Browsing by Author "Chou, Ying-Liang"

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SEISMIC HYPERBOLIC PATTERN DETECTION AND VELOCITY ANALYSIS BY SIMULATED ANNEALING
(2009)
Huang, Kuo-Chen
;
Huang, Kou-Yuan
;
Wang, Luke K.
;
Chou, Ying-Liang
;
Hsieh, Yueh-Hsun
Simulated annealing (SA) is adopted to detect the parameters of line, circle, ellipse, and hyperbola. The equation of pattern is defined under translation and rotation. The parameters of the pattern are learned with probability in SA The proposed SA parameter detection system can search a set of parameter vectors for the global minimal error. In the seismic experiments, the system can well detect line of direct wave and hyperbola of reflection wave in the real seismic data. In the seismic data processing, the reflection curves on common depth reflection point (CDP) gathers are hyperbolic patterns. So using SA, the parameters of each hyperbolic pattern can be detected. The parameters are used to calculate the root-mean-squared velocity V-rms. The V-rms is used to the normal-moveout (NMO) correction and stacking to reconstruct the image of the subsurface. Using the result of SA hyperbolic parameter detection, it is a novel method in the seismic velocity analysis.
Sequential Simulated Annealing System for Pattern Detection
(2008)
Huang, Kou-Yuan
;
Chou, Ying-Liang
;
資訊工程學系
;
Department of Computer Science
A sequential system is proposed by using simulated annealing for the detection of lines, circles, ellipses, and hyperbolas in image. The sequential detection procedures are type by type and patterns by patterns. The equation of ellipse and hyperbola is defined under translation and rotation. The distance from all points to all patterns is defined as the error. Using simulated annealing for parameter detection can search a set of parameter vectors for the global minimal error. We also propose synchronous simulated annealing detection system to compare with sequential system. In the experiments, using the sequential system, the result of a large number of simulated and real image patterns detection is better than that of using the synchronous system.
Simulated Annealing for Hierarchical Pattern Detection and Seismic Applications
(2008)
Huang, Kou-Yuan
;
Chou, Ying-Liang
;
資訊工程學系
;
Department of Computer Science
A Hierarchical system is proposed by using simulated annealing for the detection of fines, circles, ellipses, and hyperbolas in image. The hierarchical detection procedures re type by type and pattern by pattern. The equation of ellipse and hyperbola is defined under translation and rotation. distance from all points to all patterns is defined as the error. Also we use the minimum error to determine the number of patterns. The proposed simulated annealing parameter detection system can search a set of parameter vectors for the global minimal error. In the experiments, using the hierarchical system, the result of the detection of a large number of simulated image patterns is better than that of using the synchronous system. In the seismic experiments, both of two systems can well detect line of direct wave and hyperbola of reflection wave in the simulated one-shot seismogram and the real seismic data, but the hierarchical system can converge faster. The results of seismic pattern detection can improve seismic interpretation and further seismic data processing.
Simulated Annealing for Pattern Detection and Seismic Analysis
(2009)
Huang, Kou-Jen
;
Huang, Kou-Yuan
;
Wang, Luke K.
;
Chou, Ying-Liang
;
Hsieh, Yueh-Hsun
Simulated annealing (SA) is adopted to detect the parameters of line, circle, ellipse, and hyperbola. The equation of pattern is defined under translation and rotation. The distance from all points to all patterns is defined as the system error. Also we use the minimum error to determine the number of patterns. The parameters of the pattern are learned with probability in SA. The proposed SA parameter detection system can search a set of parameter vectors for the global minimal error. In the seismic experiments, the system can well detect line of direct wave and hyperbola of reflection wave in the real seismic data. In the seismic data processing, the reflection curves on common depth reflection point (CUP) gathers are hyperbolic patterns. So using SA, the parameters of each hyperbolic pattern can be detected. The parameters are used to calculate the root-mean-squared velocity V(rms). The V(rms) is used to the normal-moveout (NMO) correction and stacking to reconstruct the image of the subsurface. Using the result of SA hyperbolic parameter detection, it is a novel method in the seismic velocity analysis.