应用多策略改进量子粒子群算法的直流电与Rayleigh波联合反演  

Joint inversion of direct current and Rayleigh waves with a multi-strategy improved quantum-behaved particle swarm optimization algorithm

作　　者：朱春光 管泓清 秦天 张富翔 王强[1] 高远 ZHU Chunguang;GUAN Hongqing;QIN Tian;ZHANG Fuxiang;WANG Qiang;GAO Yuan(Langfang Comprehensive Survey Center of Natural Resources,China Geological Survey,Langfang,Hebei 065000,China;School of Geology Engineering and Geomatics,Chang’an University,Xi’an,Shaanxi 710054,China;Lishu Field Scientific Observation and Research Station for Earth Critical Zone on Black Soil,China Geological Survey,Langfang,Hebei 065000,China)

机构地区：[1]中国地质调查局廊坊自然资源综合调查中心,河北廊坊065000 [2]长安大学地质工程与测绘学院,陕西西安710054 [3]中国地质调查局梨树黑土地地球关键带野外科学观测研究站,河北廊坊065000

出　　处：《石油地球物理勘探》2025年第1期137-151,共15页Oil Geophysical Prospecting

基　　金：中国地质调查局项目“辽河平原梨树地区黑土地地表基质层调查”(DD20211590)资助。

摘　　要：针对浅地表地质分层问题,文中分析了直流电(DC)法与Rayleigh波(RW)法共同探测并进行数据联合反演的可行性,重点研究了融合多种优化策略后形成的基于重心反向学习(Centroid Opposition-Based Learning,COBL)和混沌搜索(Chaos Search,CS)的量子行为粒子群(Quantum-behaved Particle Swarm Optimization,QPSO)算法(简称为COBL-CS-QPSO算法)应用于二者的一维联合反演。通过联合反演可以从电阻率数据中提取层厚信息,弥补单独Rayleigh波反演难以精确解析层厚的问题;同时多策略算法的引入使解在搜索过程中不易陷入局部最优,并加强了不确定环境下的随机搜索效率。理论模型实验考虑了无噪声与有噪声以及已知模型层数与未知模型层数的多种情况,并使模型反演在宽泛的搜索区间内进行,最终取得了良好的反演效果。随后将该联合反演算法应用于实际数据,结果表明基于COBL-CS-QPSO算法的直流电与Rayleigh波联合反演在无钻孔信息或未知地下详细分层的条件下,能够获得相比于单独方法更为准确的结果。同时与自适应粒子群(APSO)算法的对比也体现了改进算法的反演优势。This paper addresses the challenge of shallow subsurface geological stratification by investigating the feasibility of joint inversion using direct current(DC)resistivity data and Rayleigh wave data.The study focuses on applying the quantum-behaved particle swarm optimization(QPSO)algorithm enhanced with centroid opposition-based learning(COBL)and chaos search(CS),named the COBL-CS-QPSO algorithm,which integrates multiple optimization strategies,in one-dimensional joint inversion of the two methods.The joint inversion approach enables the extraction of layer thickness information from resistivity data,thereby overcoming the limitations of Rayleigh wave inversion in accurately resolving layer thickness.The incorporation of multi-strategy algorithms mitigates the risk of solutions becoming trapped in local optima during the search process and improves the efficiency of random searches under uncertain conditions.In theoretical model settings,various scenarios are examined,including cases with and without noise,as well as with known and unknown model layer numbers.The inversion is performed over a broad search range,yielding favorable results.Subsequently,the joint inversion algorithm is applied to actual data.The results demonstrate that the joint inversion of the DC resistivity and Rayleigh wave with the COBL-CS-QPSO algorithm produces more accurate outcomes than singlemethod inversions under field conditions lacking borehole data or detailed subsurface stratification.Furthermore,a comparison with the adaptive particle swarm optimization(APSO)algorithm highlights the advantages of the improved algorithm in inversion performance.

关 键 词：Rayleigh 波法 直流电法 联合反演 量子行为粒子群算法 重心反向学习 混沌搜索 无限折叠的迭代混 沌映射 浅地表 

分 类 号：P631[天文地球—地质矿产勘探]