Syntactic pattern recognition for wavelet clustering in seismogram

Kou-Yuan Huang, Dar Ren Leu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In a seismogram there exist many kinds of wavelets. The reflected wavelet from the gas sand zone has different shape with other layers. We use shape structure of the wavelet in the analysis. Syntactic pattern recognition is applied to the clustering of wavelets in the seismogram. The extracted wavelets can be represented as the strings of symbols. We use Levenshtein distance to calculate the distance between two strings. Then we can construct the hierarchical clustering of the wavelets. Top down hierarchical clustering by recursive method is proposed. A new pseudo F-statistics (NPFS) is proposed to decide the optimal number of clusters. From the experimental results in seismogram the wavelets on the gas sand zone can be detected successfully. It can improve the seismic interpretation.

Original languageEnglish
Title of host publication2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7149-7152
Number of pages4
ISBN (Electronic)9781538671504
DOIs
StatePublished - 31 Oct 2018
Event38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain
Duration: 22 Jul 201827 Jul 2018

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2018-July

Conference

Conference38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
CountrySpain
CityValencia
Period22/07/1827/07/18

Keywords

  • Hierarchical clustering
  • Levenshtein distance
  • Seismogram
  • Syntactic method
  • Wavelet

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