Coding and Decoding: Seismic Data

The Concept of Multishooting
 
 
Elsevier (Verlag)
  • 2. Auflage
  • |
  • erschienen am 14. Dezember 2017
  • |
  • 718 Seiten
 
E-Book | ePUB mit Adobe-DRM | Systemvoraussetzungen
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978-0-12-811111-6 (ISBN)
 

Coding and Decoding Seismic Data: The Concept of Multishooting, Volume One, Second Edition, offers a thorough investigation of modern techniques for collecting, simulating, and processing multishooting data. Currently, the acquisition of seismic surveys is performed as a sequential operation in which shots are computed separately, one after the other. The cost of performing various shots simultaneously is almost identical to that of one shot; thus, the benefits of using the multishooting approach for computing seismic surveys are enormous.

By using this approach, the longstanding problem of simulating a three-dimensional seismic survey can be reduced to a matter of weeks. Providing both theoretical and practical explanations of the multishooting approach, including case histories, this book is an essential resource for exploration geophysicists and practicing seismologists.

  • Investigates how to collect, stimulate, and process multishooting data
  • Addresses the improvements in seismic characterization and resolution that can be expected from multishooting data
  • Provides information for the oil and gas exploration and production business that will influence day-to-day surveying techniques
  • Covers robust decoding methods of undetermined mixtures, nonlinear decoding, the use of constraints in decoding processes, and nonlinear imaging of undecoded data
  • Includes access to a companion site with answers to questions posed in the book


Dr. Luc Ikelle is a Professor in Geology and Geophysics at Texas A&M University. He received his PhD in Geophysics from Paris 7 University in 1986 and has sense cultivated expertise in: seismic data acquisition, modeling, processing, and interpretation for conventional and unconventional energy production; inverse problem theory, signal processing, linear and nonlinear elastic wave propagation, linear and nonlinear optics, and continuum and fracture mechanics. His research interests include a combined analysis of petroleum systems, earthquakes, and volcanic eruptions based on geology, geophysics, statistical modeling, and control theory.

He is a founding member of Geoscientists Without Borders, for which he received an award from SEG in 2010. He is a member of the editorial board of the Journal of Seismic Exploration and has published 107 refereed publications in international journals.

2468-547X
  • Englisch
  • Saint Louis
  • |
  • USA
  • 95,43 MB
978-0-12-811111-6 (9780128111116)
weitere Ausgaben werden ermittelt
  • Front Cover
  • Coding and Decoding: Seismic Data
  • Copyright
  • Contents
  • Preface (First Edition)
  • Preface (Second Edition)
  • 1 Introduction to Multishooting: Challenges and Rewards
  • 1.1 Dimensions and Notation Conventions
  • 1.1.1 Coordinate systems
  • 1.1.2 Dimensions of heterogeneous media
  • 1.1.3 Notation conventions
  • 1.1.4 The f-x and f-k domains
  • 1.2 Scattering Experiments in Petroleum Seismology
  • 1.2.1 Principles of seismic acquisition
  • 1.2.2 Seismic data
  • 1.2.3 Shot, receiver, midpoint, and offset gathers
  • 1.2.4 Multiazimuthal data
  • 1.3 Acquisition of Multishot Data
  • 1.3.1 Multiazimuth surveys
  • 1.3.2 Flip- op acquisition
  • 1.3.3 Source encoding: the marine style
  • 1.3.4 Source encoding: the land-vibroseis style
  • 1.3.5 The challenges of multishooting acquisition
  • 1.4 Processing of Multishot Data
  • 1.4.1 Reciprocity theorems
  • 1.4.2 Cross-talk and challenges of the decoding process
  • 1.4.3 The challenges of imaging multishot data without decoding
  • 1.5 The Nonlinear Elasticity and the Superposition Principle
  • 1.5.1 Linear and nonlinear media
  • 1.5.2 Second-order nonlinear media
  • 1.5.3 Volterra series
  • 1.6 The Cocktail-Party Problem: A Multidisciplinary Problem
  • 1.6.1 A brief review of the cocktail-party problem
  • 1.6.2 Coding and decoding in communication theory
  • 1.6.3 Processing of multishot data without decoding
  • 1.6.4 Nearly simultaneous earthquakes
  • 1.6.5 Nearly simultaneous sources of volcanic activities
  • 1.7 Scope and Content of This Book
  • Exercises
  • 2 Decoding of Linear Instantaneous Mixtures
  • 2.1 Seismic Data Representation as Random Variables
  • 2.1.1 Examples of random variables
  • 2.1.2 From seismic signals to seismic random variables
  • 2.1.3 Probability-density function (PDF) of seismic random variables
  • 2.1.4 Moments and cumulants of seismic random variables
  • 2.1.5 Negentropy: a measurement of non-Gaussianity
  • 2.2 Uncorrelatedness and Independence
  • 2.2.1 Joint probability-density functions and Kullback-Leibler divergence
  • 2.2.2 Joint moments and joint cumulants
  • 2.2.3 Uncorrelatedness and whiteness of random variables
  • 2.2.4 Independence of random variables
  • 2.2.5 Analysis of uncorrelatedness and independence with scatterplots
  • 2.2.6 Whitening
  • 2.3 ICA Decoding
  • 2.3.1 Decoding by maximizing contrast functions
  • 2.3.2 Decoding by cumulant-tensor diagonalization
  • 2.3.3 ICA decoding by negentropy maximizing
  • 2.3.4 ICA decoding methods for noisy mixtures
  • 2.3.5 Decoding by joint diagonalization of the autocovariance matrices
  • 2.4 Constrained Independent Component Analysis (cICA) Decoding
  • 2.4.1 Negentropy maximization
  • 2.4.2 Cumulant-tensor diagonalization
  • 2.4.3 Nonorthogonal cICA, based on maximum likelihood
  • Exercises
  • 3 Decoding of Linear Convolutive Mixtures
  • 3.1 Motivation and Foundation for Working in the T-F-X Domain
  • 3.1.1 Convolutive mixtures in the T-X domain
  • 3.1.2 Convolutive mixtures in the F-X domain
  • 3.1.3 Convolutive mixtures in the T-F-X domain
  • 3.2 Statistics of Complex Random Variables and Vectors
  • 3.2.1 The complex-valued gradient and the Hessian matrix
  • 3.2.2 Statistics of complex random variables
  • 3.2.3 Statistics of complex random vectors
  • 3.2.4 An analysis of the statistical independence of seismic data in the T-F-X domain
  • 3.3 Decoding in the T-F-X Domain
  • 3.3.1 Whiteness of complex random variables
  • 3.3.2 Decoding by negentropy maximization of complex random vectors
  • 3.3.3 Permutation inconsistency problem
  • 3.3.4 Cascaded and constrained ICA approaches
  • 3.3.5 Numerical examples
  • 3.4 Decoding In other Domains and Nonlinear Mixtures
  • 3.4.1 Decoding in the F-X domain
  • 3.4.2 Decoding in the T-X domain
  • 3.4.3 Decoding of convolutive post-nonlinear mixtures
  • Exercises
  • 4 Decoding of Underdetermined Mixtures
  • 4.1 Estimation of the Mixing Matrix
  • 4.1.1 Histograms of data-concentration directions
  • 4.1.2 Expectation maximization
  • 4.1.3 Histogram approach in the T-F-X domain
  • 4.1.4 Isolated single-shot estimation in the T-F-X domain
  • 4.1.5 Isolated single-shot estimation in the T-F domain
  • 4.2 ICA- and Sparsity-Based Decoding: 2M3S
  • 4.2.1 Combinatory search
  • 4.2.2 ICA-based decoding: formulation
  • 4.2.3 ICA-based decoding: examples
  • 4.2.4 The compressive sensing relationship with multishooting
  • 4.3 Decoding Using Denoising Tools: 1M2S and 1M4S
  • 4.3.1 Phase encoding and cross-talk
  • 4.3.2 Basic formulation of phase encoding
  • 4.3.3 ICA-based decoding
  • 4.3.4 Denoising-based decoding: l1-norm and total variations
  • 4.3.5 Compressive sensing and phase decoding data
  • 4.3.6 Denoising-based decoding: dictionary ltering
  • 4.3.7 Denoising-based decoding: median ltering
  • 4.3.8 Decoding with reference shots
  • 4.4 Multicomponent-Based Decoding: 1M2S, 1MS4, and 1M8S
  • 4.4.1 Simultaneous deghosting and decoding of multishot data
  • 4.4.2 Decoding of deghosted multishot data
  • 4.5 Array-Based Decoding
  • 4.6 ICA-Based Decoding: 1M16S
  • 4.6.1 Decoding in the T-F-X domain with a known mixing matrix
  • 4.6.2 Decoding based on isolated single-shot estimations
  • 4.6.3 Decoding in the T-F-X domain with an unknown mixing matrix
  • Exercises
  • 5 Decoding of Nonlinear Mixtures
  • 5.1 Models of Nonlinear Mixtures
  • 5.1.1 The general nonlinear mixing model
  • 5.1.2 Post-nonlinear (PNL) mixtures
  • 5.1.3 Multilayer perceptron model
  • 5.1.4 Convolutive nonlinear mixtures
  • 5.2 Scatterplots of Nonlinear Mixtures
  • 5.2.1 2D scatterplots
  • 5.2.2 3D scatterplots
  • 5.3 Decoding of Post-Nonlinear Mixtures
  • 5.3.1 Alternating conditional expectations (ACE)
  • 5.3.2 Inverse of a speci ed cumulative distribution function (ICDF)
  • 5.3.3 Geometrical-transformation approach
  • 5.4 Kernel-Based Decoding of Nonlinear Mixtures
  • 5.4.1 The making of nonlinear mixtures
  • 5.4.2 A brief background on linear principal component analysis
  • 5.4.3 A nonlinear form of principal component analysis
  • 5.4.4 Construction of linearized mixtures and decoding
  • 5.5 Kernel Canonical Correlation Analysis
  • 5.5.1 Canonical correlation analysis
  • 5.5.2 Kernel canonical correlation analysis
  • Problems
  • 6 Imaging of Multishot Data Without Decoding
  • 6.1 Linearized Inversion
  • 6.1.1 The NMF-based demultiple
  • 6.1.2 The sea-level-based demultiple
  • 6.1.3 Migration/inversion
  • 6.1.4 Velocity estimation
  • 6.1.5 Tomography
  • 6.2 Nonlinear Inversion
  • 6.2.1 Automated imaging
  • 6.2.2 Seismic imaging machine
  • 6.3 Modeling. Decoding, and Imaging in Snapshot Domain
  • 6.3.1 Multicomponent recordings
  • 6.3.2 Alternating conditional expectations (ACE)
  • Problems
  • Appendix A Some Background on Sparsity Optimization
  • A.1 l0-Norms
  • A.1.1 l2-minimization
  • A.1.2 l0-minimization: de nition
  • A.1.3 Various ways of measuring sparsity
  • A.1.4 l0-minimization: uniqueness
  • A.2 l1-Norm
  • A.2.1 An example of a linear system
  • A.2.2 Convex and nonconvex optimization problems
  • A.2.3 A practical implementation of the l1-minimization
  • A.2.4 l1-optimization of complex-valued data
  • Appendix B ICA Decomposition
  • Appendix C Nonnegative Matrix Factorization
  • C.1 Lee-Seung Matrix Factorization Algorithm
  • C.1.1 Mathematical formulation
  • C.1.2 Numerical illustrations of the forward and inverse transform
  • C.1.3 Selecting the number of elements of a dictionary
  • C.1.4 Nonnegative matrix factorization with auxiliary constraints
  • C.1.5 NMF optimization criteria
  • C.2 Other Nonnegative Matrix Factorization Algorithms
  • C.2.1 Project-gradient algorithm
  • C.2.2 Alternating least-squares algorithm
  • C.3 Decoding Challenges
  • Appendix D Nonnegative Tensor Factorization
  • D.1 PARAFAC Decomposition Model
  • D.2 Tucker Tensor Factorization
  • Appendix E A Review of 3D Finite-Difference Modeling
  • E.1 Basic Equations for Elastodynamic Wave Motion
  • E.2 Discretization In Both Time and Space
  • E.3 Staggered-Grid Implementation
  • E.4 Stability of the Staggered-Grid Finite-Difference Modeling
  • E.5 Grid Dispersion in Finite-Difference Modeling
  • E.6 Boundary Conditions
  • Bibliography
  • Index
  • Back Cover

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