摘要
大地电磁反演是非线性问题,传统的大地电磁反演采用线性反演,结果往往会陷入局部最优,为此,在标准BAS算法的基础上,引入混沌种群概念、指导性学习策略及竞技场策略,实现带学习和竞争机制的混沌天牛群搜索算法(LCCBSA)。利用LCCBSA算法、粒子群算法和遗传算法分别对测试函数试算,结果表明该算法与其他算法相比具有较快的收敛速度和寻优能力。大地电磁理论地电模型的反演试算表明,LCCBSA算法能够较好地寻找到全局最优解,并且通过与多尺度反演结果对比检验了该算法恢复地下深部低阻层的能力。最后,利用该算法反演了塔里木盆地的实测数据,反演剖面和Bostick反演结果基本一致,说明LCCBSA算法可以用于处理大地电磁实际资料,是一种可行的非线性反演方法。
Magnetotelluric(MT)inversion is a nonlinear problem.Traditional magnetotelluric inversion uses linear inversion,and the results often fall into local optimum.Therefore,based on the standard bas algorithm,we add the concept of chaotic population,guiding learning strategy and arena strategy to propose the learning and competing chaos beetle swarm algorithm(LCCBSA)in this paper.The LCCBSA algorithm,particle swarm optimization algorithm(PSO)and genetic algorithm(GA),is used to calculate the test function respectively.The results show that the algorithm has faster convergence speed and optimization ability compared with other algorithms.The inversion test of magnetotelluric theory geoelectric model shows that the LCCBSA algorithm can well find the global optimal solution,and through the comparison with multi-scale inversion results,the ability of the LCCBSA algorithm to restore deep underground low resistivity layer is tested.Finally,the measured data of Tarim Basin are inversed by this algorithm,and the inversion profile is basically consistent with Bostick inversion profile,which indicates that LCCBSA algorithm can be used to process the actual magnetotelluric data,and it is a feasible nonlinear inversion method.
作者
谢卓良
王绪本
李德伟
陈先洁
乃国茹
XIE Zhuoliang;WANG Xuben;LI Dewei;CHEN Xianjie;NAI Guoru(Chengdu University of Technology,Institute of Geophysics,Chengdu 610059,China)
出处
《物探化探计算技术》
CAS
2022年第1期41-50,共10页
Computing Techniques For Geophysical and Geochemical Exploration
基金
国家自然科学基金(41674078)。
关键词
混沌天牛群搜索算法
竞技场机制
大地电磁
反演
Chaotic beetle swarm search algorithm
arena mechanism
magnetotelluric
inversion
