標題: Syntactic Pattern Recognition for Wavelet Clustering in Seismogram
作者: Huang, Kou-Yuan
Leu, Dar-Ren
資訊工程學系
Department of Computer Science
關鍵字: Syntactics;Clustering algorithms;Shape;Clustering methods;Training;Partitioning algorithms;Hierarchical clustering;Levenshtein distance;pattern recognition;seismogram;wavelet
公開日期: 1-七月-2019
摘要: 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
期刊: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume: 12
Issue: 7
起始頁: 2453
結束頁: 2461
顯示於類別:期刊論文