Seismic pattern recognition using neural network and tree automaton

Kou-Yuan Huang*, Yi Hsian Chao

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

We combine neural network and syntactic pattern recognition, and propose a tree automaton system for the recognition of structural seismic patterns in a seismogram. Multilayer perceptron of the neural network is used for the identification of subpatterns, then a tree representation of the structural seismic pattern is constructed. We use three kinds of modified bottom-up structure preserved error correcting tree automata to recognize the tree representation of syntactic pattern, and propose a new top-down error correcting tree automaton to recognize non-structural preserved seismic pattern. In the experiments, the system is applied to the simulated and the real seismic bright spot patterns. The recognition result can improve seismic interpretation.

Original languageEnglish
Pages3080-3083
Number of pages4
StatePublished - 1 Dec 2004
Event2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet. IGARSS 2004 - Anchorage, AK, United States
Duration: 20 Sep 200424 Sep 2004

Conference

Conference2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet. IGARSS 2004
CountryUnited States
CityAnchorage, AK
Period20/09/0424/09/04

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