Time Series Analysis in Seismology

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: 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 methods are essential tools in the analysis of many geophysical systems. This volume, which consists of papers presented by a select, international group of statistical and geophysical experts at a 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 as part of the IMA's Thematic Year on Mathematics in the Geosciences, explores the application of recent advances in time series methodology to a host of important problems ranging from climate change to seismology. The works in the volume deal with theoretical and methodological issues as well as real geophysical applications, and are written with both statistical and geophysical audiences in mind. Important contributions to time series modeling, estimation, prediction, and deconvolution are presented. The results are applied to a wide range of geophysical applications including the investigation and prediction of climatic variations, the interpretation of seismic signals, the estimation of flooding risk, the description of permeability in Chinese oil fields, and the modeling of NOx decomposition from thermal power plants.

The first textbook covering spectral analysis and geophysical data inversion for undergraduate and graduate students.

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.

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.

Time-lapse (4D) seismic technology is a key enabler for improved hydrocarbon recovery and more cost-effective field operations. Practical Applications of Time-lapse Seismic Data (SEG Distinguished Instructor Series No. 16) shows how 4D seismic data are used for reservoir surveillance, how they provide valuable insight on dynamic reservoir properties such as fluid saturation, pressure, and temperature, and how they add value to reservoir management. The material, based on the 2013 SEG Distinguished Instructor Short Course, includes discussions of reservoir-engineering concepts and rock physics critical to the understanding of 4D data, along with topics in 4D seismic acquisition and processing. A primary focus of the book is interpretation and data integration. Case-study examples are used to demonstrate key concepts and are drawn on to demonstrate the range of interpretation methods currently employed by industry and the diversity of geologic settings and production scenarios in which 4D is making a difference. Time-lapse seismic interpretation is inherently integrative, drawing on geophysical, geologic, and reservoir-engineering data and concepts. As a result, this book should be of interest to individuals from all subsurface disciplines.

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.

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.

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.

Increasingly environmental scientists, palaeoceanographers and geologists are collecting quantitative records of environmental changes (time-series) from sediments, ice cores, cave calcite, corals and trees. This book explains how to analyse these records, using straightforward explanations and diagrams rather than formal mathematical derivations. All the main cyclostratigraphic methods are covered including spectral analysis, cross-spectral analysis, filtering, complex demodulation, wavelet and singular spectrum analysis. Practical problems of time-series analysis, including those of distortions of environmental signals during stratigraphic encoding, are considered in detail. Recent research into various types of tidal and climatic cycles is summarised. The book ends with an extensive reference section, and an appendix listing sources of computer algorithms. This book provides the ideal reference for all those using time-series analysis to study the nature and history of climatic and tidal cycles. It is suitable for senior undergraduate and graduate courses in environmental science, palaeoceanography and geology.

The step from one to higher dimensional signal processing - case histories; High resolution 2 - dimensional power spectral estimation; Time series analysis of geophysical inverse scattering problems; On the use of the fourier transforms to image seismic reflection data; Estimating the earth's impedence function when there is noise in the electric and magnetic signals; Nearest neighbor rule classification of stationary and nonstationary time series; Time Domain considerations in optimal estimation for bandlimited signals; Lattice methods in spectral estimation; Geometrical and lattice version of levinson's general algorithm; Order selection for lowpass IIR filters; Application of S-Arrays to seasonal data; Time series model identification and prediction variance horizons; Recursive estimation and adaptive forecasting in ARIMA models with time varying coefficients; A time series case study from biology: some interplay between theory and practice; On TIMSAC-78.

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.

Good,No Highlights,No Markup,all pages are intact, Slight Shelfwear,may have the corners slightly dented, may have slight color changes/slightly damaged spine.

A collection of applied papers on time series, appearing here for the first time in English. The applications are primarily found in engineering and the physical sciences.

This book provides an essential appraisal of the recent advances in technologies, mathematical models and computational software used by those working with geodetic data. It explains the latest methods in processing and analyzing geodetic time series data from various space missions (i.e. GNSS, GRACE) and other technologies (i.e. tide gauges), using the most recent mathematical models. The book provides practical examples of how to apply these models to estimate seal level rise as well as rapid and evolving land motion changes due to gravity (ice sheet loss) and earthquakes respectively. It also provides a necessary overview of geodetic software and where to obtain them.

Following the breakthrough in the last decade in identifying the key parameters for time and depth imaging in anisotropic media and developing practical methodologies for estimating them from seismic data, Seismic Signatures and Analysis of Reflection Data in Anisotropic Media primarily focuses on the far reaching exploration benefits of anisotropic processing. This volume provides the first comprehensive description of reflection seismic signatures and processing methods in anisotropic media. It identifies the key parameters for time and depth imaging in transversely isotropic media and describes practical methodologies for estimating them from seismic data. Also, it contains a thorough discussion of the important issues of uniqueness and stability of seismic velocity analysis in the presence of anisotropy. The book contains a complete description of anisotropic imaging methods, from the theoretical background to algorithms to implementation issues. Numerous applications to synthetic and field data illustrate the improvements achieved by the anisotropic processing and the possibility of using the estimated anisotropic parameters in lithology discrimination. Focuses on the far reaching exploration benefits of anisotropic processing First comprehensive description of reflection seismic signatures and processing methods in anisotropic media