Title: Syntactic Pattern Recognition for Wavelet Clustering in Seismogram
Authors: Huang, Kou-Yuan
Leu, Dar-Ren
資訊工程學系
Department of Computer Science
Keywords: Syntactics;Clustering algorithms;Shape;Clustering methods;Training;Partitioning algorithms;Hierarchical clustering;Levenshtein distance;pattern recognition;seismogram;wavelet
Issue Date: 1-Jul-2019
Abstract: In a seismogram, there exist many kinds of wavelets. The reflected wavelet from the gas sand zone has a different shape with other layers. Usually, the information of each wavelet is weak and unknown, and the unsupervised classification method is applied to the clustering of the wavelets. Using the shape structure of the wavelet, syntactic pattern recognition is applied to the clustering. The extracted wavelets can be represented as strings of symbols. Levenshtein distance is used to calculate the distance between the two strings. Bottom-up and top-down hierarchical clustering methods are used in the construction of the dendrogram. The top-down hierarchical clustering by the recursive method is proposed. A new pseudo F-statistics is proposed to decide the optimal number of clusters. From the experimental results in simulated and real seismograms, the wavelets on the gas sand zone can be detected successfully. It can improve the seismic interpretation.
URI: http://dx.doi.org/10.1109/JSTARS.2019.2908690
http://hdl.handle.net/11536/155428
ISSN: 1939-1404
DOI: 10.1109/JSTARS.2019.2908690
Journal: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume: 12
Issue: 7
Begin Page: 2453
End Page: 2461
Appears in Collections:Articles