, Sainan; Corradi, Valentina; Swanson, Norman

2015-01-01

Forecast accuracy is typically measured in terms of a given loss function. However, as a consequence of the use of misspecified models in multiple model comparisons, relative forecast rankings are loss function dependent. This paper addresses this issue by using a novel criterion for forecast evaluation which is based on the entire distribution of forecast errors. We introduce the concepts of general-loss (GL) forecast superiority and convex-loss (CL) forecast superiority, and we establish a ...

New watershed-based climate forecast products for hydrologists and water managers

Science.gov (United States)

Baker, S. A.; Wood, A.; Rajagopalan, B.; Lehner, F.; Peng, P.; Ray, A. J.; Barsugli, J. J.; Werner, K.

2017-12-01

Operational sub-seasonal to seasonal (S2S) climate predictions have advanced in skill in recent years but are yet to be broadly utilized by stakeholders in the water management sector. While some of the challenges that relate to fundamental predictability are difficult or impossible to surmount, other hurdles related to forecast product formulation, translation, relevance, and accessibility can be directly addressed. These include products being misaligned with users' space-time needs, products disseminated in formats users cannot easily process, and products based on raw model outputs that are biased relative to user climatologies. In each of these areas, more can be done to bridge the gap by enhancing the usability, quality, and relevance of water-oriented predictions. In addition, water stakeholder impacts can benefit from short-range extremes predictions (such as 2-3 day storms or 1-week heat waves) at S2S time-scales, for which few products exist. We present interim results of a Research to Operations (R2O) effort sponsored by the NOAA MAPP Climate Testbed to (1) formulate climate prediction products so as to reduce hurdles to in water stakeholder adoption, and to (2) explore opportunities for extremes prediction at S2S time scales. The project is currently using CFSv2 and National Multi-­Model Ensemble (NMME) reforecasts and forecasts to develop real-time watershed-based climate forecast products, and to train post-processing approaches to enhance the skill and reliability of raw real-time S2S forecasts. Prototype S2S climate data products (forecasts and associated skill analyses) are now being operationally staged at NCAR on a public website to facilitate further product development through interactions with water managers. Initial demonstration products include CFSv2-based bi-weekly climate forecasts (weeks 1-2, 2-3, and 3-4) for sub-regional scale hydrologic units, and NMME-based monthly and seasonal prediction products. Raw model mean skill at these time

Research on forecast technology of mine gas emission based on fuzzy data mining (FDM)

Energy Technology Data Exchange (ETDEWEB)

Xu Chang-kai; Wang Yao-cai; Wang Jun-wei [CUMT, Xuzhou (China). School of Information and Electrical Engineering

2004-07-01

The safe production of coalmine can be further improved by forecasting the quantity of gas emission based on the real-time data and historical data which the gas monitoring system has saved. By making use of the advantages of data warehouse and data mining technology for processing large quantity of redundancy data, the method and its application of forecasting mine gas emission quantity based on FDM were studied. The constructing fuzzy resembling relation and clustering analysis were proposed, which the potential relationship inside the gas emission data may be found. The mode finds model and forecast model were presented, and the detailed approach to realize this forecast was also proposed, which have been applied to forecast the gas emission quantity efficiently.

Short-Term Wind Power Interval Forecasting Based on an EEMD-RT-RVM Model

Directory of Open Access Journals (Sweden)

Haixiang Zang

2016-01-01

Full Text Available Accurate short-term wind power forecasting is important for improving the security and economic success of power grids. Existing wind power forecasting methods are mostly types of deterministic point forecasting. Deterministic point forecasting is vulnerable to forecasting errors and cannot effectively deal with the random nature of wind power. In order to solve the above problems, we propose a short-term wind power interval forecasting model based on ensemble empirical mode decomposition (EEMD, runs test (RT, and relevance vector machine (RVM. First, in order to reduce the complexity of data, the original wind power sequence is decomposed into a plurality of intrinsic mode function (IMF components and residual (RES component by using EEMD. Next, we use the RT method to reconstruct the components and obtain three new components characterized by the fine-to-coarse order. Finally, we obtain the overall forecasting results (with preestablished confidence levels by superimposing the forecasting results of each new component. Our results show that, compared with existing methods, our proposed short-term interval forecasting method has less forecasting errors, narrower interval widths, and larger interval coverage percentages. Ultimately, our forecasting model is more suitable for engineering applications and other forecasting methods for new energy.

A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

Directory of Open Access Journals (Sweden)

Jun-He Yang

2017-01-01

Full Text Available Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir’s water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir’s water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

Information Trading by Corporate Insiders Based on Accounting Accruals - Forecasting Economic Performance

NARCIS (Netherlands)

Hodgson, A.; van Praag, B.

2006-01-01

In this paper, we test whether directors’ (corporate insiders) trading in Australia, based on accounting accruals, provides incremental information in forecasting a firm's economic performance. We determine that directors’ trading on negative accruals in larger firms has greater forecasting content

Physics-Based Hazard Assessment for Critical Structures Near Large Earthquake Sources

Science.gov (United States)

Hutchings, L.; Mert, A.; Fahjan, Y.; Novikova, T.; Golara, A.; Miah, M.; Fergany, E.; Foxall, W.

2017-09-01

We argue that for critical structures near large earthquake sources: (1) the ergodic assumption, recent history, and simplified descriptions of the hazard are not appropriate to rely on for earthquake ground motion prediction and can lead to a mis-estimation of the hazard and risk to structures; (2) a physics-based approach can address these issues; (3) a physics-based source model must be provided to generate realistic phasing effects from finite rupture and model near-source ground motion correctly; (4) wave propagations and site response should be site specific; (5) a much wider search of possible sources of ground motion can be achieved computationally with a physics-based approach; (6) unless one utilizes a physics-based approach, the hazard and risk to structures has unknown uncertainties; (7) uncertainties can be reduced with a physics-based approach, but not with an ergodic approach; (8) computational power and computer codes have advanced to the point that risk to structures can be calculated directly from source and site-specific ground motions. Spanning the variability of potential ground motion in a predictive situation is especially difficult for near-source areas, but that is the distance at which the hazard is the greatest. The basis of a "physical-based" approach is ground-motion syntheses derived from physics and an understanding of the earthquake process. This is an overview paper and results from previous studies are used to make the case for these conclusions. Our premise is that 50 years of strong motion records is insufficient to capture all possible ranges of site and propagation path conditions, rupture processes, and spatial geometric relationships between source and site. Predicting future earthquake scenarios is necessary; models that have little or no physical basis but have been tested and adjusted to fit available observations can only "predict" what happened in the past, which should be considered description as opposed to prediction

Winter wheat quality monitoring and forecasting system based on remote sensing and environmental factors

International Nuclear Information System (INIS)

Haiyang, Yu; Yanmei, Liu; Guijun, Yang; Xiaodong, Yang; Chenwei, Nie; Dong, Ren

2014-01-01

To achieve dynamic winter wheat quality monitoring and forecasting in larger scale regions, the objective of this study was to design and develop a winter wheat quality monitoring and forecasting system by using a remote sensing index and environmental factors. The winter wheat quality trend was forecasted before the harvest and quality was monitored after the harvest, respectively. The traditional quality-vegetation index from remote sensing monitoring and forecasting models were improved. Combining with latitude information, the vegetation index was used to estimate agronomy parameters which were related with winter wheat quality in the early stages for forecasting the quality trend. A combination of rainfall in May, temperature in May, illumination at later May, the soil available nitrogen content and other environmental factors established the quality monitoring model. Compared with a simple quality-vegetation index, the remote sensing monitoring and forecasting model used in this system get greatly improved accuracy. Winter wheat quality was monitored and forecasted based on the above models, and this system was completed based on WebGIS technology. Finally, in 2010 the operation process of winter wheat quality monitoring system was presented in Beijing, the monitoring and forecasting results was outputted as thematic maps

Multitask Learning-Based Security Event Forecast Methods for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Hui He

2016-01-01

Full Text Available Wireless sensor networks have strong dynamics and uncertainty, including network topological changes, node disappearance or addition, and facing various threats. First, to strengthen the detection adaptability of wireless sensor networks to various security attacks, a region similarity multitask-based security event forecast method for wireless sensor networks is proposed. This method performs topology partitioning on a large-scale sensor network and calculates the similarity degree among regional subnetworks. The trend of unknown network security events can be predicted through multitask learning of the occurrence and transmission characteristics of known network security events. Second, in case of lacking regional data, the quantitative trend of unknown regional network security events can be calculated. This study introduces a sensor network security event forecast method named Prediction Network Security Incomplete Unmarked Data (PNSIUD method to forecast missing attack data in the target region according to the known partial data in similar regions. Experimental results indicate that for an unknown security event forecast the forecast accuracy and effects of the similarity forecast algorithm are better than those of single-task learning method. At the same time, the forecast accuracy of the PNSIUD method is better than that of the traditional support vector machine method.

Extraction Method for Earthquake-Collapsed Building Information Based on High-Resolution Remote Sensing

International Nuclear Information System (INIS)

Chen, Peng; Wu, Jian; Liu, Yaolin; Wang, Jing

2014-01-01

At present, the extraction of earthquake disaster information from remote sensing data relies on visual interpretation. However, this technique cannot effectively and quickly obtain precise and efficient information for earthquake relief and emergency management. Collapsed buildings in the town of Zipingpu after the Wenchuan earthquake were used as a case study to validate two kinds of rapid extraction methods for earthquake-collapsed building information based on pixel-oriented and object-oriented theories. The pixel-oriented method is based on multi-layer regional segments that embody the core layers and segments of the object-oriented method. The key idea is to mask layer by layer all image information, including that on the collapsed buildings. Compared with traditional techniques, the pixel-oriented method is innovative because it allows considerably rapid computer processing. As for the object-oriented method, a multi-scale segment algorithm was applied to build a three-layer hierarchy. By analyzing the spectrum, texture, shape, location, and context of individual object classes in different layers, the fuzzy determined rule system was established for the extraction of earthquake-collapsed building information. We compared the two sets of results using three variables: precision assessment, visual effect, and principle. Both methods can extract earthquake-collapsed building information quickly and accurately. The object-oriented method successfully overcomes the pepper salt noise caused by the spectral diversity of high-resolution remote sensing data and solves the problem of same object, different spectrums and that of same spectrum, different objects. With an overall accuracy of 90.38%, the method achieves more scientific and accurate results compared with the pixel-oriented method (76.84%). The object-oriented image analysis method can be extensively applied in the extraction of earthquake disaster information based on high-resolution remote sensing

Using data-driven agent-based models for forecasting emerging infectious diseases

Directory of Open Access Journals (Sweden)

Srinivasan Venkatramanan

2018-03-01

Full Text Available Producing timely, well-informed and reliable forecasts for an ongoing epidemic of an emerging infectious disease is a huge challenge. Epidemiologists and policy makers have to deal with poor data quality, limited understanding of the disease dynamics, rapidly changing social environment and the uncertainty on effects of various interventions in place. Under this setting, detailed computational models provide a comprehensive framework for integrating diverse data sources into a well-defined model of disease dynamics and social behavior, potentially leading to better understanding and actions. In this paper, we describe one such agent-based model framework developed for forecasting the 2014–2015 Ebola epidemic in Liberia, and subsequently used during the Ebola forecasting challenge. We describe the various components of the model, the calibration process and summarize the forecast performance across scenarios of the challenge. We conclude by highlighting how such a data-driven approach can be refined and adapted for future epidemics, and share the lessons learned over the course of the challenge. Keywords: Emerging infectious diseases, Agent-based models, Simulation optimization, Bayesian calibration, Ebola

Response of base-isolated nuclear structures to extreme earthquake shaking

Energy Technology Data Exchange (ETDEWEB)

Kumar, Manish, E-mail: mkumar2@buffalo.edu; Whittaker, Andrew S.; Constantinou, Michael C.

2015-12-15

Highlights: • Response-history analysis of nuclear structures base-isolated using lead–rubber bearings is performed. • Advanced numerical model of lead–rubber bearing is used to capture behavior under extreme earthquake shaking. • Results of response-history analysis obtained using simplified and advanced model of lead–rubber bearings are compared. • Heating of the lead core and variation in buckling load and axial stiffness affect the response. - Abstract: Seismic isolation using low damping rubber and lead–rubber bearings is a viable strategy for mitigating the effects of extreme earthquake shaking on safety-related nuclear structures. The mechanical properties of these bearings are not expected to change substantially in design basis shaking. However, under shaking more intense than design basis, the properties of the lead cores in lead–rubber bearings may degrade due to heating associated with energy dissipation, some bearings in an isolation system may experience net tension, and the compression and tension stiffness may be affected by the lateral displacement of the isolation system. The effects of intra-earthquake changes in mechanical properties on the response of base-isolated nuclear power plants (NPPs) are investigated using an advanced numerical model of a lead–rubber bearing that has been verified and validated, and implemented in OpenSees. A macro-model is used for response-history analysis of base-isolated NPPs. Ground motions are selected and scaled to be consistent with response spectra for design basis and beyond design basis earthquake shaking at the site of the Diablo Canyon Nuclear Generating Station. Ten isolation systems of two periods and five characteristic strengths are analyzed. The responses obtained using simplified and advanced isolator models are compared. Strength degradation due to heating of lead cores and changes in buckling load most significantly affect the response of the base-isolated NPP.

Response of base-isolated nuclear structures to extreme earthquake shaking

International Nuclear Information System (INIS)

Kumar, Manish; Whittaker, Andrew S.; Constantinou, Michael C.

2015-01-01

Highlights: • Response-history analysis of nuclear structures base-isolated using lead–rubber bearings is performed. • Advanced numerical model of lead–rubber bearing is used to capture behavior under extreme earthquake shaking. • Results of response-history analysis obtained using simplified and advanced model of lead–rubber bearings are compared. • Heating of the lead core and variation in buckling load and axial stiffness affect the response. - Abstract: Seismic isolation using low damping rubber and lead–rubber bearings is a viable strategy for mitigating the effects of extreme earthquake shaking on safety-related nuclear structures. The mechanical properties of these bearings are not expected to change substantially in design basis shaking. However, under shaking more intense than design basis, the properties of the lead cores in lead–rubber bearings may degrade due to heating associated with energy dissipation, some bearings in an isolation system may experience net tension, and the compression and tension stiffness may be affected by the lateral displacement of the isolation system. The effects of intra-earthquake changes in mechanical properties on the response of base-isolated nuclear power plants (NPPs) are investigated using an advanced numerical model of a lead–rubber bearing that has been verified and validated, and implemented in OpenSees. A macro-model is used for response-history analysis of base-isolated NPPs. Ground motions are selected and scaled to be consistent with response spectra for design basis and beyond design basis earthquake shaking at the site of the Diablo Canyon Nuclear Generating Station. Ten isolation systems of two periods and five characteristic strengths are analyzed. The responses obtained using simplified and advanced isolator models are compared. Strength degradation due to heating of lead cores and changes in buckling load most significantly affect the response of the base-isolated NPP.

Fuzzy time-series based on Fibonacci sequence for stock price forecasting

Science.gov (United States)

Chen, Tai-Liang; Cheng, Ching-Hsue; Jong Teoh, Hia

2007-07-01

Time-series models have been utilized to make reasonably accurate predictions in the areas of stock price movements, academic enrollments, weather, etc. For promoting the forecasting performance of fuzzy time-series models, this paper proposes a new model, which incorporates the concept of the Fibonacci sequence, the framework of Song and Chissom's model and the weighted method of Yu's model. This paper employs a 5-year period TSMC (Taiwan Semiconductor Manufacturing Company) stock price data and a 13-year period of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) stock index data as experimental datasets. By comparing our forecasting performances with Chen's (Forecasting enrollments based on fuzzy time-series. Fuzzy Sets Syst. 81 (1996) 311-319), Yu's (Weighted fuzzy time-series models for TAIEX forecasting. Physica A 349 (2004) 609-624) and Huarng's (The application of neural networks to forecast fuzzy time series. Physica A 336 (2006) 481-491) models, we conclude that the proposed model surpasses in accuracy these conventional fuzzy time-series models.

Forecast-based Interventions Can Reduce the Health and Economic Burden of Wildfires

Science.gov (United States)

We simulated public health forecast-based interventions during a wildfire smoke episode in rural North Carolina to show the potential for use of modeled smoke forecasts toward reducing the health burden and showed a significant economic benefit of reducing exposures. Daily and co...

Machine learning based switching model for electricity load forecasting

Energy Technology Data Exchange (ETDEWEB)

Fan, Shu; Lee, Wei-Jen [Energy Systems Research Center, The University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States); Chen, Luonan [Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-0013 (Japan)

2008-06-15

In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma. (author)

Machine learning based switching model for electricity load forecasting

Energy Technology Data Exchange (ETDEWEB)

Fan Shu [Energy Systems Research Center, University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States); Chen Luonan [Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-0013 (Japan); Lee, Weijen [Energy Systems Research Center, University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States)], E-mail: wlee@uta.edu

2008-06-15

In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma.

Machine learning based switching model for electricity load forecasting

International Nuclear Information System (INIS)

Fan Shu; Chen Luonan; Lee, Weijen

2008-01-01

In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma

Artificial intelligence based models for stream-flow forecasting: 2000-2015

Science.gov (United States)

Yaseen, Zaher Mundher; El-shafie, Ahmed; Jaafar, Othman; Afan, Haitham Abdulmohsin; Sayl, Khamis Naba

2015-11-01

The use of Artificial Intelligence (AI) has increased since the middle of the 20th century as seen in its application in a wide range of engineering and science problems. The last two decades, for example, has seen a dramatic increase in the development and application of various types of AI approaches for stream-flow forecasting. Generally speaking, AI has exhibited significant progress in forecasting and modeling non-linear hydrological applications and in capturing the noise complexity in the dataset. This paper explores the state-of-the-art application of AI in stream-flow forecasting, focusing on defining the data-driven of AI, the advantages of complementary models, as well as the literature and their possible future application in modeling and forecasting stream-flow. The review also identifies the major challenges and opportunities for prospective research, including, a new scheme for modeling the inflow, a novel method for preprocessing time series frequency based on Fast Orthogonal Search (FOS) techniques, and Swarm Intelligence (SI) as an optimization approach.

Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms

Science.gov (United States)

Huang, Xin; Wang, Huaning; Xu, Long; Liu, Jinfu; Li, Rong; Dai, Xinghua

2018-03-01

Solar flares originate from the release of the energy stored in the magnetic field of solar active regions, the triggering mechanism for these flares, however, remains unknown. For this reason, the conventional solar flare forecast is essentially based on the statistic relationship between solar flares and measures extracted from observational data. In the current work, the deep learning method is applied to set up the solar flare forecasting model, in which forecasting patterns can be learned from line-of-sight magnetograms of solar active regions. In order to obtain a large amount of observational data to train the forecasting model and test its performance, a data set is created from line-of-sight magnetogarms of active regions observed by SOHO/MDI and SDO/HMI from 1996 April to 2015 October and corresponding soft X-ray solar flares observed by GOES. The testing results of the forecasting model indicate that (1) the forecasting patterns can be automatically reached with the MDI data and they can also be applied to the HMI data; furthermore, these forecasting patterns are robust to the noise in the observational data; (2) the performance of the deep learning forecasting model is not sensitive to the given forecasting periods (6, 12, 24, or 48 hr); (3) the performance of the proposed forecasting model is comparable to that of the state-of-the-art flare forecasting models, even if the duration of the total magnetograms continuously spans 19.5 years. Case analyses demonstrate that the deep learning based solar flare forecasting model pays attention to areas with the magnetic polarity-inversion line or the strong magnetic field in magnetograms of active regions.

Earthquake Damage Assessment Using Objective Image Segmentation: A Case Study of 2010 Haiti Earthquake

Science.gov (United States)

Oommen, Thomas; Rebbapragada, Umaa; Cerminaro, Daniel

2012-01-01

In this study, we perform a case study on imagery from the Haiti earthquake that evaluates a novel object-based approach for characterizing earthquake induced surface effects of liquefaction against a traditional pixel based change technique. Our technique, which combines object-oriented change detection with discriminant/categorical functions, shows the power of distinguishing earthquake-induced surface effects from changes in buildings using the object properties concavity, convexity, orthogonality and rectangularity. Our results suggest that object-based analysis holds promise in automatically extracting earthquake-induced damages from high-resolution aerial/satellite imagery.

Earthquake countermeasure work on water intake structure based on post-construction shear reinforcement method

International Nuclear Information System (INIS)

Satou, Yoshihito; Wani, Masaaki; Wachi, Takamitsu

2017-01-01

Hamaoka Nuclear Power Station set up 'earthquake motion as the base for remodeling work' by referring to the strong earthquake fault model assumed by the 'Study meeting for Nankai Trough's mega thrust earthquake model' of the Cabinet Office. Based on this earthquake, it implemented seismic countermeasure work using ceramic fixing type post-construction shear reinforcement bars by targeting the Unit 4 water intake tank screen room. This construction work was carried out in a short period of about nine months due to a restriction in the drainage period of the water intake tank. Thanks to the improvement of process control, such as adoption of a two-shift (day and night) system, this work was completed. On the other hand, the quality of construction was secured by adopting a drilling machine with a resistance sensor at the time of drilling and plastic grout at the time of grout filling, as well as through quality inspection based on Construction Technology Review and Certification. (A.O.)

Investigation of Pre-Earthquake Ionospheric Disturbances by 3D Tomographic Analysis

Science.gov (United States)

Yagmur, M.

2016-12-01

Ionospheric variations before earthquakes have been widely discussed phenomena in ionospheric studies. To clarify the source and mechanism of these phenomena is highly important for earthquake forecasting. To well understanding the mechanical and physical processes of pre-seismic Ionospheric anomalies that might be related even with Lithosphere-Atmosphere-Ionosphere-Magnetosphere Coupling, both statistical and 3D modeling analysis are needed. For these purpose, firstly we have investigated the relation between Ionospheric TEC Anomalies and potential source mechanisms such as space weather activity and lithospheric phenomena like positive surface electric charges. To distinguish their effects on Ionospheric TEC, we have focused on pre-seismically active days. Then, we analyzed the statistical data of 54 earthquakes that M≽6 between 2000 and 2013 as well as the 2011 Tohoku and the 2016 Kumamoto Earthquakes in Japan. By comparing TEC anomaly and Solar activity by Dst Index, we have found that 28 events that might be related with Earthquake activity. Following the statistical analysis, we also investigate the Lithospheric effect on TEC change on selected days. Among those days, we have chosen two case studies as the 2011 Tohoku and the 2016 Kumamoto Earthquakes to make 3D reconstructed images by utilizing 3D Tomography technique with Neural Networks. The results will be presented in our presentation. Keywords : Earthquake, 3D Ionospheric Tomography, Positive and Negative Anomaly, Geomagnetic Storm, Lithosphere

Forecasting of resonances vibration equipment with elastic waves coolant and with the external periodic loads on NPP with WWER

International Nuclear Information System (INIS)

Proskuryakov, K.N.; Zaporozhets, M.V.; Fedorov, A.I.

2015-01-01

Forecasting are carried out for external loads in relation to the main circulation circuit - dynamic loads caused by the rotation of the MCP, dynamic loads caused by the earthquake, dynamic loads caused by damage to the MCP in the earthquake. A comparison of the response spectrum of one of the variants of the base of the NPP, with the frequency vibration of the primary circuit equipment for NPP with WWER-1000 and self-frequency of elastic waves in the fluid. Analysis of the comparison results shows that the frequency of vibration of the main equipment of the reactor plant and elastic waves are in the frequency band in the spectrum response corresponding to the maximum amplitude of the seismic action [ru

Remote-sensing based approach to forecast habitat quality under climate change scenarios.

Directory of Open Access Journals (Sweden)

Juan M Requena-Mullor

Full Text Available As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071-2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the

Short-Term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine

Energy Technology Data Exchange (ETDEWEB)

Jiang, Huaiguang; Zhang, Yingchen

2016-11-14

This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vector regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.

What Can We Learn from a Simple Physics-Based Earthquake Simulator?

Science.gov (United States)

Artale Harris, Pietro; Marzocchi, Warner; Melini, Daniele

2018-03-01

Physics-based earthquake simulators are becoming a popular tool to investigate on the earthquake occurrence process. So far, the development of earthquake simulators is commonly led by the approach "the more physics, the better". However, this approach may hamper the comprehension of the outcomes of the simulator; in fact, within complex models, it may be difficult to understand which physical parameters are the most relevant to the features of the seismic catalog at which we are interested. For this reason, here, we take an opposite approach and analyze the behavior of a purposely simple earthquake simulator applied to a set of California faults. The idea is that a simple simulator may be more informative than a complex one for some specific scientific objectives, because it is more understandable. Our earthquake simulator has three main components: the first one is a realistic tectonic setting, i.e., a fault data set of California; the second is the application of quantitative laws for earthquake generation on each single fault, and the last is the fault interaction modeling through the Coulomb Failure Function. The analysis of this simple simulator shows that: (1) the short-term clustering can be reproduced by a set of faults with an almost periodic behavior, which interact according to a Coulomb failure function model; (2) a long-term behavior showing supercycles of the seismic activity exists only in a markedly deterministic framework, and quickly disappears introducing a small degree of stochasticity on the recurrence of earthquakes on a fault; (3) faults that are strongly coupled in terms of Coulomb failure function model are synchronized in time only in a marked deterministic framework, and as before, such a synchronization disappears introducing a small degree of stochasticity on the recurrence of earthquakes on a fault. Overall, the results show that even in a simple and perfectly known earthquake occurrence world, introducing a small degree of

A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

OpenAIRE

Jun-He Yang; Ching-Hsue Cheng; Chia-Pan Chan

2017-01-01

Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting m...

The bayesian probabilistic prediction of the next earthquake in the ometepec segment of the mexican subduction zone

Science.gov (United States)

Ferraes, Sergio G.

1992-06-01

A predictive equation to estimate the next interoccurrence time (τ) for the next earthquake ( M≥6) in the Ometepec segment is presented, based on Bayes' theorem and the Gaussian process. Bayes' theorem is used to relate the Gaussian process to both a log-normal distribution of recurrence times (τ) and a log-normal distribution of magnitudes ( M) ( Nishenko and Buland, 1987; Lomnitz, 1964). We constructed two new random variables X=In M and Y=In τ with normal marginal densities, and based on the Gaussian process model we assume that their joint density is normal. Using this information, we determine the Bayesian conditional probability. Finally, a predictive equation is derived, based on the criterion of maximization of the Bayesian conditional probability. The model forecasts the next interoccurrence time, conditional on the magnitude of the last event. Realistic estimates of future damaging earthquakes are based on relocated historical earthquakes. However, at the present time there is a controversy between Nishenko-Singh and Gonzalez-Ruiz-Mc-Nally concerning the rupturing process of the 1907 earthquake. We use our Bayesian analysis to examine and discuss this very important controversy. To clarify to the full significance of the analysis, we put forward the results using two catalogues: (1) The Ometepec catalogue without the 1907 earthquake (González-Ruíz-McNally), and (2) the Ometepec catalogue including the 1907 earthquake (Nishenko-Singh). The comparison of the prediction error reveals that in the Nishenko-Singh catalogue, the errors are considerably smaller than the average error for the González-Ruíz-McNally catalogue of relocated events. Finally, using the Nishenko-Singh catalogue which locates the 1907 event inside the Ometepec segment, we conclude that the next expected damaging earthquake ( M≥6.0) will occur approximately within the next time interval τ=11.82 years from the last event (which occurred on July 2, 1984), or equivalently will

Reflections from the interface between seismological research and earthquake risk reduction

Science.gov (United States)

Sargeant, S.

2012-04-01

Scientific understanding of earthquakes and their attendant hazards is vital for the development of effective earthquake risk reduction strategies. Within the global disaster reduction policy framework (the Hyogo Framework for Action, overseen by the UN International Strategy for Disaster Reduction), the anticipated role of science and scientists is clear, with respect to risk assessment, loss estimation, space-based observation, early warning and forecasting. The importance of information sharing and cooperation, cross-disciplinary networks and developing technical and institutional capacity for effective disaster management is also highlighted. In practice, the degree to which seismological information is successfully delivered to and applied by individuals, groups or organisations working to manage or reduce the risk from earthquakes is variable. The challenge for scientists is to provide fit-for-purpose information that can be integrated simply into decision-making and risk reduction activities at all levels of governance and at different geographic scales, often by a non-technical audience (i.e. people without any seismological/earthquake engineering training). The interface between seismological research and earthquake risk reduction (defined here in terms of both the relationship between the science and its application, and the scientist and other risk stakeholders) is complex. This complexity is a function of a range issues that arise relating to communication, multidisciplinary working, politics, organisational practices, inter-organisational collaboration, working practices, sectoral cultures, individual and organisational values, worldviews and expectations. These factors can present significant obstacles to scientific information being incorporated into the decision-making process. The purpose of this paper is to present some personal reflections on the nature of the interface between the worlds of seismological research and risk reduction, and the

Reliable selection of earthquake ground motions for performance-based design

DEFF Research Database (Denmark)

Katsanos, Evangelos; Sextos, A.G.

2016-01-01

A decision support process is presented to accommodate selecting and scaling of earthquake motions as required for the time domain analysis of structures. Prequalified code-compatible suites of seismic motions are provided through a multi-criterion approach to satisfy prescribed reduced variability...... of the method, by being subjected to numerous suites of motions that were highly ranked according to both the proposed approach (δsv-sc) and the conventional index (δconv), already used by most existing code-based earthquake records selection and scaling procedures. The findings reveal the superiority...

Model-Aided Altimeter-Based Water Level Forecasting System in Mekong River

Science.gov (United States)

Chang, C. H.; Lee, H.; Hossain, F.; Okeowo, M. A.; Basnayake, S. B.; Jayasinghe, S.; Saah, D. S.; Anderson, E.; Hwang, E.

2017-12-01

Mekong River, one of the massive river systems in the world, has drainage area of about 795,000 km2 covering six countries. People living in its drainage area highly rely on resources given by the river in terms of agriculture, fishery, and hydropower. Monitoring and forecasting the water level in a timely manner, is urgently needed over the Mekong River. Recently, using TOPEX/Poseidon (T/P) altimetry water level measurements in India, Biancamaria et al. [2011] has demonstrated the capability of an altimeter-based flood forecasting system in Bangladesh, with RMSE from 0.6 - 0.8 m for lead times up to 5 days on 10-day basis due to T/P's repeat period. Hossain et al. [2013] further established a daily water level forecasting system in Bangladesh using observations from Jason-2 in India and HEC-RAS hydraulic model, with RMSE from 0.5 - 1.5 m and an underestimating mean bias of 0.25 - 1.25 m. However, such daily forecasting system relies on a collection of Jason-2 virtual stations (VSs) to ensure frequent sampling and data availability. Since the Mekong River is a meridional river with few number of VSs, the direct application of this system to the Mekong River becomes challenging. To address this problem, we propose a model-aided altimeter-based forecasting system. The discharge output by Variable Infiltration Capacity hydrologic model is used to reconstruct a daily water level product at upstream Jason-2 VSs based on the discharge-to-level rating curve. The reconstructed daily water level is then used to perform regression analysis with downstream in-situ water level to build regression models, which are used to forecast a daily water level. In the middle reach of the Mekong River from Nakhon Phanom to Kratie, a 3-day lead time forecasting can reach RMSE about 0.7 - 1.3 m with correlation coefficient around 0.95. For the lower reach of the Mekong River, the water flow becomes more complicated due to the reversal flow between the Tonle Sap Lake and the Mekong River

Neural Networks-Based Forecasting Regarding the Convergence Process of CEE Countries to the Eurozone

Directory of Open Access Journals (Sweden)

Magdalena RĂDULESCU

2014-06-01

Full Text Available In the crisis frame, many forecasts failed to provide well determined ratios. What we tried to explain in this paper is how some selected Central and Eastern European countries will perform in the near future: Romania, Bulgaria, Hungary, Poland and Czech Republic, using neural networks- based forecasting model which we created for the nominal and real convergence ratios. As a methodology, we propose the forecasting based on artificial neural network (ANN, using the well-known software tool GMDH Shell. For each output variable, we obtain a forecast model, according to previous values and other input related variables, and we applied the model to all countries. Our forecasts are much closer to the partial results of 2013 in the analyzed countries than the European Commission’s or other international organizations’ forecasts. The results of the forecast are important both for governments to design their financial strategies and for the investors in these selected countries. According to our results, the Czech Republic seems to be closer to achieve its nominal convergence in the next two years, but it faces great difficulties in the real convergence area, because it did not overpass the recession.

Weather forecasting based on hybrid neural model

Science.gov (United States)

Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

2017-11-01

Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

Frequency spectrum method-based stress analysis for oil pipelines in earthquake disaster areas.

Directory of Open Access Journals (Sweden)

Xiaonan Wu

Full Text Available When a long distance oil pipeline crosses an earthquake disaster area, inertial force and strong ground motion can cause the pipeline stress to exceed the failure limit, resulting in bending and deformation failure. To date, researchers have performed limited safety analyses of oil pipelines in earthquake disaster areas that include stress analysis. Therefore, using the spectrum method and theory of one-dimensional beam units, CAESAR II is used to perform a dynamic earthquake analysis for an oil pipeline in the XX earthquake disaster area. This software is used to determine if the displacement and stress of the pipeline meet the standards when subjected to a strong earthquake. After performing the numerical analysis, the primary seismic action axial, longitudinal and horizontal displacement directions and the critical section of the pipeline can be located. Feasible project enhancement suggestions based on the analysis results are proposed. The designer is able to utilize this stress analysis method to perform an ultimate design for an oil pipeline in earthquake disaster areas; therefore, improving the safe operation of the pipeline.

Frequency spectrum method-based stress analysis for oil pipelines in earthquake disaster areas.

Science.gov (United States)

Wu, Xiaonan; Lu, Hongfang; Huang, Kun; Wu, Shijuan; Qiao, Weibiao

2015-01-01

When a long distance oil pipeline crosses an earthquake disaster area, inertial force and strong ground motion can cause the pipeline stress to exceed the failure limit, resulting in bending and deformation failure. To date, researchers have performed limited safety analyses of oil pipelines in earthquake disaster areas that include stress analysis. Therefore, using the spectrum method and theory of one-dimensional beam units, CAESAR II is used to perform a dynamic earthquake analysis for an oil pipeline in the XX earthquake disaster area. This software is used to determine if the displacement and stress of the pipeline meet the standards when subjected to a strong earthquake. After performing the numerical analysis, the primary seismic action axial, longitudinal and horizontal displacement directions and the critical section of the pipeline can be located. Feasible project enhancement suggestions based on the analysis results are proposed. The designer is able to utilize this stress analysis method to perform an ultimate design for an oil pipeline in earthquake disaster areas; therefore, improving the safe operation of the pipeline.

Development of a hybrid earthquake early warning system based on single sensor technique

International Nuclear Information System (INIS)

Gravirov, V.V.; Kislov, K.V.

2012-01-01

There are two methods to earthquake early warning system: the method based on a network of seismic stations and the single-sensor method. Both have advantages and drawbacks. The current systems rely on high density seismic networks. Attempts at implementing techniques based on the single-station principle encounter difficulties in the identification of earthquake in noise. The noise may be very diverse, from stationary to impulsive. It seems a promising line of research to develop hybrid warning systems with single-sensors being incorporated in the overall early warning network. This will permit using all advantages and will help reduce the radius of the hazardous zone where no earthquake warning can be produced. The main problems are highlighted and the solutions of these are discussed. The system is implemented to include three detection processes in parallel. The first is based on the study of the co-occurrence matrix of the signal wavelet transform. The second consists in using the method of a change point in a random process and signal detection in a moving time window. The third uses artificial neural networks. Further, applying a decision rule out the final earthquake detection is carried out and estimate its reliability. (author)

Cloud-based systems for monitoring earthquakes and other environmental quantities

Science.gov (United States)

Clayton, R. W.; Olson, M.; Liu, A.; Chandy, M.; Bunn, J.; Guy, R.

2013-12-01

There are many advantages to using a cloud-based system to record and analyze environmental quantities such as earthquakes, radiation, various gases, dust and meteorological parameters. These advantages include robustness and dynamic scalability, and also reduced costs. In this paper, we present our experiences over the last three years in developing a cloud-based earthquake monitoring system (the Community Seismic Network). This network consists of over 600 sensors (accelerometers) in the S. California region that send data directly to the Google App Engine where they are analyzed. The system is capable of handing many other types of sensor data and generating a situation-awareness analysis as a product. Other advantages to the cloud-based system are integration with other peer networks, and being able to deploy anywhere in the world without have to build addition computing infrastructure.

Thermal IR satellite data application for earthquake research in Pakistan

Science.gov (United States)

Barkat, Adnan; Ali, Aamir; Rehman, Khaista; Awais, Muhammad; Riaz, Muhammad Shahid; Iqbal, Talat

2018-05-01

The scientific progress in space research indicates earthquake-related processes of surface temperature growth, gas/aerosol exhalation and electromagnetic disturbances in the ionosphere prior to seismic activity. Among them surface temperature growth calculated using the satellite thermal infrared images carries valuable earthquake precursory information for near/distant earthquakes. Previous studies have concluded that such information can appear few days before the occurrence of an earthquake. The objective of this study is to use MODIS thermal imagery data for precursory analysis of Kashmir (Oct 8, 2005; Mw 7.6; 26 km), Ziarat (Oct 28, 2008; Mw 6.4; 13 km) and Dalbandin (Jan 18, 2011; Mw 7.2; 69 km) earthquakes. Our results suggest that there exists an evident correlation of Land Surface Temperature (thermal; LST) anomalies with seismic activity. In particular, a rise of 3-10 °C in LST is observed 6, 4 and 14 days prior to Kashmir, Ziarat and Dalbandin earthquakes. In order to further elaborate our findings, we have presented a comparative and percentile analysis of daily and five years averaged LST for a selected time window with respect to the month of earthquake occurrence. Our comparative analyses of daily and five years averaged LST show a significant change of 6.5-7.9 °C for Kashmir, 8.0-8.1 °C for Ziarat and 2.7-5.4 °C for Dalbandin earthquakes. This significant change has high percentile values for the selected events i.e. 70-100% for Kashmir, 87-100% for Ziarat and 84-100% for Dalbandin earthquakes. We expect that such consistent results may help in devising an optimal earthquake forecasting strategy and to mitigate the effect of associated seismic hazards.

Mutual Information-Based Inputs Selection for Electric Load Time Series Forecasting

Directory of Open Access Journals (Sweden)

Nenad Floranović

2013-02-01

Full Text Available Providing accurate load forecast to electric utility corporations is essential in order to reduce their operational costs and increase profits. Hence, training set selection is an important preprocessing step which has to be considered in practice in order to increase the accuracy of load forecasts. The usage of mutual information (MI has been recently proposed in regression tasks, mostly for feature selection and for identifying the real instances from training sets that contains noise and outliers. This paper proposes a methodology for the training set selection in a least squares support vector machines (LS-SVMs load forecasting model. A new application of the concept of MI is presented for the selection of a training set based on MI computation between initial training set instances and testing set instances. Accordingly, several LS-SVMs models have been trained, based on the proposed methodology, for hourly prediction of electric load for one day ahead. The results obtained from a real-world data set indicate that the proposed method increases the accuracy of load forecasting as well as reduces the size of the initial training set needed for model training.

Combined Forecasting of Rainfall Based on Fuzzy Clustering and Cross Entropy

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Baohui Men

2017-12-01

Full Text Available Rainfall is an essential index to measure drought, and it is dependent upon various parameters including geographical environment, air temperature and pressure. The nonlinear nature of climatic variables leads to problems such as poor accuracy and instability in traditional forecasting methods. In this paper, the combined forecasting method based on data mining technology and cross entropy is proposed to forecast the rainfall with full consideration of the time-effectiveness of historical data. In view of the flaws of the fuzzy clustering method which is easy to fall into local optimal solution and low speed of operation, the ant colony algorithm is adopted to overcome these shortcomings and, as a result, refine the model. The method for determining weights is also improved by using the cross entropy. Besides, the forecast is conducted by analyzing the weighted average rainfall based on Thiessen polygon in the Beijing–Tianjin–Hebei region. Since the predictive errors are calculated, the results show that improved ant colony fuzzy clustering can effectively select historical data and enhance the accuracy of prediction so that the damage caused by extreme weather events like droughts and floods can be greatly lessened and even kept at bay.

Probabilistic runoff volume forecasting in risk-based optimization for RTC of urban drainage systems

DEFF Research Database (Denmark)

Löwe, Roland; Vezzaro, Luca; Mikkelsen, Peter Steen

2016-01-01

overflow risk. The stochastic control framework and the performance of the runoff forecasting models are tested in a case study in Copenhagen (76 km2 with 6 sub-catchments and 7 control points) using 2-h radar rainfall forecasts and inlet flows to control points computed from a variety of noisy...... smoothing. Simulations demonstrate notable improvements of the control efficiency when considering forecast information and additionally when considering forecast uncertainty, compared with optimization based on current basin fillings only....

Forecast of Frost Days Based on Monthly Temperatures

Science.gov (United States)

Castellanos, M. T.; Tarquis, A. M.; Morató, M. C.; Saa-Requejo, A.

2009-04-01

Although frost can cause considerable crop damage and mitigation practices against forecasted frost exist, frost forecasting technologies have not changed for many years. The paper reports a new method to forecast the monthly number of frost days (FD) for several meteorological stations at Community of Madrid (Spain) based on successive application of two models. The first one is a stochastic model, autoregressive integrated moving average (ARIMA), that forecasts monthly minimum absolute temperature (tmin) and monthly average of minimum temperature (tminav) following Box-Jenkins methodology. The second model relates these monthly temperatures to minimum daily temperature distribution during one month. Three ARIMA models were identified for the time series analyzed with a stational period correspondent to one year. They present the same stational behavior (moving average differenced model) and different non-stational part: autoregressive model (Model 1), moving average differenced model (Model 2) and autoregressive and moving average model (Model 3). At the same time, the results point out that minimum daily temperature (tdmin), for the meteorological stations studied, followed a normal distribution each month with a very similar standard deviation through years. This standard deviation obtained for each station and each month could be used as a risk index for cold months. The application of Model 1 to predict minimum monthly temperatures showed the best FD forecast. This procedure provides a tool for crop managers and crop insurance companies to asses the risk of frost frequency and intensity, so that they can take steps to mitigate against frost damage and estimated the damage that frost would cost. This research was supported by Comunidad de Madrid Research Project 076/92. The cooperation of the Spanish National Meteorological Institute and the Spanish Ministerio de Agricultura, Pesca y Alimentation (MAPA) is gratefully acknowledged.

Statistical eruption forecast for the Chilean Southern Volcanic Zone: typical probabilities of volcanic eruptions as baseline for possibly enhanced activity following the large 2010 Concepción earthquake

Directory of Open Access Journals (Sweden)

Y. Dzierma

2010-10-01

Full Text Available A probabilistic eruption forecast is provided for ten volcanoes of the Chilean Southern Volcanic Zone (SVZ. Since 70% of the Chilean population lives in this area, the estimation of future eruption likelihood is an important part of hazard assessment. After investigating the completeness and stationarity of the historical eruption time series, the exponential, Weibull, and log-logistic distribution functions are fit to the repose time distributions for the individual volcanoes and the models are evaluated. This procedure has been implemented in two different ways to methodologically compare details in the fitting process. With regard to the probability of at least one VEI ≥ 2 eruption in the next decade, Llaima, Villarrica and Nevados de Chillán are most likely to erupt, while Osorno shows the lowest eruption probability among the volcanoes analysed. In addition to giving a compilation of the statistical eruption forecasts along the historically most active volcanoes of the SVZ, this paper aims to give "typical" eruption probabilities, which may in the future permit to distinguish possibly enhanced activity in the aftermath of the large 2010 Concepción earthquake.

Exploring the applicability of future air quality predictions based on synoptic system forecasts

International Nuclear Information System (INIS)

Yuval; Broday, David M.; Alpert, Pinhas

2012-01-01

For a given emissions inventory, the general levels of air pollutants and the spatial distribution of their concentrations are determined by the physiochemical state of the atmosphere. Apart from the trivial seasonal and daily cycles, most of the variability is associated with the atmospheric synoptic scale. A simple methodology for assessing future levels of air pollutants' concentrations based on synoptic forecasts is presented. At short time scales the methodology is comparable and slightly better than persistence and seasonal forecasts at categorical classification of pollution levels. It's utility is shown for air quality studies at the long time scale of a changing climate scenario, where seasonality and persistence cannot be used. It is demonstrated that the air quality variability due to changes in the pollution emissions can be expected to be much larger than that associated with the effects of climatic changes. - Highlights: ► A method for short and long term air quality forecasts is introduced. ► The method is based on prediction of synoptic systems. ► The method beats simple benchmarks in short term forecasts. ► Assessment of future air pollution in a changing climate scenario is demonstrated. - Air quality in a changing climate scenario can be studied using air pollution predictions based on synoptic system forecasts.

Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks

Directory of Open Access Journals (Sweden)

Zhisheng Zhang

2016-01-01

Full Text Available Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of K-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.

An improvement of the Earthworm Based Earthquake Alarm Reporting system in Taiwan

Science.gov (United States)

Chen, D. Y.; Hsiao, N. C.; Yih-Min, W.

2017-12-01

The Central Weather Bureau of Taiwan (CWB) has operated the Earthworm Based Earthquake Alarm Reporting (eBEAR) system for the purpose of earthquake early warning (EEW). The system has been used to report EEW messages to the general public since 2016 through text message from the mobile phones and the television programs. The system for inland earthquakes is able to provide accurate and fast warnings. The average epicenter error is about 5 km and the processing time is about 15 seconds. The epicenter error is defined as the distance between the epicenter estimated by the EEW system and the epicenter estimated by man. The processing time is defined as the time difference between the time earthquakes occurred and the time the system issued warning. The CWB seismic network consist about 200 seismic stations. In some area of Taiwan the distance between each seismic station is about 10 km. It means that when an earthquake occurred the seismic P wave is able to propagate through 6 stations, which is the minimum number of required stations in the EEW system, within 20 km. If the latency of data transmitting is about 1 sec, the P-wave velocity is about 6 km per sec and we take 3-sec length time window to estimate earthquake magnitude, then the processing should be around 8 sec. In fact, however, the average processing time is larger than this figure. Because some outliers of P-wave onset picks may exist in the beginning of the earthquake occurrence, the Geiger's method we used in the EEW system for earthquake location is not stable. It usually takes more time to wait for enough number of good picks. In this study we used grid search method to improve the estimations of earthquake location. The MAXEL algorithm (Sheen et al., 2015, 2016) was tested in the EEW system by simulating historical earthquakes occurred in Taiwan. The results show the processing time can be reduced and the location accuracy is acceptable for EEW purpose.

Seismic‐hazard forecast for 2016 including induced and natural earthquakes in the central and eastern United States

Science.gov (United States)

Petersen, Mark D.; Mueller, Charles; Moschetti, Morgan P.; Hoover, Susan M.; Llenos, Andrea L.; Ellsworth, William L.; Michael, Andrew J.; Rubinstein, Justin L.; McGarr, Arthur F.; Rukstales, Kenneth S.

2016-01-01

The U.S. Geological Survey (USGS) has produced a one‐year (2016) probabilistic seismic‐hazard assessment for the central and eastern United States (CEUS) that includes contributions from both induced and natural earthquakes that are constructed with probabilistic methods using alternative data and inputs. This hazard assessment builds on our 2016 final model (Petersen et al., 2016) by adding sensitivity studies, illustrating hazard in new ways, incorporating new population data, and discussing potential improvements. The model considers short‐term seismic activity rates (primarily 2014–2015) and assumes that the activity rates will remain stationary over short time intervals. The final model considers different ways of categorizing induced and natural earthquakes by incorporating two equally weighted earthquake rate submodels that are composed of alternative earthquake inputs for catalog duration, smoothing parameters, maximum magnitudes, and ground‐motion models. These alternatives represent uncertainties on how we calculate earthquake occurrence and the diversity of opinion within the science community. In this article, we also test sensitivity to the minimum moment magnitude between M 4 and M 4.7 and the choice of applying a declustered catalog with b=1.0 rather than the full catalog with b=1.3. We incorporate two earthquake rate submodels: in the informed submodel we classify earthquakes as induced or natural, and in the adaptive submodel we do not differentiate. The alternative submodel hazard maps both depict high hazard and these are combined in the final model. Results depict several ground‐shaking measures as well as intensity and include maps showing a high‐hazard level (1% probability of exceedance in 1 year or greater). Ground motions reach 0.6g horizontal peak ground acceleration (PGA) in north‐central Oklahoma and southern Kansas, and about 0.2g PGA in the Raton basin of Colorado and New Mexico, in central Arkansas, and in

Comparison of the sand liquefaction estimated based on codes and practical earthquake damage phenomena

Science.gov (United States)

Fang, Yi; Huang, Yahong

2017-12-01

Conducting sand liquefaction estimated based on codes is the important content of the geotechnical design. However, the result, sometimes, fails to conform to the practical earthquake damages. Based on the damage of Tangshan earthquake and engineering geological conditions, three typical sites are chosen. Moreover, the sand liquefaction probability was evaluated on the three sites by using the method in the Code for Seismic Design of Buildings and the results were compared with the sand liquefaction phenomenon in the earthquake. The result shows that the difference between sand liquefaction estimated based on codes and the practical earthquake damage is mainly attributed to the following two aspects: The primary reasons include disparity between seismic fortification intensity and practical seismic oscillation, changes of groundwater level, thickness of overlying non-liquefied soil layer, local site effect and personal error. Meanwhile, although the judgment methods in the codes exhibit certain universality, they are another reason causing the above difference due to the limitation of basic data and the qualitative anomaly of the judgment formulas.

OMG Earthquake! Can Twitter improve earthquake response?

Science.gov (United States)

Earle, P. S.; Guy, M.; Ostrum, C.; Horvath, S.; Buckmaster, R. A.

2009-12-01

The U.S. Geological Survey (USGS) is investigating how the social networking site Twitter, a popular service for sending and receiving short, public, text messages, can augment its earthquake response products and the delivery of hazard information. The goal is to gather near real-time, earthquake-related messages (tweets) and provide geo-located earthquake detections and rough maps of the corresponding felt areas. Twitter and other social Internet technologies are providing the general public with anecdotal earthquake hazard information before scientific information has been published from authoritative sources. People local to an event often publish information within seconds via these technologies. In contrast, depending on the location of the earthquake, scientific alerts take between 2 to 20 minutes. Examining the tweets following the March 30, 2009, M4.3 Morgan Hill earthquake shows it is possible (in some cases) to rapidly detect and map the felt area of an earthquake using Twitter responses. Within a minute of the earthquake, the frequency of “earthquake” tweets rose above the background level of less than 1 per hour to about 150 per minute. Using the tweets submitted in the first minute, a rough map of the felt area can be obtained by plotting the tweet locations. Mapping the tweets from the first six minutes shows observations extending from Monterey to Sacramento, similar to the perceived shaking region mapped by the USGS “Did You Feel It” system. The tweets submitted after the earthquake also provided (very) short first-impression narratives from people who experienced the shaking. Accurately assessing the potential and robustness of a Twitter-based system is difficult because only tweets spanning the previous seven days can be searched, making a historical study impossible. We have, however, been archiving tweets for several months, and it is clear that significant limitations do exist. The main drawback is the lack of quantitative information

Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method

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Wen-Yeau Chang

2013-09-01

Full Text Available High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO based hybrid forecasting method for short-term wind power forecasting. The hybrid forecasting method combines the persistence method, the back propagation neural network, and the radial basis function (RBF neural network. The EPSO algorithm is employed to optimize the weight coefficients in the hybrid forecasting method. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of wind power generation of a wind energy conversion system (WECS installed on the Taichung coast of Taiwan. Comparisons of forecasting performance are made with the individual forecasting methods. Good agreements between the realistic values and forecasting values are obtained; the test results show the proposed forecasting method is accurate and reliable.

Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts

Science.gov (United States)

Schmidt, Thomas; Kalisch, John; Lorenz, Elke; Heinemann, Detlev

2016-03-01

Clouds are the dominant source of small-scale variability in surface solar radiation and uncertainty in its prediction. However, the increasing share of solar energy in the worldwide electric power supply increases the need for accurate solar radiation forecasts. In this work, we present results of a very short term global horizontal irradiance (GHI) forecast experiment based on hemispheric sky images. A 2-month data set with images from one sky imager and high-resolution GHI measurements from 99 pyranometers distributed over 10 km by 12 km is used for validation. We developed a multi-step model and processed GHI forecasts up to 25 min with an update interval of 15 s. A cloud type classification is used to separate the time series into different cloud scenarios. Overall, the sky-imager-based forecasts do not outperform the reference persistence forecasts. Nevertheless, we find that analysis and forecast performance depends strongly on the predominant cloud conditions. Especially convective type clouds lead to high temporal and spatial GHI variability. For cumulus cloud conditions, the analysis error is found to be lower than that introduced by a single pyranometer if it is used representatively for the whole area in distances from the camera larger than 1-2 km. Moreover, forecast skill is much higher for these conditions compared to overcast or clear sky situations causing low GHI variability, which is easier to predict by persistence. In order to generalize the cloud-induced forecast error, we identify a variability threshold indicating conditions with positive forecast skill.

Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts

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T. Schmidt

2016-03-01

Full Text Available Clouds are the dominant source of small-scale variability in surface solar radiation and uncertainty in its prediction. However, the increasing share of solar energy in the worldwide electric power supply increases the need for accurate solar radiation forecasts. In this work, we present results of a very short term global horizontal irradiance (GHI forecast experiment based on hemispheric sky images. A 2-month data set with images from one sky imager and high-resolution GHI measurements from 99 pyranometers distributed over 10 km by 12 km is used for validation. We developed a multi-step model and processed GHI forecasts up to 25 min with an update interval of 15 s. A cloud type classification is used to separate the time series into different cloud scenarios. Overall, the sky-imager-based forecasts do not outperform the reference persistence forecasts. Nevertheless, we find that analysis and forecast performance depends strongly on the predominant cloud conditions. Especially convective type clouds lead to high temporal and spatial GHI variability. For cumulus cloud conditions, the analysis error is found to be lower than that introduced by a single pyranometer if it is used representatively for the whole area in distances from the camera larger than 1–2 km. Moreover, forecast skill is much higher for these conditions compared to overcast or clear sky situations causing low GHI variability, which is easier to predict by persistence. In order to generalize the cloud-induced forecast error, we identify a variability threshold indicating conditions with positive forecast skill.

Evaluating the spatio-temporal performance of sky imager based solar irradiance analysis and forecasts

Science.gov (United States)

Schmidt, T.; Kalisch, J.; Lorenz, E.; Heinemann, D.

2015-10-01

Clouds are the dominant source of variability in surface solar radiation and uncertainty in its prediction. However, the increasing share of solar energy in the world-wide electric power supply increases the need for accurate solar radiation forecasts. In this work, we present results of a shortest-term global horizontal irradiance (GHI) forecast experiment based on hemispheric sky images. A two month dataset with images from one sky imager and high resolutive GHI measurements from 99 pyranometers distributed over 10 km by 12 km is used for validation. We developed a multi-step model and processed GHI forecasts up to 25 min with an update interval of 15 s. A cloud type classification is used to separate the time series in different cloud scenarios. Overall, the sky imager based forecasts do not outperform the reference persistence forecasts. Nevertheless, we find that analysis and forecast performance depend strongly on the predominant cloud conditions. Especially convective type clouds lead to high temporal and spatial GHI variability. For cumulus cloud conditions, the analysis error is found to be lower than that introduced by a single pyranometer if it is used representatively for the whole area in distances from the camera larger than 1-2 km. Moreover, forecast skill is much higher for these conditions compared to overcast or clear sky situations causing low GHI variability which is easier to predict by persistence. In order to generalize the cloud-induced forecast error, we identify a variability threshold indicating conditions with positive forecast skill.

Quasi-static earthquake cycle simulation based on nonlinear viscoelastic finite element analyses

Science.gov (United States)

Agata, R.; Ichimura, T.; Hyodo, M.; Barbot, S.; Hori, T.

2017-12-01

To explain earthquake generation processes, simulation methods of earthquake cycles have been studied. For such simulations, the combination of the rate- and state-dependent friction law at the fault plane and the boundary integral method based on Green's function in an elastic half space is widely used (e.g. Hori 2009; Barbot et al. 2012). In this approach, stress change around the fault plane due to crustal deformation can be computed analytically, while the effects of complex physics such as mantle rheology and gravity are generally not taken into account. To consider such effects, we seek to develop an earthquake cycle simulation combining crustal deformation computation based on the finite element (FE) method with the rate- and state-dependent friction law. Since the drawback of this approach is the computational cost associated with obtaining numerical solutions, we adopt a recently developed fast and scalable FE solver (Ichimura et al. 2016), which assumes use of supercomputers, to solve the problem in a realistic time. As in the previous approach, we solve the governing equations consisting of the rate- and state-dependent friction law. In solving the equations, we compute stress changes along the fault plane due to crustal deformation using FE simulation, instead of computing them by superimposing slip response function as in the previous approach. In stress change computation, we take into account nonlinear viscoelastic deformation in the asthenosphere. In the presentation, we will show simulation results in a normative three-dimensional problem, where a circular-shaped velocity-weakening area is set in a square-shaped fault plane. The results with and without nonlinear viscosity in the asthenosphere will be compared. We also plan to apply the developed code to simulate the post-earthquake deformation of a megathrust earthquake, such as the 2011 Tohoku earthquake. Acknowledgment: The results were obtained using the K computer at the RIKEN (Proposal number

Case history of an anticipated event: The major (Mw = 7.0) Vrancea, Romania earthquake of 1986 - revisited

International Nuclear Information System (INIS)

Marza, V.; Burlacu, B V.; Pantea, A; Malita, Z.

2002-01-01

This is a reissue of a paper initially published in the European Seismological Commission Proceedings of the XXI General Assembly held on 23-27 August 1988 in Sofia, Bulgaria, p. 515-523, and released in 1989. We present here an excerpt of the original paper, taking only advantage of the modern digital graphics, removing some 'typing' mistakes or adding some explanatory late notes, in order to remember the conspicuous earthquake prediction research results done by Romanian seismology after the forecasted 1977 Vrancea major event. For the sake of understanding we distinguish between earthquake forecasting (long-term prediction, that is a time-window of years, but less than 20% of the mean return period for the involved magnitude and a lead time of years) and earthquake anticipation (medium-term prediction, i.e. a time-window of a few months and a lead time of months), stages what proved to be feasible for Vrancea seismogenic zone. Analysis and discussion of a variety of precursory seismicity patterns (p.s.p.) belonging to all temporal developmental stages of the preparatory (geo)physical process leading to the killer and damaging major subcrustal Vrancea, Romania, earthquake of August 30, 1986 (epicenter = 45.5 angle N/26.4 angle E; depth 144 km; magnitude(s) m w =7.0, M w =7.3, M L =7.0; I o =VIII 1/2 MSK) are performed and documented, clearly proving that the earthquake would not has been unexpected. The salient features of the Vrancea Seismogenic Zone (VSZ) and its tectonic setting have been presented elsewhere. The seismological data base used in this study is the earthquake master catalogue of Constantinescu and Marza, updated on the basis of the data supplied by the real-time telemetered seismographic network of Romania, centered on VSZ. The contents of the paper is as follows: 1. Introduction; 2. The Vrancea 1986 Major (m w =7.0) Subcrustal Earthquake Related Precursors; 2.1. Regularity Patterns; 2.2. Preseismic Quiescence; 2.3. Hypocentral migration

Flood forecasting and uncertainty of precipitation forecasts

International Nuclear Information System (INIS)

Kobold, Mira; Suselj, Kay

2004-01-01

The timely and accurate flood forecasting is essential for the reliable flood warning. The effectiveness of flood warning is dependent on the forecast accuracy of certain physical parameters, such as the peak magnitude of the flood, its timing, location and duration. The conceptual rainfall - runoff models enable the estimation of these parameters and lead to useful operational forecasts. The accurate rainfall is the most important input into hydrological models. The input for the rainfall can be real time rain-gauges data, or weather radar data, or meteorological forecasted precipitation. The torrential nature of streams and fast runoff are characteristic for the most of the Slovenian rivers. Extensive damage is caused almost every year- by rainstorms affecting different regions of Slovenia' The lag time between rainfall and runoff is very short for Slovenian territory and on-line data are used only for now casting. Forecasted precipitations are necessary for hydrological forecast for some days ahead. ECMWF (European Centre for Medium-Range Weather Forecasts) gives general forecast for several days ahead while more detailed precipitation data with limited area ALADIN/Sl model are available for two days ahead. There is a certain degree of uncertainty using such precipitation forecasts based on meteorological models. The variability of precipitation is very high in Slovenia and the uncertainty of ECMWF predicted precipitation is very large for Slovenian territory. ECMWF model can predict precipitation events correctly, but underestimates amount of precipitation in general The average underestimation is about 60% for Slovenian region. The predictions of limited area ALADIN/Si model up to; 48 hours ahead show greater applicability in hydrological forecasting. The hydrological models are sensitive to precipitation input. The deviation of runoff is much bigger than the rainfall deviation. Runoff to rainfall error fraction is about 1.6. If spatial and time distribution

Forecasting Energy CO2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model

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Xingsheng Gu

2013-03-01

Full Text Available he accurate forecasting of carbon dioxide (CO2 emissions from fossil fuel energy consumption is a key requirement for making energy policy and environmental strategy. In this paper, a novel quantum harmony search (QHS algorithm-based discounted mean square forecast error (DMSFE combination model is proposed. In the DMSFE combination forecasting model, almost all investigations assign the discounting factor (β arbitrarily since β varies between 0 and 1 and adopt one value for all individual models and forecasting periods. The original method doesn’t consider the influences of the individual model and the forecasting period. This work contributes by changing β from one value to a matrix taking the different model and the forecasting period into consideration and presenting a way of searching for the optimal β values by using the QHS algorithm through optimizing the mean absolute percent error (MAPE objective function. The QHS algorithm-based optimization DMSFE combination forecasting model is established and tested by forecasting CO2 emission of the World top‒5 CO2 emitters. The evaluation indexes such as MAPE, root mean squared error (RMSE and mean absolute error (MAE are employed to test the performance of the presented approach. The empirical analyses confirm the validity of the presented method and the forecasting accuracy can be increased in a certain degree.

Why local people did not present a problem in the 2016 Kumamoto earthquake, Japan though people accused in the 2009 L'Aquila earthquake?

Science.gov (United States)

Sugimoto, M.

2016-12-01

Risk communication is a big issues among seismologists after the 2009 L'Aquila earthquake all over the world. A lot of people remember 7 researchers as "L'Aquila 7" were accused in Italy. Seismologists said it is impossible to predict an earthquake by science technology today and join more outreach activities. "In a subsequent inquiry of the handling of the disaster, seven members of the Italian National Commission for the Forecast and Prevention of Major Risks were accused of giving "inexact, incomplete and contradictory" information about the danger of the tremors prior to the main quake. On 22 October 2012, six scientists and one ex-government official were convicted of multiple manslaughter for downplaying the likelihood of a major earthquake six days before it took place. They were each sentenced to six years' imprisonment (Wikipedia)". Finally 6 scientists are not guilty. The 2016 Kumamoto earthquake hit Kyushu, Japan in April. They are very similar seismological situations between the 2016 Kumamoto earthquake and the 2009 L'Aquila earthquake. The foreshock was Mj6.5 and Mw6.2 in 14 April 2016. The main shock was Mj7.3 and Mw7.0. Japan Metrological Agency (JMA) misleaded foreshock as mainshock before main shock occured. 41 people died by the main shock in Japan. However local people did not accused scientists in Japan. It has been less big earhquakes around 100 years in Kumamoto. Poeple was not so matured that they treated earthquake information in Kyushu, Japan. How are there differences between Japan and Italy? We learn about outreach activities for sciencits from this case.

Insights into earthquake hazard map performance from shaking history simulations

Science.gov (United States)

Stein, S.; Vanneste, K.; Camelbeeck, T.; Vleminckx, B.

2017-12-01

Why recent large earthquakes caused shaking stronger than predicted by earthquake hazard maps is under debate. This issue has two parts. Verification involves how well maps implement probabilistic seismic hazard analysis (PSHA) ("have we built the map right?"). Validation asks how well maps forecast shaking ("have we built the right map?"). We explore how well a map can ideally perform by simulating an area's shaking history and comparing "observed" shaking to that predicted by a map generated for the same parameters. The simulations yield shaking distributions whose mean is consistent with the map, but individual shaking histories show large scatter. Infrequent large earthquakes cause shaking much stronger than mapped, as observed. Hence, PSHA seems internally consistent and can be regarded as verified. Validation is harder because an earthquake history can yield shaking higher or lower than that predicted while being consistent with the hazard map. The scatter decreases for longer observation times because the largest earthquakes and resulting shaking are increasingly likely to have occurred. For the same reason, scatter is much less for the more active plate boundary than for a continental interior. For a continental interior, where the mapped hazard is low, even an M4 event produces exceedances at some sites. Larger earthquakes produce exceedances at more sites. Thus many exceedances result from small earthquakes, but infrequent large ones may cause very large exceedances. However, for a plate boundary, an M6 event produces exceedance at only a few sites, and an M7 produces them in a larger, but still relatively small, portion of the study area. As reality gives only one history, and a real map involves assumptions about more complicated source geometries and occurrence rates, which are unlikely to be exactly correct and thus will contribute additional scatter, it is hard to assess whether misfit between actual shaking and a map — notably higher

Preliminary Report Summarizes Tsunami Impacts and Lessons Learned from the September 7, 2017, M8.1 Tehuantepec Earthquake

Science.gov (United States)

Wilson, R. I.; Ramirez-Herrera, M. T.; Dengler, L. A.; Miller, K.; LaDuke, Y.

2017-12-01

The preliminary tsunami impacts from the September 7, 2017, M8.1 Tehuantepec Earthquake have been summarized in the following report: https://www.eeri.org/wp-content/uploads/EERI-Recon-Rpt-090717-Mexico-tsunami_fn.pdf. Although the tsunami impacts were not as significant as those from the earthquake itself (98 fatalities and 41,000 homes damaged), the following are highlights and lessons learned: The Tehuantepec earthquake was one of the largest down-slab normal faulting events ever recorded. This situation complicated the tsunami forecast since forecast methods and pre-event modeling are primarily associated with megathrust earthquakes where the most significant tsunamis are generated. Adding non-megathrust source modeling to the tsunami forecast databases of conventional warning systems should be considered. Offshore seismic and tsunami hazard analyses using past events should incorporate the potential for large earthquakes occurring along sources other than the megathrust boundary. From an engineering perspective, initial reports indicate there was only minor tsunami damage along the Mexico coast. There was damage to Marina Chiapas where floating docks overtopped their piles. Increasing pile heights could reduce the potential for damage to floating docks. Tsunami warning notifications did not get to the public in time to assist with evacuation. Streamlining the messaging in Mexico from the warning system directly to the public should be considered. And, for local events, preparedness efforts should place emphasis on responding to feeling the earthquake and not waiting to be notified. Although the U.S. tsunami warning centers were timely with their international and domestic messaging, there were some issues with how those messages were presented and interpreted. The use of a "Tsunami Threat" banner on the new main warning center website created confusion with emergency managers in the U.S. where no tsunami threat was expected to exist. Also, some U.S. states and

Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior

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Yuancheng Li

2016-11-01

Full Text Available The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day loads of users are calculated separately according to different date types (ordinary workdays, day before holidays, holidays. Second, the similarity between user electricity behaviors is mined and the user electricity loads are clustered to classify the users with similar behaviors into the same cluster. Finally, the load forecasting model based on the Online Sequential Extreme Learning Machine (OS-ELM is applied to different clusters to conduct load forecasting and the load forecast is summed to obtain the system load. In order to prove the validity of the proposed method, we performed simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation. The experimental results show that the proposed method is able to mine the user electricity behaviors deeply, improve the accuracy of load forecasting by the reasonable clustering of users, and reveal the relationship between forecasting accuracy and cluster numbers.

Grey Forecast Rainfall with Flow Updating Algorithm for Real-Time Flood Forecasting

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Jui-Yi Ho

2015-04-01

Full Text Available The dynamic relationship between watershed characteristics and rainfall-runoff has been widely studied in recent decades. Since watershed rainfall-runoff is a non-stationary process, most deterministic flood forecasting approaches are ineffective without the assistance of adaptive algorithms. The purpose of this paper is to propose an effective flow forecasting system that integrates a rainfall forecasting model, watershed runoff model, and real-time updating algorithm. This study adopted a grey rainfall forecasting technique, based on existing hourly rainfall data. A geomorphology-based runoff model can be used for simulating impacts of the changing geo-climatic conditions on the hydrologic response of unsteady and non-linear watershed system, and flow updating algorithm were combined to estimate watershed runoff according to measured flow data. The proposed flood forecasting system was applied to three watersheds; one in the United States and two in Northern Taiwan. Four sets of rainfall-runoff simulations were performed to test the accuracy of the proposed flow forecasting technique. The results indicated that the forecast and observed hydrographs are in good agreement for all three watersheds. The proposed flow forecasting system could assist authorities in minimizing loss of life and property during flood events.

Recognition of underground nuclear explosion and natural earthquake based on neural network

International Nuclear Information System (INIS)

Yang Hong; Jia Weimin

2000-01-01

Many features are extracted to improve the identified rate and reliability of underground nuclear explosion and natural earthquake. But how to synthesize these characters is the key of pattern recognition. Based on the improved Delta algorithm, features of underground nuclear explosion and natural earthquake are inputted into BP neural network, and friendship functions are constructed to identify the output values. The identified rate is up to 92.0%, which shows that: the way is feasible

Predicting Dynamic Response of Structures under Earthquake Loads Using Logical Analysis of Data

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Ayman Abd-Elhamed

2018-04-01

Full Text Available In this paper, logical analysis of data (LAD is used to predict the seismic response of building structures employing the captured dynamic responses. In order to prepare the data, computational simulations using a single degree of freedom (SDOF building model under different ground motion records are carried out. The selected excitation records are real and of different peak ground accelerations (PGA. The sensitivity of the seismic response in terms of displacements of floors to the variation in earthquake characteristics, such as soil class, characteristic period, and time step of records, peak ground displacement, and peak ground velocity, have also been considered. The dynamic equation of motion describing the building model and the applied earthquake load are presented and solved incrementally using the Runge-Kutta method. LAD then finds the characteristic patterns which lead to forecast the seismic response of building structures. The accuracy of LAD is compared to that of an artificial neural network (ANN, since the latter is the most known machine learning technique. Based on the conducted study, the proposed LAD model has been proven to be an efficient technique to learn, simulate, and blindly predict the dynamic response behaviour of building structures subjected to earthquake loads.

Rapid modeling of complex multi-fault ruptures with simplistic models from real-time GPS: Perspectives from the 2016 Mw 7.8 Kaikoura earthquake

Science.gov (United States)

Crowell, B.; Melgar, D.

2017-12-01

The 2016 Mw 7.8 Kaikoura earthquake is one of the most complex earthquakes in recent history, rupturing across at least 10 disparate faults with varying faulting styles, and exhibiting intricate surface deformation patterns. The complexity of this event has motivated the need for multidisciplinary geophysical studies to get at the underlying source physics to better inform earthquake hazards models in the future. However, events like Kaikoura beg the question of how well (or how poorly) such earthquakes can be modeled automatically in real-time and still satisfy the general public and emergency managers. To investigate this question, we perform a retrospective real-time GPS analysis of the Kaikoura earthquake with the G-FAST early warning module. We first perform simple point source models of the earthquake using peak ground displacement scaling and a coseismic offset based centroid moment tensor (CMT) inversion. We predict ground motions based on these point sources as well as simple finite faults determined from source scaling studies, and validate against true recordings of peak ground acceleration and velocity. Secondly, we perform a slip inversion based upon the CMT fault orientations and forward model near-field tsunami maximum expected wave heights to compare against available tide gauge records. We find remarkably good agreement between recorded and predicted ground motions when using a simple fault plane, with the majority of disagreement in ground motions being attributable to local site effects, not earthquake source complexity. Similarly, the near-field tsunami maximum amplitude predictions match tide gauge records well. We conclude that even though our models for the Kaikoura earthquake are devoid of rich source complexities, the CMT driven finite fault is a good enough "average" source and provides useful constraints for rapid forecasting of ground motion and near-field tsunami amplitudes.

A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting

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Chengshi Tian

2018-03-01

Full Text Available Short-term load forecasting plays an indispensable role in electric power systems, which is not only an extremely challenging task but also a concerning issue for all society due to complex nonlinearity characteristics. However, most previous combined forecasting models were based on optimizing weight coefficients to develop a linear combined forecasting model, while ignoring that the linear combined model only considers the contribution of the linear terms to improving the model’s performance, which will lead to poor forecasting results because of the significance of the neglected and potential nonlinear terms. In this paper, a novel nonlinear combined forecasting system, which consists of three modules (improved data pre-processing module, forecasting module and the evaluation module is developed for short-term load forecasting. Different from the simple data pre-processing of most previous studies, the improved data pre-processing module based on longitudinal data selection is successfully developed in this system, which further improves the effectiveness of data pre-processing and then enhances the final forecasting performance. Furthermore, the modified support vector machine is developed to integrate all the individual predictors and obtain the final prediction, which successfully overcomes the upper drawbacks of the linear combined model. Moreover, the evaluation module is incorporated to perform a scientific evaluation for the developed system. The half-hourly electrical load data from New South Wales are employed to verify the effectiveness of the developed forecasting system, and the results reveal that the developed nonlinear forecasting system can be employed in the dispatching and planning for smart grids.

USGS Imagery Applications During Disaster Response After Recent Earthquakes

Science.gov (United States)

Hudnut, K. W.; Brooks, B. A.; Glennie, C. L.; Finnegan, D. C.

2015-12-01

It is not only important to rapidly characterize surface fault rupture and related ground deformation after an earthquake, but also to repeatedly make observations following an event to forecast fault afterslip. These data may also be used by other agencies to monitor progress on damage repairs and restoration efforts by emergency responders and the public. Related requirements include repeatedly obtaining reference or baseline imagery before a major disaster occurs, as well as maintaining careful geodetic control on all imagery in a time series so that absolute georeferencing may be applied to the image stack through time. In addition, repeated post-event imagery acquisition is required, generally at a higher repetition rate soon after the event, then scaled back to less frequent acquisitions with time, to capture phenomena (such as fault afterslip) that are known to have rates that decrease rapidly with time. For example, lidar observations acquired before and after the South Napa earthquake of 2014, used in our extensive post-processing work that was funded primarily by FEMA, aided in the accurate forecasting of fault afterslip. Lidar was used to independently validate and verify the official USGS afterslip forecast. In order to keep pace with rapidly evolving technology, a development pipeline must be established and maintained to continually test and incorporate new sensors, while adapting these new components to the existing platform and linking them to the existing base software system, and then sequentially testing the system as it evolves. Improvements in system performance by incremental upgrades of system components and software are essential. Improving calibration parameters and thereby progressively eliminating artifacts requires ongoing testing, research and development. To improve the system, we have formed an interdisciplinary team with common interests and diverse sources of support. We share expertise and leverage funding while effectively and

The severity of an earthquake

Science.gov (United States)

,

1997-01-01

The severity of an earthquake can be expressed in terms of both intensity and magnitude. However, the two terms are quite different, and they are often confused. Intensity is based on the observed effects of ground shaking on people, buildings, and natural features. It varies from place to place within the disturbed region depending on the location of the observer with respect to the earthquake epicenter. Magnitude is related to the amount of seismic energy released at the hypocenter of the earthquake. It is based on the amplitude of the earthquake waves recorded on instruments

Use of ground-based wind profiles in mesoscale forecasting

Science.gov (United States)

Schlatter, Thomas W.

1985-01-01

A brief review is presented of recent uses of ground-based wind profile data in mesoscale forecasting. Some of the applications are in real time, and some are after the fact. Not all of the work mentioned here has been published yet, but references are given wherever possible. As Gage and Balsley (1978) point out, sensitive Doppler radars have been used to examine tropospheric wind profiles since the 1970's. It was not until the early 1980's, however, that the potential contribution of these instruments to operational forecasting and numerical weather prediction became apparent. Profiler winds and radiosonde winds compare favorably, usually within a few m/s in speed and 10 degrees in direction (see Hogg et al., 1983), but the obvious advantage of the profiler is its frequent (hourly or more often) sampling of the same volume. The rawinsonde balloon is launched only twice a day and drifts with the wind. In this paper, I will: (1) mention two operational uses of data from a wind profiling system developed jointly by the Wave Propagation and Aeronomy Laboratories of NOAA; (2) describe a number of displays of these same data on a workstation for mesoscale forecasting developed by the Program for Regional Observing and Forecasting Services (PROFS); and (3) explain some interesting diagnostic calculations performed by meteorologists of the Wave Propagation Laboratory.

Earthquake potential revealed by tidal influence on earthquake size-frequency statistics

Science.gov (United States)

Ide, Satoshi; Yabe, Suguru; Tanaka, Yoshiyuki

2016-11-01

The possibility that tidal stress can trigger earthquakes is long debated. In particular, a clear causal relationship between small earthquakes and the phase of tidal stress is elusive. However, tectonic tremors deep within subduction zones are highly sensitive to tidal stress levels, with tremor rate increasing at an exponential rate with rising tidal stress. Thus, slow deformation and the possibility of earthquakes at subduction plate boundaries may be enhanced during periods of large tidal stress. Here we calculate the tidal stress history, and specifically the amplitude of tidal stress, on a fault plane in the two weeks before large earthquakes globally, based on data from the global, Japanese, and Californian earthquake catalogues. We find that very large earthquakes, including the 2004 Sumatran, 2010 Maule earthquake in Chile and the 2011 Tohoku-Oki earthquake in Japan, tend to occur near the time of maximum tidal stress amplitude. This tendency is not obvious for small earthquakes. However, we also find that the fraction of large earthquakes increases (the b-value of the Gutenberg-Richter relation decreases) as the amplitude of tidal shear stress increases. The relationship is also reasonable, considering the well-known relationship between stress and the b-value. This suggests that the probability of a tiny rock failure expanding to a gigantic rupture increases with increasing tidal stress levels. We conclude that large earthquakes are more probable during periods of high tidal stress.

Stress triggering of the Lushan M7. 0 earthquake by the Wenchuan Ms8. 0 earthquake

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Wu Jianchao

2013-08-01

Full Text Available The Wenchuan Ms8. 0 earthquake and the Lushan M7. 0 earthquake occurred in the north and south segments of the Longmenshan nappe tectonic belt, respectively. Based on the focal mechanism and finite fault model of the Wenchuan Ms8. 0 earthquake, we calculated the coulomb failure stress change. The inverted coulomb stress changes based on the Nishimura and Chenji models both show that the Lushan M7. 0 earthquake occurred in the increased area of coulomb failure stress induced by the Wenchuan Ms8. 0 earthquake. The coulomb failure stress increased by approximately 0. 135 – 0. 152 bar in the source of the Lushan M7. 0 earthquake, which is far more than the stress triggering threshold. Therefore, the Lushan M7. 0 earthquake was most likely triggered by the coulomb failure stress change.

Power Scaling of the Size Distribution of Economic Loss and Fatalities due to Hurricanes, Earthquakes, Tornadoes, and Floods in the USA

Science.gov (United States)

Tebbens, S. F.; Barton, C. C.; Scott, B. E.

2016-12-01

Traditionally, the size of natural disaster events such as hurricanes, earthquakes, tornadoes, and floods is measured in terms of wind speed (m/sec), energy released (ergs), or discharge (m3/sec) rather than by economic loss or fatalities. Economic loss and fatalities from natural disasters result from the intersection of the human infrastructure and population with the size of the natural event. This study investigates the size versus cumulative number distribution of individual natural disaster events for several disaster types in the United States. Economic losses are adjusted for inflation to 2014 USD. The cumulative number divided by the time over which the data ranges for each disaster type is the basis for making probabilistic forecasts in terms of the number of events greater than a given size per year and, its inverse, return time. Such forecasts are of interest to insurers/re-insurers, meteorologists, seismologists, government planners, and response agencies. Plots of size versus cumulative number distributions per year for economic loss and fatalities are well fit by power scaling functions of the form p(x) = Cx-β; where, p(x) is the cumulative number of events with size equal to and greater than size x, C is a constant, the activity level, x is the event size, and β is the scaling exponent. Economic loss and fatalities due to hurricanes, earthquakes, tornadoes, and floods are well fit by power functions over one to five orders of magnitude in size. Economic losses for hurricanes and tornadoes have greater scaling exponents, β = 1.1 and 0.9 respectively, whereas earthquakes and floods have smaller scaling exponents, β = 0.4 and 0.6 respectively. Fatalities for tornadoes and floods have greater scaling exponents, β = 1.5 and 1.7 respectively, whereas hurricanes and earthquakes have smaller scaling exponents, β = 0.4 and 0.7 respectively. The scaling exponents can be used to make probabilistic forecasts for time windows ranging from 1 to 1000 years

GIS BASED SYSTEM FOR POST-EARTHQUAKE CRISIS MANAGMENT USING CELLULAR NETWORK

OpenAIRE

Raeesi, M.; Sadeghi-Niaraki, A.

2013-01-01

Earthquakes are among the most destructive natural disasters. Earthquakes happen mainly near the edges of tectonic plates, but they may happen just about anywhere. Earthquakes cannot be predicted. Quick response after disasters, like earthquake, decreases loss of life and costs. Massive earthquakes often cause structures to collapse, trapping victims under dense rubble for long periods of time. After the earthquake and destroyed some areas, several teams are sent to find the location of the d...

Pulverization provides a mechanism for the nucleation of earthquakes at low stress on strong faults

Science.gov (United States)

Felzer, Karen R.

2014-01-01

An earthquake occurs when rock that has been deformed under stress rebounds elastically along a fault plane (Gilbert, 1884; Reid, 1911), radiating seismic waves through the surrounding earth. Rupture along the entire fault surface does not spontaneously occur at the same time, however. Rather the rupture starts in one tiny area, the rupture nucleation zone, and spreads sequentially along the fault. Like a row of dominoes, one bit of rebounding fault triggers the next. This triggering is understood to occur because of the large dynamic stresses at the tip of an active seismic rupture. The importance of these crack tip stresses is a central question in earthquake physics. The crack tip stresses are minimally important, for example, in the time predictable earthquake model (Shimazaki and Nakata, 1980), which holds that prior to rupture stresses are comparable to fault strength in many locations on the future rupture plane, with bits of variation. The stress/strength ratio is highest at some point, which is where the earthquake nucleates. This model does not require any special conditions or processes at the nucleation site; the whole fault is essentially ready for rupture at the same time. The fault tip stresses ensure that the rupture occurs as a single rapid earthquake, but the fact that fault tip stresses are high is not particularly relevant since the stress at most points does not need to be raised by much. Under this model it should technically be possible to forecast earthquakes based on the stress-renewaql concept, or estimates of when the fault as a whole will reach the critical stress level, a practice used in official hazard mapping (Field, 2008). This model also indicates that physical precursors may be present and detectable, since stresses are unusually high over a significant area before a large earthquake.

Cyclone track forecasting based on satellite images using artificial neural networks

OpenAIRE

Kovordanyi, Rita; Roy, Chandan

2009-01-01

Many places around the world are exposed to tropical cyclones and associated storm surges. In spite of massive efforts, a great number of people die each year as a result of cyclone events. To mitigate this damage, improved forecasting techniques must be developed. The technique presented here uses artificial neural networks to interpret NOAA-AVHRR satellite images. A multi-layer neural network, resembling the human visual system, was trained to forecast the movement of cyclones based on sate...

Daily Average Wind Power Interval Forecasts Based on an Optimal Adaptive-Network-Based Fuzzy Inference System and Singular Spectrum Analysis

Directory of Open Access Journals (Sweden)

Zhongrong Zhang

2016-01-01

Full Text Available Wind energy has increasingly played a vital role in mitigating conventional resource shortages. Nevertheless, the stochastic nature of wind poses a great challenge when attempting to find an accurate forecasting model for wind power. Therefore, precise wind power forecasts are of primary importance to solve operational, planning and economic problems in the growing wind power scenario. Previous research has focused efforts on the deterministic forecast of wind power values, but less attention has been paid to providing information about wind energy. Based on an optimal Adaptive-Network-Based Fuzzy Inference System (ANFIS and Singular Spectrum Analysis (SSA, this paper develops a hybrid uncertainty forecasting model, IFASF (Interval Forecast-ANFIS-SSA-Firefly Alogorithm, to obtain the upper and lower bounds of daily average wind power, which is beneficial for the practical operation of both the grid company and independent power producers. To strengthen the practical ability of this developed model, this paper presents a comparison between IFASF and other benchmarks, which provides a general reference for this aspect for statistical or artificially intelligent interval forecast methods. The comparison results show that the developed model outperforms eight benchmarks and has a satisfactory forecasting effectiveness in three different wind farms with two time horizons.

Natural Time, Nowcasting and the Physics of Earthquakes: Estimation of Seismic Risk to Global Megacities

Science.gov (United States)

Rundle, John B.; Luginbuhl, Molly; Giguere, Alexis; Turcotte, Donald L.

2018-02-01

Natural Time ("NT") refers to the concept of using small earthquake counts, for example of M > 3 events, to mark the intervals between large earthquakes, for example M > 6 events. The term was first used by Varotsos et al. (2005) and later by Holliday et al. (2006) in their studies of earthquakes. In this paper, we discuss ideas and applications arising from the use of NT to understand earthquake dynamics, in particular by use of the idea of nowcasting. Nowcasting differs from forecasting, in that the goal of nowcasting is to estimate the current state of the system, rather than the probability of a future event. Rather than focus on an individual earthquake faults, we focus on a defined local geographic region surrounding a particular location. This local region is considered to be embedded in a larger regional setting from which we accumulate the relevant statistics. We apply the nowcasting idea to the practical development of methods to estimate the current state of risk for dozens of the world's seismically exposed megacities, defined as cities having populations of over 1 million persons. We compute a ranking of these cities based on their current nowcast value, and discuss the advantages and limitations of this approach. We note explicitly that the nowcast method is not a model, in that there are no free parameters to be fit to data. Rather, the method is simply a presentation of statistical data, which the user can interpret. Among other results, we find, for example, that the current nowcast ranking of the Los Angeles region is comparable to its ranking just prior to the January 17, 1994 Northridge earthquake.

Surface rupturing earthquakes repeated in the 300 years along the ISTL active fault system, central Japan

Science.gov (United States)

Katsube, Aya; Kondo, Hisao; Kurosawa, Hideki

2017-06-01

Surface rupturing earthquakes produced by intraplate active faults generally have long recurrence intervals of a few thousands to tens of thousands of years. We here report the first evidence for an extremely short recurrence interval of 300 years for surface rupturing earthquakes on an intraplate system in Japan. The Kamishiro fault of the Itoigawa-Shizuoka Tectonic Line (ISTL) active fault system generated a Mw 6.2 earthquake in 2014. A paleoseismic trench excavation across the 2014 surface rupture showed the evidence for the 2014 event and two prior paleoearthquakes. The slip of the penultimate earthquake was similar to that of 2014 earthquake, and its timing was constrained to be after A.D. 1645. Judging from the timing, the damaged area, and the amount of slip, the penultimate earthquake most probably corresponds to a historical earthquake in A.D. 1714. The recurrence interval of the two most recent earthquakes is thus extremely short compared with intervals on other active faults known globally. Furthermore, the slip repetition during the last three earthquakes is in accordance with the time-predictable recurrence model rather than the characteristic earthquake model. In addition, the spatial extent of the 2014 surface rupture accords with the distribution of a serpentinite block, suggesting that the relatively low coefficient of friction may account for the unusually frequent earthquakes. These findings would affect long-term forecast of earthquake probability and seismic hazard assessment on active faults.

Earthquake acceleration amplification based on single microtremor test

Science.gov (United States)

Jaya Syahbana, Arifan; Kurniawan, Rahmat; Soebowo, Eko

2018-02-01

Understanding soil dynamics is needed to understand soil behaviour, including the parameters of earthquake acceleration amplification. Many researchers now conduct single microtremor tests to obtain amplification of velocity and natural periods of soil at test sites. However, these amplification parameters are rarely used, so a method is needed to convert the velocity amplification to acceleration amplification. This paper will discuss the proposed process of changing the value of amplification. The proposed method is to integrate the time histories of the synthetic earthquake acceleration of the soil surface under the deaggregation at that location so the time histories of the velocity earthquake will be obtained. Next is to conduct a “fitting curve” between amplification by a single microtremor test with amplification of the synthetic earthquake velocity time histories. After obtaining the fitting curve time histories of velocity, differentiation will be conducted to obtain fitting curve acceleration time histories. The final step after obtaining the fitting curve is to compare the acceleration of the “fitting curve” against the histories time of the acceleration of synthetic earthquake at bedrocks to obtain single microtremor acceleration amplification factor.

Connecting slow earthquakes to huge earthquakes.

Science.gov (United States)

Obara, Kazushige; Kato, Aitaro

2016-07-15

Slow earthquakes are characterized by a wide spectrum of fault slip behaviors and seismic radiation patterns that differ from those of traditional earthquakes. However, slow earthquakes and huge megathrust earthquakes can have common slip mechanisms and are located in neighboring regions of the seismogenic zone. The frequent occurrence of slow earthquakes may help to reveal the physics underlying megathrust events as useful analogs. Slow earthquakes may function as stress meters because of their high sensitivity to stress changes in the seismogenic zone. Episodic stress transfer to megathrust source faults leads to an increased probability of triggering huge earthquakes if the adjacent locked region is critically loaded. Careful and precise monitoring of slow earthquakes may provide new information on the likelihood of impending huge earthquakes. Copyright © 2016, American Association for the Advancement of Science.

GPS-based PWV for precipitation forecasting and its application to a typhoon event

Science.gov (United States)

Zhao, Qingzhi; Yao, Yibin; Yao, Wanqiang

2018-01-01

The temporal variability of precipitable water vapour (PWV) derived from Global Navigation Satellite System (GNSS) observations can be used to forecast precipitation events. A number of case studies of precipitation events have been analysed in Zhejiang Province, and a forecasting method for precipitation events was proposed. The PWV time series retrieved from the Global Positioning System (GPS) observations was processed by using a least-squares fitting method, so as to obtain the line tendency of ascents and descents over PWV. The increment of PWV for a short time (two to six hours) and PWV slope for a longer time (a few hours to more than ten hours) during the PWV ascending period are considered as predictive factors with which to forecast the precipitation event. The numerical results show that about 80%-90% of precipitation events and more than 90% of heavy rain events can be forecasted two to six hours in advance of the precipitation event based on the proposed method. 5-minute PWV data derived from GPS observations based on real-time precise point positioning (RT-PPP) were used for the typhoon event that passed over Zhejiang Province between 10 and 12 July, 2015. A good result was acquired using the proposed method and about 74% of precipitation events were predicted at some ten to thirty minutes earlier than their onset with a false alarm rate of 18%. This study shows that the GPS-based PWV was promising for short-term and now-casting precipitation forecasting.

PROPOSAL FOR IMPROVEMENT OF BUINESS CONTINUITY PLAN (BCP) BASED ON THE LESSONS OF THE GREAT EAST JAPAN EARTHQUAKE

Science.gov (United States)

Maruya, Hiroaki

For most Japanese companies and organizations, the enormous damage of the Great East Japan Earthquake was more than expected. In addition to great tsunami and earthquake motion, the lack of electricity and fuel disturbed to business activities seriously, and they should be considered important constraint factors in future earthquakes. Furthermore, disruption of supply chains also led considerable decline of production in many industries across Japan and foreign countries. Therefore it becomes urgent need for Japanese government and industries to utilize the lessons of the Great Earthquake and execute effective countermeasures, considering great earthquakes such as Tonankai & Nankai earthquakes and Tokyo Inland Earthquakes. Obviously most basic step is improving earthquake-resistant ability of buildings and facilities. In addition the spread of BCP and BCM to enterprises and organizations is indispensable. Based on the lessons, the BCM should include the point of view of the supply chain management more clearly, and emphasize "substitute strategy" more explicitly because a company should survive even if it completely loses its present production base. The central and local governments are requested, in addition to develop their own BCP, to improve related systematic conditions for BCM of the private sectors.

From Data-Sharing to Model-Sharing: SCEC and the Development of Earthquake System Science (Invited)

Science.gov (United States)

Jordan, T. H.

2009-12-01

Earthquake system science seeks to construct system-level models of earthquake phenomena and use them to predict emergent seismic behavior—an ambitious enterprise that requires high degree of interdisciplinary, multi-institutional collaboration. This presentation will explore model-sharing structures that have been successful in promoting earthquake system science within the Southern California Earthquake Center (SCEC). These include disciplinary working groups to aggregate data into community models; numerical-simulation working groups to investigate system-specific phenomena (process modeling) and further improve the data models (inverse modeling); and interdisciplinary working groups to synthesize predictive system-level models. SCEC has developed a cyberinfrastructure, called the Community Modeling Environment, that can distribute the community models; manage large suites of numerical simulations; vertically integrate the hardware, software, and wetware needed for system-level modeling; and promote the interactions among working groups needed for model validation and refinement. Various socio-scientific structures contribute to successful model-sharing. Two of the most important are “communities of trust” and collaborations between government and academic scientists on mission-oriented objectives. The latter include improvements of earthquake forecasts and seismic hazard models and the use of earthquake scenarios in promoting public awareness and disaster management.

Connecting slow earthquakes to huge earthquakes

OpenAIRE

Obara, Kazushige; Kato, Aitaro

2016-01-01

Slow earthquakes are characterized by a wide spectrum of fault slip behaviors and seismic radiation patterns that differ from those of traditional earthquakes. However, slow earthquakes and huge megathrust earthquakes can have common slip mechanisms and are located in neighboring regions of the seismogenic zone. The frequent occurrence of slow earthquakes may help to reveal the physics underlying megathrust events as useful analogs. Slow earthquakes may function as stress meters because of th...

Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

Science.gov (United States)

Pathak, Jaideep; Wikner, Alexander; Fussell, Rebeckah; Chandra, Sarthak; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

2018-04-01

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.

ENSO-based probabilistic forecasts of March-May U.S. tornado and hail activity

Science.gov (United States)

Lepore, Chiara; Tippett, Michael K.; Allen, John T.

2017-09-01

Extended logistic regression is used to predict March-May severe convective storm (SCS) activity based on the preceding December-February (DJF) El Niño-Southern Oscillation (ENSO) state. The spatially resolved probabilistic forecasts are verified against U.S. tornado counts, hail events, and two environmental indices for severe convection. The cross-validated skill is positive for roughly a quarter of the U.S. Overall, indices are predicted with more skill than are storm reports, and hail events are predicted with more skill than tornado counts. Skill is higher in the cool phase of ENSO (La Niña like) when overall SCS activity is higher. SCS forecasts based on the predicted DJF ENSO state from coupled dynamical models initialized in October of the previous year extend the lead time with only a modest reduction in skill compared to forecasts based on the observed DJF ENSO state.

Tradable Earthquake Certificates

NARCIS (Netherlands)

Woerdman, Edwin; Dulleman, Minne

2018-01-01

This article presents a market-based idea to compensate for earthquake damage caused by the extraction of natural gas and applies it to the case of Groningen in the Netherlands. Earthquake certificates give homeowners a right to yearly compensation for both property damage and degradation of living

Analysis of Seismotektonic Patterns in Sumatra Region Based on the Focal Mechanism of Earthquake Period 1976-2016

Science.gov (United States)

Indah, F. P.; Syafriani, S.; Andiyansyah, Z. S.

2018-04-01

Sumatra is in an active subduction zone between the indo-australian plate and the eurasian plate and is located at a fault along the sumatra fault so that sumatra is vulnerable to earthquakes. One of the ways to find out the cause of earthquake can be done by identifying the type of earthquake-causing faults based on earthquake of focal mechanism. The data used to identify the type of fault cause of earthquake is the earth tensor moment data which is sourced from global cmt period 1976-2016. The data used in this research using magnitude m ≥ 6 sr. This research uses gmt software (generic mapping tolls) to describe the form of fault. From the research result, it is found that the characteristics of fault field that formed in every region in sumatera island based on data processing and data of earthquake history of 1976-2016 period that the type of fault in sumatera fault is strike slip, fault type in mentawai fault is reverse fault (rising faults) and dip-slip, while the fault type in the subduction zone is dip-slip.

Fear based Education or Curiosity based Education as an Example of Earthquake and Natural Disaster Education: Results of Statistical Study in Primary Schools in Istanbul-Turkey

Science.gov (United States)

Ozcep, T.; Ozcep, F.

2012-04-01

Natural disaster reduction focuses on the urgent need for prevention activities to reduce loss of life, damage to property, infrastructure and environment, and the social and economic disruption caused by natural hazards. One of the most important factors in reduction of the potential damage of earthquakes is trained manpower. To understanding the causes of earthquakes and other natural phenomena (landslides, avalanches, floods, volcanoes, etc.) is one of the pre-conditions to show a conscious behavior. The aim of the study is to analysis and to investigate, how earthquakes and other natural phenomena are perceived by the students and the possible consequences of this perception, and their effects of reducing earthquake damage. One of the crucial questions is that is our education system fear or curiosity based education system? Effects of the damages due to earthquakes have led to look like a fear subject. In fact, due to the results of the effects, the earthquakes are perceived scary phenomena. In the first stage of the project, the learning (or perception) levels of earthquakes and other natural disasters for the students of primary school are investigated with a survey. Aim of this survey study of earthquakes and other natural phenomena is that have the students fear based or curiosity based approaching to the earthquakes and other natural events. In the second stage of the project, the path obtained by the survey are evaluated with the statistical point of approach. A questionnaire associated with earthquakes and natural disasters are applied to primary school students (that total number of them is approximately 700 pupils) to measure the curiosity and/or fear levels. The questionnaire consists of 17 questions related to natural disasters. The questions are: "What is the Earthquake ?", "What is power behind earthquake?", "What is the mental response during the earthquake ?", "Did we take lesson from earthquake's results ?", "Are you afraid of earthquake

Twitter earthquake detection: Earthquake monitoring in a social world

Science.gov (United States)

Earle, Paul S.; Bowden, Daniel C.; Guy, Michelle R.

2011-01-01

The U.S. Geological Survey (USGS) is investigating how the social networking site Twitter, a popular service for sending and receiving short, public text messages, can augment USGS earthquake response products and the delivery of hazard information. Rapid detection and qualitative assessment of shaking events are possible because people begin sending public Twitter messages (tweets) with in tens of seconds after feeling shaking. Here we present and evaluate an earthquake detection procedure that relies solely on Twitter data. A tweet-frequency time series constructed from tweets containing the word "earthquake" clearly shows large peaks correlated with the origin times of widely felt events. To identify possible earthquakes, we use a short-term-average, long-term-average algorithm. When tuned to a moderate sensitivity, the detector finds 48 globally-distributed earthquakes with only two false triggers in five months of data. The number of detections is small compared to the 5,175 earthquakes in the USGS global earthquake catalog for the same five-month time period, and no accurate location or magnitude can be assigned based on tweet data alone. However, Twitter earthquake detections are not without merit. The detections are generally caused by widely felt events that are of more immediate interest than those with no human impact. The detections are also fast; about 75% occur within two minutes of the origin time. This is considerably faster than seismographic detections in poorly instrumented regions of the world. The tweets triggering the detections also provided very short first-impression narratives from people who experienced the shaking.

Seismicity map tools for earthquake studies

Science.gov (United States)

Boucouvalas, Anthony; Kaskebes, Athanasios; Tselikas, Nikos

2014-05-01

We report on the development of new and online set of tools for use within Google Maps, for earthquake research. We demonstrate this server based and online platform (developped with PHP, Javascript, MySQL) with the new tools using a database system with earthquake data. The platform allows us to carry out statistical and deterministic analysis on earthquake data use of Google Maps and plot various seismicity graphs. The tool box has been extended to draw on the map line segments, multiple straight lines horizontally and vertically as well as multiple circles, including geodesic lines. The application is demonstrated using localized seismic data from the geographic region of Greece as well as other global earthquake data. The application also offers regional segmentation (NxN) which allows the studying earthquake clustering, and earthquake cluster shift within the segments in space. The platform offers many filters such for plotting selected magnitude ranges or time periods. The plotting facility allows statistically based plots such as cumulative earthquake magnitude plots and earthquake magnitude histograms, calculation of 'b' etc. What is novel for the platform is the additional deterministic tools. Using the newly developed horizontal and vertical line and circle tools we have studied the spatial distribution trends of many earthquakes and we here show for the first time the link between Fibonacci Numbers and spatiotemporal location of some earthquakes. The new tools are valuable for examining visualizing trends in earthquake research as it allows calculation of statistics as well as deterministic precursors. We plan to show many new results based on our newly developed platform.

Hybrid Forecasting Approach Based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting

Directory of Open Access Journals (Sweden)

Weide Li

2017-01-01

Full Text Available Accurate electric power demand forecasting plays a key role in electricity markets and power systems. The electric power demand is usually a non-linear problem due to various unknown reasons, which make it difficult to get accurate prediction by traditional methods. The purpose of this paper is to propose a novel hybrid forecasting method for managing and scheduling the electricity power. EEMD-SCGRNN-PSVR, the proposed new method, combines ensemble empirical mode decomposition (EEMD, seasonal adjustment (S, cross validation (C, general regression neural network (GRNN and support vector regression machine optimized by the particle swarm optimization algorithm (PSVR. The main idea of EEMD-SCGRNN-PSVR is respectively to forecast waveform and trend component that hidden in demand series to substitute directly forecasting original electric demand. EEMD-SCGRNN-PSVR is used to predict the one week ahead half-hour’s electricity demand in two data sets (New South Wales (NSW and Victorian State (VIC in Australia. Experimental results show that the new hybrid model outperforms the other three models in terms of forecasting accuracy and model robustness.

Microcontroller-based network for meteorological sensing and weather forecast calculations

Directory of Open Access Journals (Sweden)

A. Vas

2012-06-01

Full Text Available Weather forecasting needs a lot of computing power. It is generally accomplished by using supercomputers which are expensive to rent and to maintain. In addition, weather services also have to maintain radars, balloons and pay for worldwide weather data measured by stations and satellites. Weather forecasting computations usually consist of solving differential equations based on the measured parameters. To do that, the computer uses the data of close and distant neighbor points. Accordingly, if small-sized weather stations, which are capable of making measurements, calculations and communication, are connected through the Internet, then they can be used to run weather forecasting calculations like a supercomputer does. It doesn’t need any central server to achieve this, because this network operates as a distributed system. We chose Microchip’s PIC18 microcontroller (μC platform in the implementation of the hardware, and the embedded software uses the TCP/IP Stack v5.41 provided by Microchip.

A new cascade NN based method to short-term load forecast in deregulated electricity market

International Nuclear Information System (INIS)

Kouhi, Sajjad; Keynia, Farshid

2013-01-01

Highlights: • We are proposed a new hybrid cascaded NN based method and WT to short-term load forecast in deregulated electricity market. • An efficient preprocessor consist of normalization and shuffling of signals is presented. • In order to select the best inputs, a two-stage feature selection is presented. • A new cascaded structure consist of three cascaded NNs is used as forecaster. - Abstract: Short-term load forecasting (STLF) is a major discussion in efficient operation of power systems. The electricity load is a nonlinear signal with time dependent behavior. The area of electricity load forecasting has still essential need for more accurate and stable load forecast algorithm. To improve the accuracy of prediction, a new hybrid forecast strategy based on cascaded neural network is proposed for STLF. This method is consists of wavelet transform, an intelligent two-stage feature selection, and cascaded neural network. The feature selection is used to remove the irrelevant and redundant inputs. The forecast engine is composed of three cascaded neural network (CNN) structure. This cascaded structure can be efficiently extract input/output mapping function of the nonlinear electricity load data. Adjustable parameters of the intelligent feature selection and CNN is fine-tuned by a kind of cross-validation technique. The proposed STLF is tested on PJM and New York electricity markets. It is concluded from the result, the proposed algorithm is a robust forecast method

Baseline and Target Values for PV Forecasts: Toward Improved Solar Power Forecasting: Preprint

Energy Technology Data Exchange (ETDEWEB)

Zhang, Jie; Hodge, Bri-Mathias; Lu, Siyuan; Hamann, Hendrik F.; Lehman, Brad; Simmons, Joseph; Campos, Edwin; Banunarayanan, Venkat

2015-08-05

Accurate solar power forecasting allows utilities to get the most out of the solar resources on their systems. To truly measure the improvements that any new solar forecasting methods can provide, it is important to first develop (or determine) baseline and target solar forecasting at different spatial and temporal scales. This paper aims to develop baseline and target values for solar forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output. forecasting metrics. These were informed by close collaboration with utility and independent system operator partners. The baseline values are established based on state-of-the-art numerical weather prediction models and persistence models. The target values are determined based on the reduction in the amount of reserves that must be held to accommodate the uncertainty of solar power output.

Automatic generation of smart earthquake-resistant building system: Hybrid system of base-isolation and building-connection

Directory of Open Access Journals (Sweden)

M. Kasagi

2016-02-01

Full Text Available A base-isolated building may sometimes exhibit an undesirable large response to a long-duration, long-period earthquake ground motion and a connected building system without base-isolation may show a large response to a near-fault (rather high-frequency earthquake ground motion. To overcome both deficiencies, a new hybrid control system of base-isolation and building-connection is proposed and investigated. In this new hybrid building system, a base-isolated building is connected to a stiffer free wall with oil dampers. It has been demonstrated in a preliminary research that the proposed hybrid system is effective both for near-fault (rather high-frequency and long-duration, long-period earthquake ground motions and has sufficient redundancy and robustness for a broad range of earthquake ground motions.An automatic generation algorithm of this kind of smart structures of base-isolation and building-connection hybrid systems is presented in this paper. It is shown that, while the proposed algorithm does not work well in a building without the connecting-damper system, it works well in the proposed smart hybrid system with the connecting damper system.

Development of damage probability matrices based on Greek earthquake damage data

Science.gov (United States)