time series analysis in seismology

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Time Series Analysis in Seismology
Author : Alejandro Ramírez-Rojas,Leonardo Di G. Sigalotti,Elsa Leticia Flores Márquez,Otto Rendón
Publisher : Elsevier
Release Date : 2019-08-02
ISBN 10 : 0128149027
Pages : 404 pages
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Time Series Analysis in Seismology: Practical Applications provides technical assistance and coverage of available methods to professionals working in the field of seismology. Beginning with a thorough review of open problems in geophysics, including tectonic plate dynamics, localization of solitons, and forecasting, the book goes on to describe the various types of time series or punctual processes obtained from those systems. Additionally, the book describes a variety of methods and techniques relating to seismology and includes a discussion of future developments and improvements. Time Series Analysis in Seismology offers a concise presentation of the most recent advances in the analysis of geophysical data, particularly with regard to seismology, making it a valuable tool for researchers and students working in seismology and geophysics. Presents the necessary tools for time series analysis as it relates to seismology in a compact and consistent manner Includes a discussion of technical resources that can be applied to time series data analysis across multiple disciplines Describes the methods and techniques available for solving problems related to the analysis of complex data sets Provides exercises at the end of each chapter to enhance comprehension

Complexity of Seismic Time Series
Author : Tamaz Chelidze,Filippos Vallianatos,Luciano Telesca
Publisher : Elsevier
Release Date : 2018-05-21
ISBN 10 : 012813139X
Pages : 546 pages
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Complexity of Seismic Time Series: Measurement and Application applies the tools of nonlinear dynamics to seismic analysis, allowing for the revelation of new details in micro-seismicity, new perspectives in seismic noise, and new tools for prediction of seismic events. The book summarizes both advances and applications in the field, thus meeting the needs of both fundamental and practical seismology. Merging the needs of the classical field and the very modern terms of complexity science, this book covers theory and its application to advanced nonlinear time series tools to investigate Earth’s vibrations, making it a valuable tool for seismologists, hazard managers and engineers. Covers the topic of Earth’s vibrations involving many different aspects of theoretical and observational seismology Identifies applications of advanced nonlinear time series tools for the characterization of these Earth’s signals Merges the needs of geophysics with the applications of complexity theory Describes different methodologies to analyze problems, not only in the context of geosciences, but also those associated with different complex systems across disciplines

Time Series Analysis and Inverse Theory for Geophysicists
Author : David Gubbins
Publisher : Cambridge University Press
Release Date : 2004-03-18
ISBN 10 : 9780521525695
Pages : 255 pages
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The first textbook covering spectral analysis and geophysical data inversion for undergraduate and graduate students.

Time Series Analysis and Applications to Geophysical Systems
Author : David Brillinger,Enders Anthony Robinson,Frederic Paik Schoenberg
Publisher : Springer Science & Business Media
Release Date : 2012-12-06
ISBN 10 : 1468493868
Pages : 260 pages
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This IMA Volume in Mathematics and its Applications TIME SERIES ANALYSIS AND APPLICATIONS TO GEOPHYSICAL SYSTEMS contains papers presented at a very successful workshop on the same title. The event which was held on November 12-15, 2001 was an integral part of the IMA 2001-2002 annual program on " Mathematics in the Geosciences. " We would like to thank David R. Brillinger (Department of Statistics, Uni versity of California, Berkeley), Enders Anthony Robinson (Department of Earth and Environmental Engineering, Columbia University), and Fred eric Paik Schoenberg (Department of Statistics, University of California, Los Angeles) for their superb role as workshop organizers and editors of the proceedings. We are also grateful to Robert H. Shumway (Department of Statistics, University of California, Davis) for his help in organizing the four-day event. We take this opportunity to thank the National Science Foundation for its support of the IMA. Series Editors Douglas N. Arnold, Director of the IMA Fadil Santosa, Deputy Director of the IMA v PREFACE This volume contains a collection of papers that were presented dur ing the Workshop on Time Series Analysis and Applications to Geophysical Systems at the Institute for Mathematics and its Applications (IMA) at the University of Minnesota from November 12-15, 2001. This was part of the IMA Thematic Year on Mathematics in the Geosciences, and was the last in a series of four Workshops during the Fall Quarter dedicated to Dynamical Systems and Ergodic Theory.

Time Sequence Analysis in Geophysics: Third Edition
Author : E. R. Kanasewich
Publisher : University of Alberta
Release Date : 1981
ISBN 10 : 9780888640741
Pages : 480 pages
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Time sequence analysis is the study of relations between a sequence of data points or sequence of signals in order to determine the physical properties of the earth. Providing an up-to-date treatment on time series and time sequence, this book is intended for senior or graduate students in seismology, geomagnetism and exploratory geophysics.

Government-wide Index to Federal Research & Development Reports
Author : N.A
Publisher : N.A
Release Date : 1967-10
ISBN 10 :
Pages : 329 pages
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Time Sequence Analysis in Geophysics
Author : E. R. Kanasewich
Publisher : N.A
Release Date : 1974
ISBN 10 :
Pages : 352 pages
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Time sequence analysis is the study of relations between a sequence of data points or sequence of signals in order to determine the physical properties of the earth. Providing an up-to-date treatment on time series and time sequence, this book is intended for senior or graduate students in seismology, geomagnetism and exploratory geophysics.

Natural Time Analysis: The New View of Time
Author : Panayiotis Varotsos,Nicholas V. Sarlis,Efthimios S. Skordas
Publisher : Springer Science & Business Media
Release Date : 2011-08-14
ISBN 10 : 3642164498
Pages : 452 pages
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This book deals with the theory and the applications of a new time domain, termed natural time domain, that has been forwarded by the authors almost a decade ago (P.A. Varotsos, N.V. Sarlis and E.S. Skordas, Practica of Athens Academy 76, 294-321, 2001; Physical Review E 66, 011902, 2002). In particular, it has been found that novel dynamical features hidden behind time series in complex systems can emerge upon analyzing them in this new time domain, which conforms to the desire to reduce uncertainty and extract signal information as much as possible. The analysis in natural time enables the study of the dynamical evolution of a complex system and identifies when the system enters a critical stage. Hence, natural time plays a key role in predicting impending catastrophic events in general. Relevant examples of data analysis in this new time domain have been published during the last decade in a large variety of fields, e.g., Earth Sciences, Biology and Physics. The book explains in detail a series of such examples including the identification of the sudden cardiac death risk in Cardiology, the recognition of electric signals that precede earthquakes, the determination of the time of an impending major mainshock in Seismology, and the analysis of the avalanches of the penetration of magnetic flux into thin films of type II superconductors in Condensed Matter Physics. In general, this book is concerned with the time-series analysis of signals emitted from complex systems by means of the new time domain and provides advanced students and research workers in diverse fields with a sound grounding in the fundamentals of current research work on detecting (long-range) correlations in complex time series. Furthermore, the modern techniques of Statistical Physics in time series analysis, for example Hurst analysis, the detrended fluctuation analysis, the wavelet transform etc., are presented along with their advantages when natural time domain is employed.

Nonlinear Time Series Analysis in the Geosciences
Author : Reik V. Donner,Susana M. Barbosa
Publisher : Springer Science & Business Media
Release Date : 2008-08-18
ISBN 10 : 3540789375
Pages : 390 pages
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The understanding of dynamical processes in the complex system “Earth” requires the appropriate analysis of a large amount of data from observations and/or model simulations. In this volume, modern nonlinear approaches are introduced and used to study specifiic questions relevant to present-day geoscience. The approaches include spatio-temporal methods, time-frequency analysis, dimension analysis (in particular, for multivariate data), nonlinear statistical decomposition, methods designed for treating data with uneven sampling or missing values, nonlinear correlation and synchronization analysis, surrogate data techniques, network approaches, and nonlinear methods of noise reduction. This book aims to present a collection of state-of-the-art scientific contributions used in current studies by some of the world's leading scientists in this field.

An Introduction to Seismology, Earthquakes, and Earth Structure
Author : Seth Stein,Michael Wysession
Publisher : John Wiley & Sons
Release Date : 2013-05-30
ISBN 10 : 1118687450
Pages : 512 pages
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An Introduction to Seismology, Earthquakes and Earth Structures is an introduction to seismology and its role in the earth sciences, and is written for advanced undergraduate and beginning graduate students. The fundamentals of seismic wave propagation are developed using a physical approach and then applied to show how refraction, reflection, and teleseismic techniques are used to study the structure and thus the composition and evolution of the earth. The book shows how seismic waves are used to study earthquakes and are integrated with other data to investigate the plate tectonic processes that cause earthquakes. Figures, examples, problems, and computer exercises teach students about seismology in a creative and intuitive manner. Necessary mathematical tools including vector and tensor analysis, matrix algebra, Fourier analysis, statistics of errors, signal processing, and data inversion are introduced with many relevant examples. The text also addresses the fundamentals of seismometry and applications of seismology to societal issues. Special attention is paid to help students visualize connections between different topics and view seismology as an integrated science. An Introduction to Seismology, Earthquakes, and Earth Structure gives an excellent overview for students of geophysics and tectonics, and provides a strong foundation for further studies in seismology. Multidisciplinary examples throughout the text - catering to students in varied disciplines (geology, mineralogy, petrology, physics, etc.). Most up to date book on the market - includes recent seismic events such as the 1999 Earthquakes in Turkey, Greece, and Taiwan). Chapter outlines - each chapter begins with an outline and a list of learning objectives to help students focus and study. Essential math review - an entire section reviews the essential math needed to understand seismology. This can be covered in class or left to students to review as needed. End of chapter problem sets - homework problems that cover the material presented in the chapter. Solutions to all odd numbered problem sets are listed in the back so that students can track their progress. Extensive References - classic references and more current references are listed at the end of each chapter. A set of instructor's resources containing downloadable versions of all the figures in the book, errata and answers to homework problems is available at: http://levee.wustl.edu/seismology/book/. Also available on this website are PowerPoint lecture slides corresponding to the first 5 chapters of the book.

Time Series with Applications to Seismology
Author : G. R. Dargahi-Noubary
Publisher : Nova Science Pub Incorporated
Release Date : 1999
ISBN 10 :
Pages : 240 pages
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In this book the author will present several approaches for modelling and identification of seismic records based on formulations for source and transmission path. This will demonstrate an approach based on a criterion other that statistical goodness of fit. They will provide reasons for the success of the models and methods that are used frequently. This will provide a criterion to decide which models are good for all data sets, not just for the data at hand. Moreover, we will explain the relationships that exist between certain geophysical variables (e.g. corner frequency) and statistical quantities (e.g. zero-crossing) and use that to support our modelling and identification approaches.

Time Series Modeling of Neuroscience Data
Author : Tohru Ozaki
Publisher : CRC Press
Release Date : 2012-01-26
ISBN 10 : 1420094610
Pages : 574 pages
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Recent advances in brain science measurement technology have given researchers access to very large-scale time series data such as EEG/MEG data (20 to 100 dimensional) and fMRI (140,000 dimensional) data. To analyze such massive data, efficient computational and statistical methods are required. Time Series Modeling of Neuroscience Data shows how to efficiently analyze neuroscience data by the Wiener-Kalman-Akaike approach, in which dynamic models of all kinds, such as linear/nonlinear differential equation models and time series models, are used for whitening the temporally dependent time series in the framework of linear/nonlinear state space models. Using as little mathematics as possible, this book explores some of its basic concepts and their derivatives as useful tools for time series analysis. Unique features include: A statistical identification method of highly nonlinear dynamical systems such as the Hodgkin-Huxley model, Lorenz chaos model, Zetterberg Model, and more Methods and applications for Dynamic Causality Analysis developed by Wiener, Granger, and Akaike A state space modeling method for dynamicization of solutions for the Inverse Problems A heteroscedastic state space modeling method for dynamic non-stationary signal decomposition for applications to signal detection problems in EEG data analysis An innovation-based method for the characterization of nonlinear and/or non-Gaussian time series An innovation-based method for spatial time series modeling for fMRI data analysis The main point of interest in this book is to show that the same data can be treated using both a dynamical system and time series approach so that the neural and physiological information can be extracted more efficiently. Of course, time series modeling is valid not only in neuroscience data analysis but also in many other sciences and engineering fields where the statistical inference from the observed time series data plays an important role.