Browsing by Author "Leu, Dar-Ren"

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SYNTACTIC PATTERN RECOGNITION FOR WAVELET CLUSTERING IN SEISMOGRAM
(2018-01-01)
Huang, Kou-Yuan
;
Leu, Dar-Ren
;
資訊工程學系
;
Department of Computer Science
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.
Syntactic Pattern Recognition for Wavelet Clustering in Seismogram
(2019-07-01)
Huang, Kou-Yuan
;
Leu, Dar-Ren
;
資訊工程學系
;
Department of Computer Science
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.
Syntactic Pattern Recognition for Wavelet Clustering in Seismogram
(2019-07-01)
Huang, Kou-Yuan
;
Leu, Dar-Ren
;
資訊工程學系
;
Department of Computer Science
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.