Fault-Zone Guided Wave, Ground Motion, Landslide and Earthquake Forecast
Fault-Zone Guided Wave, Ground Motion, Landslide and Earthquake Forecast

Author: Yong-Gang Li

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2018-03-19

Total Pages: 242

ISBN-13: 3110543559

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The book covers multi-disciplinary topics in observational, computational and applied geophysics in aspects of solid earth system. The authors provide an up-to-date overview for methods and techniques in seismology, with a focus on fault structure, strong ground motion and earthquake forecast based on full-3D earth structure models. Abundant of case studies make it a practical reference for researchers in seismology and applied geophysics.

Earthquake and Disaster Risk: Decade Retrospective of the Wenchuan Earthquake
Earthquake and Disaster Risk: Decade Retrospective of the Wenchuan Earthquake

Author: Yong-Gang Li

Publisher: Springer

Published: 2019-05-04

Total Pages: 259

ISBN-13: 9811380155

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This book presents review papers and research articles focusing on the 2008 Wenchuan earthquake in Sichuan, China, discussing cross-disciplinary and multiple thematic aspects of modern seismological, geophysical, geological and stochastic methodology and technology. Resulting from international and regional earthquake research and disaster mitigation collaborations, and written by international authors from multiple institutions and disciplines, it describes methods and techniques in earthquake science based on investigations of the Wenchuan earthquake. It also includes extensive reference lists to aid further research. The book helps both senior researchers and graduate students in earthquake science to broaden their horizons in data analysis, numerical modeling and structural retrieval for the tectonic, geological, geophysical and mechanical interpretation of the 2008 M8 Wenchuan earthquake to support a global and regional cooperation for preparedness, and the mitigation and management of seismic risk.

Statistical Methods and Modeling of Seismogenesis
Statistical Methods and Modeling of Seismogenesis

Author: Nikolaos Limnios

Publisher: John Wiley & Sons

Published: 2021-05-25

Total Pages: 338

ISBN-13: 1789450373

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The study of earthquakes is a multidisciplinary field, an amalgam of geodynamics, mathematics, engineering and more. The overriding commonality between them all is the presence of natural randomness. Stochastic studies (probability, stochastic processes and statistics) can be of different types, for example, the black box approach (one state), the white box approach (multi-state), the simulation of different aspects, and so on. This book has the advantage of bringing together a group of international authors, known for their earthquake-specific approaches, to cover a wide array of these myriad aspects. A variety of topics are presented, including statistical nonparametric and parametric methods, a multi-state system approach, earthquake simulators, post-seismic activity models, time series Markov models with regression, scaling properties and multifractal approaches, selfcorrecting models, the linked stress release model, Markovian arrival models, Poisson-based detection techniques, change point detection techniques on seismicity models, and, finally, semi-Markov models for earthquake forecasting.

Earthquake Occurrence
Earthquake Occurrence

Author: Rodolfo Console

Publisher: John Wiley & Sons

Published: 2017-07-17

Total Pages: 141

ISBN-13: 1119372224

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Earthquake Occurrence provides the reader with a review of algorithms applicable for modeling seismicity, such as short-term earthquake clustering and pseudo-periodic long-term behavior of major earthquakes. The concept of the likelihood ratio of a set of observations under different hypotheses is applied for comparison among various models. In short-term models, known by the term ETAS, the occurrence space and time rate density of earthquakes is modeled as the sum of two terms, one representing the independent or spontaneous events, and the other representing the activity triggered by previous earthquakes. Examples of the application of such algorithms in real cases are also reported. Dealing with long-term recurrence models, renewal time-dependent models, implying a pseudo-periodicity of earthquake occurrence, are compared with the simple time-independent Poisson model, in which every event occurs regardless of what has occurred in the past. The book also introduces a number of computer codes developed by the authors over decades of seismological research.

Advances in Assessment and Modeling of Earthquake Loss
Advances in Assessment and Modeling of Earthquake Loss

Author: Sinan Akkar

Publisher: Springer Nature

Published: 2021-06-02

Total Pages: 315

ISBN-13: 3030688135

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This open access book originates from an international workshop organized by Turkish Natural Catastrophe Insurance Pool (TCIP) in November 2019 that gathered renown researchers from academia, representatives of leading international reinsurance and modeling companies as well as government agencies responsible of insurance pricing in Turkey. The book includes chapters related to post-earthquake damage assessment, the state-of-art and novel earthquake loss modeling, their implementation and implication in insurance pricing at national, regional and global levels, and the role of earthquake insurance in building resilient societies and fire following earthquakes. The rich context encompassed in the book makes it a valuable tool not only for professionals and researchers dealing with earthquake loss modeling but also for practitioners in the insurance and reinsurance industry.

Statistical Methods for Trend Detection and Analysis in the Environmental Sciences
Statistical Methods for Trend Detection and Analysis in the Environmental Sciences

Author: Richard Chandler

Publisher: John Wiley & Sons

Published: 2011-03-25

Total Pages: 348

ISBN-13: 111999196X

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The need to understand and quantify change is fundamental throughout the environmental sciences. This might involve describing past variation, understanding the mechanisms underlying observed changes, making projections of possible future change, or monitoring the effect of intervening in some environmental system. This book provides an overview of modern statistical techniques that may be relevant in problems of this nature. Practitioners studying environmental change will be familiar with many classical statistical procedures for the detection and estimation of trends. However, the ever increasing capacity to collect and process vast amounts of environmental information has led to growing awareness that such procedures are limited in the insights that they can deliver. At the same time, significant developments in statistical methodology have often been widely dispersed in the statistical literature and have therefore received limited exposure in the environmental science community. This book aims to provide a thorough but accessible review of these developments. It is split into two parts: the first provides an introduction to this area and the second part presents a collection of case studies illustrating the practical application of modern statistical approaches to the analysis of trends in real studies. Key Features: Presents a thorough introduction to the practical application and methodology of trend analysis in environmental science. Explores non-parametric estimation and testing as well as parametric techniques. Methods are illustrated using case studies from a variety of environmental application areas. Looks at trends in all aspects of a process including mean, percentiles and extremes. Supported by an accompanying website featuring datasets and R code. The book is designed to be accessible to readers with some basic statistical training, but also contains sufficient detail to serve as a reference for practising statisticians. It will therefore be of use to postgraduate students and researchers both in the environmental sciences and in statistics.

Bayesian Inference for Stochastic Processes
Bayesian Inference for Stochastic Processes

Author: Lyle D. Broemeling

Publisher: CRC Press

Published: 2017-12-12

Total Pages: 432

ISBN-13: 1315303582

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This is the first book designed to introduce Bayesian inference procedures for stochastic processes. There are clear advantages to the Bayesian approach (including the optimal use of prior information). Initially, the book begins with a brief review of Bayesian inference and uses many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete state space and continuous time and continuous state space. The elements necessary to understanding stochastic processes are then introduced, followed by chapters devoted to the Bayesian analysis of such processes. It is important that a chapter devoted to the fundamental concepts in stochastic processes is included. Bayesian inference (estimation, testing hypotheses, and prediction) for discrete time Markov chains, for Markov jump processes, for normal processes (e.g. Brownian motion and the Ornstein–Uhlenbeck process), for traditional time series, and, lastly, for point and spatial processes are described in detail. Heavy emphasis is placed on many examples taken from biology and other scientific disciplines. In order analyses of stochastic processes, it will use R and WinBUGS. Features: Uses the Bayesian approach to make statistical Inferences about stochastic processes The R package is used to simulate realizations from different types of processes Based on realizations from stochastic processes, the WinBUGS package will provide the Bayesian analysis (estimation, testing hypotheses, and prediction) for the unknown parameters of stochastic processes To illustrate the Bayesian inference, many examples taken from biology, economics, and astronomy will reinforce the basic concepts of the subject A practical approach is implemented by considering realistic examples of interest to the scientific community WinBUGS and R code are provided in the text, allowing the reader to easily verify the results of the inferential procedures found in the many examples of the book Readers with a good background in two areas, probability theory and statistical inference, should be able to master the essential ideas of this book.

Statistical Methods and Modeling of Seismogenesis
Statistical Methods and Modeling of Seismogenesis

Author: Nikolaos Limnios

Publisher: John Wiley & Sons

Published: 2021-04-27

Total Pages: 336

ISBN-13: 1119825040

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The study of earthquakes is a multidisciplinary field, an amalgam of geodynamics, mathematics, engineering and more. The overriding commonality between them all is the presence of natural randomness. Stochastic studies (probability, stochastic processes and statistics) can be of different types, for example, the black box approach (one state), the white box approach (multi-state), the simulation of different aspects, and so on. This book has the advantage of bringing together a group of international authors, known for their earthquake-specific approaches, to cover a wide array of these myriad aspects. A variety of topics are presented, including statistical nonparametric and parametric methods, a multi-state system approach, earthquake simulators, post-seismic activity models, time series Markov models with regression, scaling properties and multifractal approaches, selfcorrecting models, the linked stress release model, Markovian arrival models, Poisson-based detection techniques, change point detection techniques on seismicity models, and, finally, semi-Markov models for earthquake forecasting.

Stochastic Processes for Spatial Econometrics
Stochastic Processes for Spatial Econometrics

Author: Jorge Mateu Mahiques

Publisher: Netbiblo

Published: 2010

Total Pages: 90

ISBN-13: 849745412X

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This monograph presents a general methodology which is shown to be valid in the analysis of spatial point structures and that is certainly easier to use by non-expert researchers coming from other applied sciences than other much modern techniques. We suggest that the local conditioning approach has the advantage that it is statistically efficient, easy to correct for edge-effects and provides similar results than other (more complicated) likelihood-based methods. We show a mathematical justification to prove that any purely inhibitory pairwise interaction point process (pipp) can be obtained as the limit of a sequence of auto-Poisson lattice schemes and within this context we develop the pseudolikelihood estimating equations. We particularly focus on developing a Monte Carlo simulation study to analyze the behaviour of the parameter s of a particular pipp model derived using this technique. We also stress that this methodology has a wide range of applications in many fields, particularly in economy and demography.