Abstract
Decision-theoretic and syntactic pattern recognition techniques are employed to detect the physical anomalies (bright spots) and to recognize the structural seismic patterns in two-dimensional seismograms. Here, decision-theoretic methods include Bayes classification, linear and quadratic classifications, tree classification, partitioning-method and tree classification, and sequential classification. The generated features are envelope, instantaneous frequency, polarity, moments, and contrasts from cooccurrence matrix. A hierarchical system is proposed for seismic syntactic pattern recognition. Syntactic methods include error-correcting finite state automaton, picture description language (PDL), and tree automaton. Experiments using simulated and real seismograms are presented. The recognition results are quite encouraging.
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Huang, KY. (1992). Pattern Recognition to Seismic Exploration. In: Palaz, I., Sengupta, S.K. (eds) Automated Pattern Analysis in Petroleum Exploration. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4388-5_7
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DOI: https://doi.org/10.1007/978-1-4612-4388-5_7
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