基于改进粒子群算法的SOTEM电场分量E_(x)反演  

Inversion of electric field component E_(x) in the SOTEM method based on the improved particle swarm optimization algorithm

作　　者：张继锋[1,2,3] 陈昌涧 李宇腾 游希然 马子奔 ZHANG Jifeng;CHEN Changjian;LI Yuteng;YOU Xiran;MA Ziben(College of Geological Engineering and Geomatics,Chang’an University,Xi’an 710054,China;National Engineering Research Center for Offshore Oil and Gas Exploration,Beijing 100028,China;Integrated Geophysical Simulation Lab of Chang’an University,Xi’an 710054,China;CCTEG Xi’an Research Institute(Group)Co.,Ltd.,Xi’an 710077,China;CCTEG China Coal Research Institute,Beijing 100013,China)

机构地区：[1]长安大学地质工程与测绘学院,陕西西安710054 [2]海洋油气勘探国家工程研究中心,北京100028 [3]长安大学地球物理场多参数综合模拟实验室,陕西西安710054 [4]中煤科工西安研究院(集团)有限公司,陕西西安710077 [5]煤炭科学研究总院,北京100013

出　　处：《煤田地质与勘探》2025年第4期213-221,共9页Coal Geology & Exploration

基　　金：国家自然科学基金项目(42174168);陕西省自然科学基础研究计划项目青年基金项目(2024JC-YBQN-0331);中煤科工西安研究院(集团)有限公司科技创新基金项目(2024XAYKF01)。

摘　　要：【目的】针对电性源短偏移距瞬变电磁法(SOTEM)水平电场分量反演中传统算法易陷入局部极值的问题,提出一种融合重心反向学习策略的改进粒子群算法。【方法】该算法通过引入重心反向学习策略,动态调整学习因子和自适应惯性权重,有效提升了全局搜索能力与收敛效率。研究构建了三层、五层及七层典型地电模型,来验证算法性能。【结果和结论】研究结果表明:对于五层和七层地电模型,阻尼最小二乘算法的反演平均误差分别为0.34%和4.68%,改进粒子群算法反演平均误差分别为0.21%和0.87%,可见改进粒子群算法反演对复杂地电结构的识别精度提升显著。在多层数(≥5)及宽泛参数搜索区间条件下,三层和五层地电模型反演平均误差均小于5%,验证了改进粒子群算法的有效性。利用某区实测数据进行阻尼最小二乘反演和改进粒子群算法反演,改进的粒子群算法较阻尼最小二乘算法有着较好的反演效果,反演结果与已知矿体的电性结构吻合较好,研究成果为提高SOTEM在矿产勘探中的分辨率提供了理论支持。[Objective] The inversion of the horizontal electric field component in the grounded-source short offset transient electromagnetic(SOTEM) method using traditional algorithms is prone to fall into local extrema.To address this challenge,this study proposed an improved particle swarm optimization(PSO) algorithm that integrates the center of gravity reverse learning strategy.[Methods] Based on the center of gravity reverse learning strategy,the improved PSO algorithm can dynamically adjust learning factors and the value of the adaptive inertia weight,thus improving the global search capability and convergence efficiency effectively.The performance of the improved PSO algorithm was verified using the typical three-,five-,and seven-layered geoelectric models constructed in this study.[Results and Conclusions] The results of this study indicate that for the five-and seven-layered geoelectric models,the damped least squares method yielded average inversion errors of 0.34% and 4.68%,respectively,while the improved PSO algorithm yielded average inversion errors of 0.21% and 0.87%,respectively.This suggests that the improved PSO algorithm significantly improved the identification accuracy of complex geoelectric structures.Under the conditions of multi-layer(≥5) initial inversion intervals and wide search intervals,the improved PSO algorithm yielded average inversion errors of less than5% for both three-and five-layered geoelectric models,substantiating its effectiveness.Inversion was conducted for the measured data from a certain mining area using the damped least squares method and the improved PSO algorithm.The inversion results demonstrate that the improved PSO algorithm outperformed the damped least squares method,with the inversion results of the improved PSO algorithm agreeing well with the electrical structure of the known ore body.The results of this study will provide theoretical support for improving the resolution of SOTEM in mineral exploration.

关 键 词：电性源短偏移距瞬变电磁法 粒子群算法 反演 电场分量Ex 

分 类 号：P631[天文地球—地质矿产勘探]