This textbook contains a consideration of the wide field of problems connected with statistical methods of processing of observed data, with the main examples and considered models related to geophysics and seismic exploration. This textbook will be particularly helpful to students and professionals from various fields of physics, connected with an estimation of the parameters of the physical objects by experimental data. The reader can also find many important topics, which are the basis for statistical methods of estimation and inverse problem solutions.
Data Analysis Methods in Physical Oceanography is a practical reference guide to established and modern data analysis techniques in earth and ocean sciences. This second and revised edition is even more comprehensive with numerous updates, and an additional appendix on 'Convolution and Fourier transforms'. Intended for both students and established scientists, the five major chapters of the book cover data acquisition and recording, data processing and presentation, statistical methods and error handling, analysis of spatial data fields, and time series analysis methods. Chapter 5 on time series analysis is a book in itself, spanning a wide diversity of topics from stochastic processes and stationarity, coherence functions, Fourier analysis, tidal harmonic analysis, spectral and cross-spectral analysis, wavelet and other related methods for processing nonstationary data series, digital filters, and fractals. The seven appendices include unit conversions, approximation methods and nondimensional numbers used in geophysical fluid dynamics, presentations on convolution, statistical terminology, and distribution functions, and a number of important statistical tables. Twenty pages are devoted to references. Featuring: • An in-depth presentation of modern techniques for the analysis of temporal and spatial data sets collected in oceanography, geophysics, and other disciplines in earth and ocean sciences. • A detailed overview of oceanographic instrumentation and sensors - old and new - used to collect oceanographic data. • 7 appendices especially applicable to earth and ocean sciences ranging from conversion of units, through statistical tables, to terminology and non-dimensional parameters. In praise of the first edition: "(...)This is a very practical guide to the various statistical analysis methods used for obtaining information from geophysical data, with particular reference to oceanography(...) The book provides both a text for advanced students of the geophysical sciences and a useful reference volume for researchers." Aslib Book Guide Vol 63, No. 9, 1998 "(...)This is an excellent book that I recommend highly and will definitely use for my own research and teaching." EOS Transactions, D.A. Jay, 1999 "(...)In summary, this book is the most comprehensive and practical source of information on data analysis methods available to the physical oceanographer. The reader gets the benefit of extremely broad coverage and an excellent set of examples drawn from geographical observations." Oceanography, Vol. 12, No. 3, A. Plueddemann, 1999 "(...)Data Analysis Methods in Physical Oceanography is highly recommended for a wide range of readers, from the relative novice to the experienced researcher. It would be appropriate for academic and special libraries." E-Streams, Vol. 2, No. 8, P. Mofjelf, August 1999
This publication is designed to provide a practical understanding of methods of parameter estimation and uncertainty analysis. The practical problems covered range from simple processing of time- and space-series data to inversion of potential field, seismic, electrical, and electromagnetic data. The various formulations are reconciled with field data in the numerous examples provided in the book; well-documented computer programmes are also given to show how easy it is to implement inversion algorithms.
This book was primarily written for an audience that has heard about neural networks or has had some experience with the algorithms, but would like to gain a deeper understanding of the fundamental material. For those that already have a solid grasp of how to create a neural network application, this work can provide a wide range of examples of nuances in network design, data set design, testing strategy, and error analysis. Computational, rather than artificial, modifiers are used for neural networks in this book to make a distinction between networks that are implemented in hardware and those that are implemented in software. The term artificial neural network covers any implementation that is inorganic and is the most general term. Computational neural networks are only implemented in software but represent the vast majority of applications. While this book cannot provide a blue print for every conceivable geophysics application, it does outline a basic approach that has been used successfully.
Data Analysis Methods in Physical Oceanography, Third Edition is a practical reference to established and modern data analysis techniques in earth and ocean sciences. Its five major sections address data acquisition and recording, data processing and presentation, statistical methods and error handling, analysis of spatial data fields, and time series analysis methods. The revised Third Edition updates the instrumentation used to collect and analyze physical oceanic data and adds new techniques including Kalman Filtering. Additionally, the sections covering spectral, wavelet, and harmonic analysis techniques are completely revised since these techniques have attracted significant attention over the past decade as more accurate and efficient data gathering and analysis methods. Completely updated and revised to reflect new filtering techniques and major updating of the instrumentation used to collect and analyze data Co-authored by scientists from academe and industry, both of whom have more than 30 years of experience in oceanographic research and field work Significant revision of sections covering spectral, wavelet, and harmonic analysis techniques Examples address typical data analysis problems yet provide the reader with formulaic “recipes for working with their own data Significant expansion to 350 figures, illustrations, diagrams and photos
Presenting statistical and stochastic methods for the analysis and design of technological systems in engineering and applied areas, this work documents developments in statistical modelling, identification, estimation and signal processing. The book covers such topics as subspace methods, stochastic realization, state space modelling, and identification and parameter estimation.
DEKORP, the German continental reflection seismic program, was the major focus of deep seismic research in Germany in the 1980s and 1990s. The seismic sections provided fundamental new insight into deep geological structure of the European continent and the dynamics of continental formation. They formed the basis for worldwide comparative studies of orogenic structure. The complicated signature of the reflections from the deep crust indicated that new processing and interpretation techniques must be considered to better image the crystalline crust. Results of these efforts, including pre-stack migration, 3-D imaging, shear waves and seismic anisotropy, are presented in this special volume. In part, the articles open the perspective to new and future research. In part, they document research activity triggered by technical and interpretational questions raised by DEKORP field work and profiling results. Many of the presented methods can find immediate application in industrial seismic prospecting.
Öz Yilmaz has expanded his original volume on processing to include inversion and interpretation of seismic data. In addition to the developments in all aspects of conventional processing, this two-volume set represents a comprehensive and complete coverage of the modern trends in the seismic industry-from time to depth, from 3-D to 4-D, from 4-D to 4-C, and from isotropy to anisotropy.
This volume is intended to give the geophysical signal analyst sufficient material to understand the usefulness of data covariance matrix analysis in the processing of geophysical signals. A background of basic linear algebra, statistics, and fundamental random signal analysis is assumed. This reference is unique in that the data vector covariance matrix is used throughout. Rather than dealing with only one seismic data processing problem and presenting several methods, the concentration in this book is on only one fundamental methodology-analysis of the sample covariance matrix-presenting many seismic data problems to which the methodology applies. This volume should be of interest to many researchers, providing a method amenable to many distinct applications. It offers a diverse sampling and discussion of the theory and the literature developed to date from a common viewpoint.
This revised and expanded text explains the latest statistical methods that are being used to describe, analyze, test, and forecast atmospheric data. It features numerous worked examples, illustrations, equations, and exercises with separate solutions. The book will help advanced students and professionals understand and communicate what their data sets have to say, and make sense of the scientific literature in meteorology, climatology, and related disciplines.
The expansion of statistics; Classical statistics; The frequency-distribution of igneous rocks; A test for the significance of lithological variations; Application of logarithmic moments to size frequency distributions of sediments; On the role of the logarithmically normal law of frequency distribution in petrology and geochemistry; Statistics in sedimentary petrology; Studies in quantitative paleontology II multivariate analysis - a new analytical tool for paleontology and geology; Experimental design in the earth sciences; Sedimentation time trend functions and their application for correlation of sedimentary deposits; Classification of modern bahamian carbonate sediments; Multivariate analytical treatment of quantitative species associations: an example from paleoecology; Cluster analysis applied to multivariate geologic problems; The statistics of orientation data; Statistical methods - a second look; Some consequences of applying lognormal theory to pseudolognormal distributions; An approximate statistical test for correlations between proportions; Critical review of some multivariate procedures in the analysis of geochemical data; Factor analysis; Problems of sampling in geoscience; New statistical methods; Principles of geostatistics; Markovian models in stratigraphic analysis; Interpretation of complex lithologic successions by substitutability analysis; An empirical discrimanant method applied to sedimentary-rock classification from major-element geochemistry; Progress in R- and Q- mode analysis: correspondence analysis and its application to the study of geological processes; References; Author citation index.
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 explosive development of information science and technology puts in new problems involving statistical data analysis. These problems result from higher re quirements concerning the reliability of statistical decisions, the accuracy of math ematical models and the quality of control in complex systems. A new aspect of statistical analysis has emerged, closely connected with one of the basic questions of cynergetics: how to "compress" large volumes of experimental data in order to extract the most valuable information from data observed. De tection of large "homogeneous" segments of data enables one to identify "hidden" regularities in an object's behavior, to create mathematical models for each seg ment of homogeneity, to choose an appropriate control, etc. Statistical methods dealing with the detection of changes in the characteristics of random processes can be of great use in all these problems. These methods have accompanied the rapid growth in data beginning from the middle of our century. According to a tradition of more than thirty years, we call this sphere of statistical analysis the "theory of change-point detection. " During the last fifteen years, we have witnessed many exciting developments in the theory of change-point detection. New promising directions of research have emerged, and traditional trends have flourished anew. Despite this, most of the results are widely scattered in the literature and few monographs exist. A real need has arisen for up-to-date books which present an account of important current research trends, one of which is the theory of non parametric change--point detection.
Massive data streams, large quantities of data that arrive continuously, are becoming increasingly commonplace in many areas of science and technology. Consequently development of analytical methods for such streams is of growing importance. To address this issue, the National Security Agency asked the NRC to hold a workshop to explore methods for analysis of streams of data so as to stimulate progress in the field. This report presents the results of that workshop. It provides presentations that focused on five different research areas where massive data streams are present: atmospheric and meteorological data; high-energy physics; integrated data systems; network traffic; and mining commercial data streams. The goals of the report are to improve communication among researchers in the field and to increase relevant statistical science activity.
Since 1984, Geophysical Data Analysis has filled the need for a short, concise reference on inverse theory for individuals who have an intermediate background in science and mathematics. The new edition maintains the accessible and succinct manner for which it is known, with the addition of: MATLAB examples and problem sets Advanced color graphics Coverage of new topics, including Adjoint Methods; Inversion by Steepest Descent, Monte Carlo and Simulated Annealing methods; and Bootstrap algorithm for determining empirical confidence intervals Additional material on probability, including Bayesian influence, probability density function, and metropolis algorithm Detailed discussion of application of inverse theory to tectonic, gravitational and geomagnetic studies Numerous examples and end-of-chapter homework problems help you explore and further understand the ideas presented Use as classroom text facilitated by a complete set of exemplary lectures in Microsoft PowerPoint format and homework problem solutions for instructors
We are poised to embark on a new era of discovery in the study of geomorphology. The discipline has a long and illustrious history, but in recent years an entirely new way of studying landscapes and seascapes has been developed. It involves the use of 3D seismic data. Just as CAT scans allow medical staff to view our anatomy in 3D, seismic data now allows Earth scientists to do what the early geomorphologists could only dream of - view tens and hundreds of square kilometres of the Earth's subsurface in 3D and therefore see for the first time how landscapes have evolved through time. This volume demonstrates how Earth scientists are starting to use this relatively new tool to study the dynamic evolution of a range of sedimentary environments.