Syntactic Pattern Recognition For Seismic Oil Exploration

By: Kou-yuan Huang

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Hardcover | 9 May 2002

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148 Pages

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The use of pattern recognition has become more and more important in seismic oil exploration. Interpreting a large volume of seismic data is a challenging problem. Seismic reflection data in the one-shot seismogram and stacked seismogram may contain some structural information from the response of the subsurface. Syntactic/structural pattern recognition techniques can recognize the structural seismic patterns and improve seismic interpretations. The syntactic analysis methods include: (1) the error-correcting finite-state parsing; (2) the modified error-correcting Earley's parsing; (3) the parsing using the match primitive measure; (4) the Levenshtein distance computation; (5) the likelihood ratio test; (6) the error-correcting tree automata; and (7) a hierarchical system. Syntactic seismic pattern recognition can be one of the milestones of a geophysical intelligent interpretation system. The syntactic methods in this book can be applied to other areas, such as the medical diagnosis system. The work should be of interest to geophysicists, computer scientists and electrical engineers.

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Author's Biography	p. vii
Preface	p. ix
Introduction to Syntactic Pattern Recognition	p. 1
Summary	p. 1
Introduction	p. 1
Organization of This Book	p. 3
Introduction to Formal Languages and Automata	p. 7
Summary	p. 7
Languages and Grammars	p. 7
Type 0 (unrestricted) grammar	p. 9
Type 1 (context-sensitive) grammar	p. 9
Type 2 (context-free) grammar	p. 9
Type 3 (finite-state or regular) grammar	p. 9
Finite-State Automaton	p. 10
Earley's Parsing	p. 14
Finite-State Grammatical Inference	p. 16
Inference of Canonical Finite-State Grammar	p. 16
Inference of Finite-State Grammar Based on K-Tails	p. 17
String Distance Computation	p. 18
Error-Correcting Finite-State Automaton for Recognition of Ricker Wavelets	p. 21
Summary	p. 21
Introduction	p. 21
Syntactic Pattern Recognition	p. 22
Training and Testing Ricker Wavelets	p. 22
Location of Waveforms and Pattern Representation	p. 25
Expanded Grammars	p. 25
General Expanded Finite-State Grammar	p. 25
Restricted Expanded Finite-State Grammar	p. 28
Minimum-Distance Error-Correcting Finite-State Parsing	p. 31
Classification of Ricker Wavelets	p. 32
Discussion and Conclusions	p. 37
Attributed Grammar and Error-Correcting Earley's Parsing	p. 39
Summary	p. 39
Introduction	p. 39
Attributed Primitives and String	p. 41
Definition of Error Transformations for Attributed Strings	p. 41
Inference of Attributed Grammar	p. 42
Minimum-Distance Error-Correcting Earley's Parsing for Attributed String	p. 45
Experiment	p. 47
Attributed Grammar and Match Primitive Measure (MPM) for Recognition of Seismic Wavelets	p. 51
Summary	p. 51
Similarity Measure of Attributed String Matching	p. 51
Inference of Attributed Grammar	p. 55
Top-Down Parsing Using Mpm	p. 56
Experiments of Seismic Pattern Recognition	p. 58
Recognition of Seismic Ricker Wavelets	p. 58
Recognition of Wavelets in Real Seismogram	p. 60
Conclusions	p. 64
String Distance and Likelihood Ratio test for Detection of Candidate Bright Spot	p. 65
Summary	p. 65
Introduction	p. 65
Optimal Quantization Encoding	p. 66
Likelihood Ratio Test (LRT)	p. 67
Levenshtein Distance and Error Probability	p. 68
Experiment at Mississippi Canyon	p. 69
Likelihood Ratio Test (LRT)	p. 72
Threshold for Global Detection	p. 72
Threshold for the Detection of Candidate Bright Spot	p. 72
Experiment at High Island	p. 73
Tree Grammar and Automaton for Seismic Pattern Recognition	p. 75
Summary	p. 75
Introduction	p. 75
Tree Grammar and Language	p. 77
Tree Automaton	p. 78
Tree Representations of Patterns	p. 84
Inference of Expansive Tree Grammar	p. 85
Weighted Minimum-Distance Specta	p. 86
Modified Maximum-Likelihood Specta	p. 92
Minimum Distance Gecta	p. 94
Experiments on Input Testing Seismograms	p. 95
Discussion and Conclusions	p. 102
A Hierarchical Recognition System of Seismic Patterns and Future Study	p. 103
Summary	p. 103
Introduction	p. 103
Syntactic Pattern Recognition	p. 107
Linking Processing and Segmentation	p. 107
Primitive Recognition	p. 107
Training Patterns	p. 108
Grammatical Inference	p. 109
Finite-state Error Correcting Parsing	p. 109
Common-source Simulated Seismogram Results	p. 110
Stacked Simulated Seismogram Results	p. 117
Conclusions	p. 121
Future Study	p. 121
References	p. 123
Index	p. 131
Table of Contents provided by Ingram. All Rights Reserved.

Syntactic Pattern Recognition For Seismic Oil Exploration

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