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Overview
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 book will benefit geophysicists, computer scientists and electrical engineers.
Synopsis
The theories of syntactic pattern recognition and its applications in geophysical intelligent interpretation systems are described in this text for geophysicists, computer scientists, and electrical engineers. Topics include the theory of formal languages and parsing methods, syntactic pattern recognition techniques applied to 1-D seismic traces in order to classify Ricker wavelets, methods using Early's parsing, the use of dynamic programming to compute matches of primitive measures, the application of Levenshtein distance computation, tree automaton system, and the use of a hierarchical system to recognize patterns in a seismogram. Huang was educated in the US and Taiwan and now teaches computer science at the National Chiao Tung U. in Taiwan. Annotation (c)2003 Book News, Inc., Portland, OR