Neural implementation of fuzzy K-NN classification for seismic pattern recognition

Kou-Yuan Huang*, Yune Wei Yuan

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

Abstract

Fuzzy K-nearest neighbor classification rule is implemented by neural network of the Hamming net and is applied to seismic first arrival picking. Two kinds of feature set are used in two experiments. The first experiment uses amplitude, mean power level, power ratio, and envelope slope as the features. The second experiment uses average amplitude and envelope at the local maximum amplitude as the features. In the training stage, the training wavelets of the first arrival are selected from the training traces, and features are generated. Then assign fuzzy membership to each training wavelet. In the testing stage, features of each seismic trace is generated, and each candidate local picks is selected. Then the features of each candidate local pick are through the neural network to determine the candidate is the first arrival or not. The experimental results are quite encouraging.

Original languageEnglish
Pages1588-1593
Number of pages6
StatePublished - 1 Jan 1996
EventProceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) - Washington, DC, USA
Duration: 3 Jun 19966 Jun 1996

Conference

ConferenceProceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4)
CityWashington, DC, USA
Period3/06/966/06/96

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