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Deconvolution of Geophysical Time Series in the Exploration for Oil and Natural Gas
Optimal Seismic Deconvolution: An Estimation-Based Approach presents an approach to the problem of seismic deconvolution. It is meant for two different audiences: practitioners of recursive estimation theory and geophysical signal processors. The book opens with a chapter on elements of minimum-variance estimation that are essential for all later developments. Included is a derivation of the Kaiman filter and discussions of prediction and smoothing. Separate chapters follow on minimum-variance deconvolution; maximum-likelihood and maximum a posteriori estimation methods; the philosophy of maximum-likelihood deconvolution (MLD); and two detection procedures for determining the location parameters in the input sequence product model. Subsequent chapters deal with the problem of estimating the parameters of the source wavelet when everything else is assumed known a priori; estimation of statistical parameters when the source wavelet is known a priori; and a different block component method for simultaneously estimating all wavelet and statistical parameters, detecting input signal occurrence times, and deconvolving a seismic signal. The final chapter shows how to incorporate the simplest of all models—the normal incidence model—into the maximum-likelihood deconvolution procedure.
Geophysics, the excellent exploration tool which traditionally uses the latest techniques has been in great demand, and has assisted by remarkable development of the methods which consist of gravimetry, electromagnetics and, the most important, seismic reflection. The book is presented like an encyclopedia. One may find an exact definition, illustrated with simple sketches, precise formulae & orders of magnitude & data which have so often been missing.
How to produce images with sound has intrigued engineers and scientists for many years. Bats, whales and dolphins can easily get good mental images with acoustical energy, but humans have little natural ability for obtaining such images. The history of engineering and science, however, is an impressive demonstration that technological solutions can compensate, and then some, for deficiencies of nature in humans. Thus with the proper technology, we too can "see" with sound. Many methods involv ing ultrasonic energy can be employed to enable us to do so. Few of these methods are at all reminiscent of the acoustic systems employed by animals. Pulse-echo, phase-amplitude and amplitude-mapping approaches constitute the conceptual bases for three fundamentally different types of acoustic imaging systems and can be used for categorizing the systems. However, by now systems exist that combine the approaches in such sophisticated ways as to make an unambiguous categorization of some of the more complicated systems difficult or impossible. Among the instruments so far pro duced are mechanically-scanning focused instruments, chirped pulse-echo instruments, and instruments involving holography, tomography, parametric excitation, phase conju gation, neural networks, random phase transduction, finite element methods, Doppler frequency shifting, pseudo inversion, Bragg diffraction and reflection, and a host of other principles. The fifty-five chapters in this volume are selected from papers presented at the Eighteenth International Symposium on Acoustical Imaging which was held in Santa Barbara, California on September 18 - 20, 1989.
Covering ideas and methods while concentrating on fundamentals, this book includes wave motion; digital imaging; digital filtering; visualization aspects of the seismic reflection method; sampling theory; the frequency spectrum; synthetic seismograms; wavelet processing; deconvolution; seismic attributes; phase rotation; and seismic attenuation.
This state-of-the-art survey serves as a complete overview of the subject. Besides the principles and theoretical foundations, emphasis is laid on practical applicability -- describing not only classical methods, but also modern developments and their applications. Students, researchers and practitioners, especially in the fields of data registration, treatment and evaluation, will find this a wealth of information.
Originating in 1967 as notes to accompany a basic seminar for the Canadian SEG and then expanded in 1968 as an SEG Continuing Education course, this text focuses on how to choose processes and parameters for any given field data.
Convolution is the most important operation that describes the behavior of a linear time-invariant dynamical system. Deconvolution is the unraveling of convolution. It is the inverse problem of generating the system's input from knowledge about the system's output and dynamics. Deconvolution requires a careful balancing of bandwidth and signal-to-noise ratio effects. Maximum-likelihood deconvolution (MLD) is a design procedure that handles both effects. It draws upon ideas from Maximum Likelihood, when unknown parameters are random. It leads to linear and nonlinear signal processors that provide high-resolution estimates of a system's input. All aspects of MLD are described, from first principles in this book. The purpose of this volume is to explain MLD as simply as possible. To do this, the entire theory of MLD is presented in terms of a convolutional signal generating model and some relatively simple ideas from optimization theory. Earlier approaches to MLD, which are couched in the language of state-variable models and estimation theory, are unnecessary to understand the essence of MLD. MLD is a model-based signal processing procedure, because it is based on a signal model, namely the convolutional model. The book focuses on three aspects of MLD: (1) specification of a probability model for the system's measured output; (2) determination of an appropriate likelihood function; and (3) maximization of that likelihood function. Many practical algorithms are obtained. Computational aspects of MLD are described in great detail. Extensive simulations are provided, including real data applications.
This book completes Professor Shrock's full-scale history of MIT's Geology Department.