This collection of conference papers describes state-of-the-art methodologies and algorithms used in the treatment of inverse problems, focusing on seismology and image processing. The papers also describe new general methodologies for analysis and solution of inverse problems by means of statistical and deterministic algorithms. The book gives a glimpse of recent techniques, many of which are still under development.
This collection of conference papers describes state-of-the-art methodologies and algorithms used in the treatment of inverse problems, focusing on seismology and image processing. The papers also describe new general methodologies for analysis and solution of inverse problems by means of statistical and deterministic algorithms. The book gives a glimpse of recent techniques, many of which are still under development.
Paleostress Inversion Techniques: Methods and Applications for Tectonics is an ideal reference for both academic and industry researchers in the Earth Sciences. The book introduces the methodologies developed to reconstruct (paleo) stress tensors from geological data. The interest and potential outcomes of the methods are illustrated by practical examples and supplementary electronic material and an overview on future research directions. As paleostress inversion methods are particularly useful in tectonic analyses at regional and local scales and their outcomes are relevant when trying to predict the orientations of fracture sets and potential fluid flow paths and associated mineralizations, this book provides an ideal resource. - Includes detailed explanations of methods, along with concrete applications of paleostress inversion techniques - Clearly illustrates the outcomes, advantages and limitations of the techniques - Serves as a practical guide for both academics and industry researchers interested in structural geology, geodynamics and tectonics
This collection of conference papers describes state-of-the-art methodologies and algorithms used in the treatment of inverse problems, focusing on seismology and image processing. The papers also describe new general methodologies for analysis and solution of inverse problems by means of statistical and deterministic algorithms. The book gives a glimpse of recent techniques, many of which are still under development.
This book introduces readers to seismic inversion methods and their application to both synthetic and real seismic data sets. Seismic inversion methods are routinely used to estimate attributes like P-impedance, S-impedance, density, the ratio of P-wave and S-wave velocities and elastic impedances from seismic and well log data. These attributes help to understand lithology and fluid contents in the subsurface. There are several seismic inversion methods available, but their application and results differ considerably, which can lead to confusion. This book explains all popular inversion methods, discusses their mathematical backgrounds, and demonstrates their capacity to extract information from seismic reflection data. The types covered include model-based inversion, colored inversion, sparse spike inversion, band-limited inversion, simultaneous inversion, elastic impedance inversion and geostatistical inversion, which includes single-attribute analysis, multi-attribute analysis, probabilistic neural networks and multi-layer feed-forward neural networks. In addition, the book describes local and global optimization methods and their application to seismic reflection data. Given its multidisciplinary, integrated and practical approach, the book offers a valuable tool for students and young professionals, especially those affiliated with oil companies.
Inverse problems arise in practical applications whenever one needs to deduce unknowns from observables. This monograph is a valuable contribution to the highly topical field of computational inverse problems. Both mathematical theory and numerical algorithms for model-based inverse problems are discussed in detail. The mathematical theory focuses on nonsmooth Tikhonov regularization for linear and nonlinear inverse problems. The computational methods include nonsmooth optimization algorithms, direct inversion methods and uncertainty quantification via Bayesian inference.The book offers a comprehensive treatment of modern techniques, and seamlessly blends regularization theory with computational methods, which is essential for developing accurate and efficient inversion algorithms for many practical inverse problems.It demonstrates many current developments in the field of computational inversion, such as value function calculus, augmented Tikhonov regularization, multi-parameter Tikhonov regularization, semismooth Newton method, direct sampling method, uncertainty quantification and approximate Bayesian inference. It is written for graduate students and researchers in mathematics, natural science and engineering.
The purpose of this book is twofold: To survey control system design methods based on the system inversion technique and to collect into one place the many recent results in the field. It has been known for some time that inverse systems may be used to solve numerous control problems. Despite the importance and conceptual simplicity of this topic there appears to be no monograph written on it. The purpose of this work is therefore to present and apply a systematic design method which bases itself on the fundamental system property of invertibility. Many different theoretical and practical aspects are considered in this volume working from elementary topics in the first section to current research in the second.
An up-to-date overview of global optimization methods used to formulate and interpret geophysical observations, for researchers, graduate students and professionals.
This book describes the theory and practice of inverting seismic data for the subsurface rock properties of the earth. The primary application is for inverting reflection and/or transmission data from engineering or exploration surveys, but the methods described also can be used for earthquake studies. Seismic Inversion will be of benefit to scientists and advanced students in engineering, earth sciences, and physics. It is desirable that the reader has some familiarity with certain aspects of numerical computation, such as finite-difference solutions to partial differential equations, numerical linear algebra, and the basic physics of wave propagation. For those not familiar with the terminology and methods of seismic exploration, a brief introduction is provided. To truly understand the nuances of seismic inversion, we have to actively practice what we preach (or teach). Therefore, computational labs are provided for most of the chapters, and some field data labs are given as well.
Seismic inversion aims to reconstruct a quantitative model of the Earth subsurface, by solving an inverse problem based on seismic measurements. There are at least three fundamental issues to be solved simultaneously: non-linearity, non-uniqueness, and instability. This book covers the basic theory and techniques used in seismic inversion, corresponding to these three issues, emphasising the physical interpretation of theoretical concepts and practical solutions. This book is written for master and doctoral students who need to understand the mathematical tools and the engineering aspects of the inverse problem needed to obtain geophysically meaningful solutions. Building on the basic theory of linear inverse problems, the methodologies of seismic inversion are explained in detail, including ray-impedance inversion and waveform tomography etc. The application methodologies are categorised into convolutional and wave-equation based groups. This systematic presentation simplifies the subject and enables an in-depth understanding of seismic inversion. This book also provides a practical guide to reservoir geophysicists who are attempting quantitative reservoir characterisation based on seismic data. Philosophically, the seismic inverse problem allows for a range of possible solutions, but the techniques described herein enable geophysicists to exclude models that cannot satisfy the available data. This book summarises the author’s extensive experience in both industry and academia and includes innovative techniques not previously published.
