摘要
将人工鱼群算法和Pearson相关系数结合,引入到进行裂缝属性识别的横波分裂方法中。结果表明,本文使用的方法能快速准确地识别裂缝属性,相比于模型空间扫描、粒子群算法及遗传算法等识别方法,收敛速度增快了约3倍,稳定性上也有所提高。
In this study, the Artificial Fish-School Algorithm and Pearson correlation coefficient are com- bined, and introduced into the identification of fracture properties with shear wave splitting method. The resultsshow 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 in- creased convergence rate which is three times faster.
出处
《世界地质》
CAS
2017年第1期293-298,共6页
World Geology
基金
国家自然科学基金项目(41430322
41474030
41174068)
吉林省重点科技攻关项目(20150204002SF)
地质调查项目(DD20160207)
吉林大学基本科研业务费项目联合资助
关键词
横波分裂
人工鱼群算法
裂缝识别
Pearson相关系数
shear-wave splitting
Artificial Fish-School Algorithm
fracture identification
Pearson correla-tion coefficients
