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 |