Neural networks for seismic wavelet extraction and clustering

Kou-Yuan Huang*, Shen Pyng Wang

*Corresponding author for this work

研究成果: Article同行評審

摘要

The neural net of Carpenter/Grossberg's adaptive resonance theory (ART2) has the ability of the unsupervised self-organizing clustering for analog input patterns. So we use the ART2 neural net for seismic wavelet clustering in the seismogram. The preprocessing includes peak detection and wavelet extraction using the supervised multilayer perceptron (MLP). After extracting seismic wavelets, we use ART2 net to cluster the seismic wavelets. In the experiment, the real seismogram at Mississippi Canyon is analyzed. 371 wavelets are extracted and clustered into 3 classes. The wavelet clustering results can show the uniform property of the reflection layer and improve the seismic interpretation.

原文English
頁(從 - 到)741-744
頁數4
期刊SEG Technical Program Expanded Abstracts
19
發行號1
DOIs
出版狀態Published - 1 一月 2000
事件2000 Society of Exploration Geophysicists Annual Meeting, SEG 2000 - Calgary, Canada
持續時間: 6 八月 200011 八月 2000

指紋 深入研究「Neural networks for seismic wavelet extraction and clustering」主題。共同形成了獨特的指紋。

引用此