Title :
Simulated annealing for pattern detection and seismic analysis
Author :
Huang, Kou-Jen ; Huang, Kou-Yuan ; Wang, Luke K. ; Chou, Ying-Liang ; Hsieh, Yueh-Hsun ; Hsieh, Shan-Chih
Author_Institution :
Dept. of Electr. Eng., Nat. Kaohsiung Univ. of Appl. Sci., Kaohsiung, Taiwan
Abstract :
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 (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 Vrms. The Vrms 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.
Keywords :
geophysical signal processing; image reconstruction; least mean squares methods; object detection; probability; seismic waves; seismology; simulated annealing; common depth reflection point; direct wave; global minimum error; hyperbolic pattern detection; image reconstruction; normal-moveout correction; parameter detection; probability; reflection wave; root-mean-squared velocity; seismic data processing; seismic velocity analysis; simulated annealing; Analytical models; Computer science; Data processing; Equations; Image reconstruction; Neural networks; Pattern analysis; Reflection; Simulated annealing; Stacking;
Conference_Titel :
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-3548-7
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2009.5179090