基于Lévay飞行的粒子群算法在大地电磁反演中的应用

doi:10.11720/wtyht.2023.1330

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摘要

粒子群优化算法在大地电磁测深反演中相较于一般的线性反演算法具有多种优点。然而标准粒子群算法在多维优化问题中存在早熟问题,为此,采用基于Lévy飞行随机游走策略的优化粒子群算法来处理局部最优解,增加寻优能力。通过对地电模型的反演对比表明,改进后的粒子群算法相较于标准粒子群算法适应度值下降速度更快、寻优能力更好。最后将该算法应用于已知钻孔旁实测数据,结果较好,表明该算法具有较好的实用性。

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	张阳阳
	杜威
	王芝水
	缪旭煌
	张翔

关键词 ： Lévy飞行, 大地电磁测深反演, 非线性反演, 粒子群优化算法, 一维有限元正演

Abstract：

Particle swarm optimization algorithm has many advantages compared with linear inversion algorithm in magnetotelluric sounding inversion.However, the standard particle swarm algorithm also suffers from premature maturity in multidimensional optimization problems.Therefore, an optimized particle swarm algorithm based on the Lévy flight randomized wandering strategy is used to escape the local optimal solution,The results show that compared with the standard particle swarm optimization algorithm, the optimized particle swarm algorithm has faster fitness decline and better optimization ability.Finally, the improved particle swarm optimization algorithm is applied to the measured data of known boreholes, and the results show that the algorithm has good practicability.

Key words： Lévy flight magnetotelluric sounding inversion nonlinear inversion particle swarm optimization algorithm one-dimensional finite element forward modeling

收稿日期: 2022-06-30 修回日期: 2022-11-17 出版日期: 2023-08-20

ZTFLH:

P631

基金资助:国家自然青年科学基金项目(41904129)

通讯作者: 杜威(1990-),女,副教授,从事地球物理数据处理与解释工作。Email:duwei@ynu.edu.cn

作者简介: 张阳阳(1989-),男,工程师,从事电磁法数据处理与解释工作。Email:abner361@qq.com

引用本文:

张阳阳, 杜威, 王芝水, 缪旭煌, 张翔. 基于Lévay飞行的粒子群算法在大地电磁反演中的应用[J]. 物探与化探, 2023, 47(4): 986-993.
ZHANG Yang-Yang, DU Wei, WANG Zhi-Shui, MIAO Xu-Huang, ZHANG Xiang. Application of particle swarm algorithm based on Lévy flight in magnetotelluric inversion. Geophysical and Geochemical Exploration, 2023, 47(4): 986-993.

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https://www.wutanyuhuatan.com/CN/10.11720/wtyht.2023.1330 或 https://www.wutanyuhuatan.com/CN/Y2023/V47/I4/986

Fig.1 迭代200次的Lévy飞行示意
a—Lévy飞行轨迹;b—Lévy飞行步长分布

Fig.2 LFPSO算法流程

Fig.3 可变步长有限元法单元节点示意

Fig.4 固定步长和可变步长的有限元正演在简单地电模型(G型)中的正演结果对比
a—视电阻率结果对比;b—阻抗相位结果对比

Fig.5 2层地电模型PSO、LFPSO反演结果与理论值对比
a—G型曲线对比;b—D型曲线对比;c—G型曲线适应度迭代变化;d—D型曲线适应度迭代变化

Fig.6 3层地电模型PSO、LFPSO反演结果与理论值对比
a—H型曲线对比;b—K型曲线对比;c—H型曲线适应度迭代变化;d—K型曲线适应度迭代变化

Table 1 4种不同地电模型PSO、LFPSO算法反演结果

Table 2 6层薄互层地电模型参数

Fig.7 Savitzky-Golay滤波器的滤波效果

Fig.8 OCCAM反演与PSO反演在6层薄互层中的反演结果
a—六层薄互层有限元正演电阻率、相位曲线;b—反演结果

Fig.9 井旁测点实测数据反演结果

[1]	李金铭. 地电场与电法勘探[M]. 北京: 地质出版社, 2007.
[1]	Li J M. Geoelectric field and electrical exploration[M]. Beijing: Geology Press, 2007.
[2]	冯思臣. 一维大地电磁测深反演算法比较研究[D]. 成都: 成都理工大学, 2007.
[2]	Feng S C. A comparative study of one dimensional magnetotelluric sounding inversion algorithms[D]. Chengdu: Chengdu University of Technology, 2007.
[3]	石昆法. 可控源音频大地电磁法理论与应用[M]. 北京: 科学出版社, 1999.
[3]	Shi K F. Theory and application of controlled source audio magnetotelluric method[M]. Beijing: Science Press, 1999.
[4]	汤井田, 任政勇, 化希瑞. 地球物理学中的电磁场正演与反演[J]. 地球物理学进展, 2007, 22(4):1181-1194.
[4]	Tang J T, Ren Z Y, Hua X R. The forward modeling and inversion in geophysical electromagnetic field[J]. Progress in Geophysics, 2007, 22(4):1181-1194.
[5]	王光杰, 王勇, 李帝铨, 等. 基于遗传算法CSAMT反演计算研究[J]. 地球物理学进展, 2016, 21(4):1285-1289.
[5]	Wang G J, Wang Y, Li D Q, et al. Research on inversion calculation of CSAMT based on genetic algorithm[J]. Progress in Geophysics, 2016, 21(4):1285-1289.
[6]	孙彩堂, 李玲, 黄维宁, 等. 基于自适应遗传算法的CSAMT一维反演[J]. 石油地球物理勘探, 2017, 52(2):392-397.
[6]	Sun C T, Li L, Huang W N, et al. One-dimensional inversion of CSAMT based on adaptive genetic algorithm[J]. Oil Geophysical Prospecting, 2017, 52(2):392-397.
[7]	蒋龙聪, 刘江平. 模拟退火算法及其改进[J]. 工程地球物理学报, 2007, 4(2):135-140.
[7]	Jiang L C, Liu J P. Revised simulated annealing algorithm[J]. Chinese Journal of Engineering Geophysics, 2007, 4(2):135-140.
[8]	师学明, 王家映. 一维层状介质大地电磁模拟退火反演法[J]. 地球科学, 1998, 7(5):542-545.
[8]	Shi X M, Wang J Y. One dimensional magnetotelluric sounding inversion using simulated annealing[J]. Earth Science, 1998, 7(5):542-545.
[9]	曾志文, 陈晓, 郭冬, 等. 双种群人工蜂群算法及其在MT和重力联合反演中的应用[J]. 石油地球物理勘探, 2021, 56(6):1400-1408.
[9]	Zeng Z W, Chen X, Guo D, et al. Dual-population artificial bee colony algorithm and its application in joint inversion of magnetotelluric and gravity data[J]. Oil Geophysical Prospecting, 2021, 56(6):1400-1408.
[10]	谢卓良, 王绪本, 李德伟, 等. 基于混沌天牛群算法的大地电磁反演[J]. 物探化探计算技术, 2022, 44(1):41-50.
[10]	Xie Z L, Wang X B, Li D W, et al. Magnetotelluric inversion based on chaotic beetle swarm algorithm[J]. Computing Techniques for Geophysical and Geochemical Exploration, 2022, 44(1):41-50.
[11]	徐正玉, 付能翼, 周洁, 等. 瞬变电磁法非线性优化反演算法对比[J]. 吉林大学学报:地球科学版, 2022, 52(3):744-753.
[11]	Xu Z Y, Fu N Y, Zhou J, et al. Comparison of nonlinear optimization and inversion algorithms of transient electromagnetic method[J]. Journal of Jilin University:Earth Science Edition, 2022, 52(3):744-753.
[12]	王一鸣, 宋先海, 张学强. 基于蚁狮优化算法的瑞雷波频散曲线反演[J]. 地质科学通报, 2022, 42(3):331-337.
[12]	Wang Y M, Song X H, Zhang X Q. Inversion of the dispersion curve of Rayleigh wave based on antlion optimizer algorithm[J]. Bulletin of Geological Science and Technology, 2022, 42(3):331-337.
[13]	Kennedy J, Eberhart R. Particle swarm optimization[C]// Piscataway: Proceeding of IEEE International Conference on Neural Networks,IEEE CS, 1995:1942-1948.
[14]	刘建华. 粒子群算法的基本理论及其改进研究[D]. 长沙: 中南大学, 2009.
[14]	Liu J H. The research of basic theory analysis and imporvement on particle swarm optimization[D]. Changsha: Central South University, 2009.
[15]	陈先洁, 王绪本, 李德伟, 等. 基于改进粒子群算法的大地电磁阻抗张量分解方法[J]. 物探化探计算技术, 2021, 43(5):620-627.
[15]	Chen X J, Wang X B, Li D W, et al. Tensor decomposition method of magnetotelluric impedance based on particle swarm optimization[J]. Computing Techniques for Geophysical and Geochemical, 2021, 43(5):620-627.
[16]	Francesca P, Alessandro S, Alberto G. A review of geophysical modeling based on particle swarm optimization[J]. Surveys in Geophysics, 2021, 42(3):505-549. doi: 10.1007/s10712-021-09638-4
[17]	师学明, 肖敏, 范建柯, 等. 大地电磁阻尼粒子群优化反演法研究[J]. 地球物理学报, 2009, 52(4):1114-1120.
[17]	Shi X M, Xiao M, Fan J K, et al. The damped PSO algorithm and its application for magnetotelluric sounding data inversion[J]. Chinese Journal of Geophysics, 2009, 52(4):1114-1120.
[18]	肖敏. 二维大地电磁粒子群优化算法反演方法研究[D]. 武汉: 中国地质大学(武汉), 2011.
[18]	Xiao M. Research on inversion method of magnetotelluric damped particle swarm optimization[D]. Wuhan: China University of Geosciences(Wuhan), 2011.
[19]	韩家兴, 吴施楷, 田仁飞, 等. 基于粒子群优化算法的多元线性拟合方法研究及其应用[J]. 物探化探计算技术, 2016, 38(2):212-218.
[19]	Han J X, Wu S K, Tian R F, et al. The particle swarm optimization research and application based on multivariate linear fitting method[J]. Computing Techniques for Geophysical and Geochemical Exploration, 2016, 38(2):212-218.
[20]	李明星, 肖林通, 张倚瑞, 等. 瞬变电磁粒子群优化反演研究[J]. 煤炭技术, 2014, 33(9):302-304.
[20]	Li M X, Xiao L T, Zhang Y R, et al. Research on particle swarm optimization inversion of transient electromagnetic method[J]. Coal Technology, 2014, 33(9):302-304.
[21]	Kennedy J. The particle swarm:social adaptation of knowledge[C]// Indianapolis: IEEE International Conference on Evolutionary Computation, 1997.
[22]	Mantegna R N. Fast,accurate algorithm for numerical simulation of Lévy stable stochastic processes[J]. Physical Review E, 1994, 49(5):4677. doi: 10.1103/PhysRevE.49.4677
[23]	吴小平, 徐果明. 大地电磁数据的Occam反演改进[J]. 地球物理学报, 1998, 41(4):547-554.
[23]	Wu X P, Xu G M. Improvement of Occam’s inversion for MT data[J]. Chinese Journal of Geophysics, 1998, 41(4):547-554.
[24]	吴小平, 徐果明, 卫山, 等. 利用新的MT视电阻率定义识别薄互层[J]. 石油地球物理勘探, 1998, 33(3):328-335.
[24]	Wu X P, Xu G M, Wei S, et al. Defining and identifying thin interbeds by using new MT apparent resistivity[J]. Oil Geophysical Prospecting, 1998, 33(3):328-335.
[25]	梁生贤, 吾守艾力·肉孜, 廖国忠, 等. 大地电磁线性反演算法比较[J]. 地球物理学进展, 2014, 29(6):2702-2707.
[25]	Liang S X, Wu-shou-ai-li·R Z, Liao G Z, et al. Comparison and analysis of two-dimensional linear algorithm inversion for magnetoteluric[J]. Progres in Geophysics, 2014, 29(6):2702-2707.

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