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| Keywords | |
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shear-wave splitting |
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Artificial Fish-School Algorithm |
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fracture identification |
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Pearson correlation coefficients |
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| PubMed | |
Fracture property identification based on Artificial Fish-Swarm Algorithm and Pearson correlation coefficients
LIU Ting1, TIAN You1, ZHU Hong-xiang1, ZHOU Chao2, QIAO Han-qing1
1. College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China;
2. China Railway Eryuan Engineering Group CO. LTD, Chengdu 610031, China
In this study, the Artificial Fish-School Algorithm and Pearson correlation coefficient are combined, and introduced into the identification of fracture properties with shear wave splitting method. The results show that the method can effectively and accurately identify the fracture properties. Compared with model space scanning, particle swarm optimization and genetic algorithm, this method has an improved stability and an increased convergence rate which is three times faster.