Neural networks for robust recognition of seismic reflection patterns

Jar Long Wang, Kou-Yuan Huang*

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

3 Scopus citations

Abstract

In this paper, a neural network approach for the recognition of seismic reflection patterns is developed. A multi-layer perceptron is trained as the classifier by using the back-propagation algorithm which varies the number of hidden units [1], and the central moments are employed for feature extraction. Seismic reflection patterns (classes) are designed in the experimental domain. The network is initially trained with noisefree training samples, and is retrained gradually with misclassified noisy shifted testing patterns to improve the robustness of the classifier. Through classifying a large set of unknown testing patterns of various noise degrees, a great augmentation in system robustness and an encouraging recognition performance are presented.

Original languageEnglish
Pages246-249
Number of pages4
StatePublished - 1 Jan 1993
Event1993 Society of Exploration Geophysicists Annual Meeting - Washington, United States
Duration: 26 Sep 199330 Sep 1993

Conference

Conference1993 Society of Exploration Geophysicists Annual Meeting
CountryUnited States
CityWashington
Period26/09/9330/09/93

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