Seismic pattern recognition using cellular neural network

Kou-Yuan Huang, Wen Hsuan Hsieh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Cellular neural network is adopted for seismic pattern recognition. We design cellular neural network to behave as associative memory according to the stored patterns, and finish the training process of the network. Then we use this associative memory to recognize seismic test patterns. In the experiments, the analyzed seismic patterns are bright spot pattern, right and left pinch-out patterns. From the recognition results, the noisy seismic patterns can be recovered. Seismic pattern recognition can help the analysis and interpretation of seismic data.

Original languageEnglish
Title of host publication2017 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publicationInternational Cooperation for Global Awareness, IGARSS 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3712-3715
Number of pages4
ISBN (Electronic)9781509049516
DOIs
StatePublished - 1 Dec 2017
Event37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 - Fort Worth, United States
Duration: 23 Jul 201728 Jul 2017

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2017-July

Conference

Conference37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
CountryUnited States
CityFort Worth
Period23/07/1728/07/17

Keywords

  • Cellular neural network
  • Pattern recognition
  • Seismic patterns

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