Neural network and tree automaton for seismic pattern recognition

Kou-Yuan Huang; Yi Hsian Chao

doi:10.1109/IJCNN.2004.1379996

Neural network and tree automaton for seismic pattern recognition

Kou-Yuan Huang^*, Yi Hsian Chao

^*Corresponding author for this work

Department of Computer Science

Research output: Contribution to journal › Conference article › peer-review

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 language	English
Pages (from-to)	663-668
Number of pages	6
Journal	IEEE International Conference on Neural Networks - Conference Proceedings
Volume	1
DOIs	https://doi.org/10.1109/IJCNN.2004.1379996
State	Published - 1 Dec 2004
Event	2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary Duration: 25 Jul 2004 → 29 Jul 2004

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title = "Neural network and tree automaton for seismic pattern recognition",

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.",

author = "Kou-Yuan Huang and Chao, {Yi Hsian}",

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N2 - 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.

AB - 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.

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