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利用自适应混沌遗传粒子群算法反演瑞雷面波频散曲线

杨博1,2,3 熊章强1,2,3 张大洲1,2,3 杨振涛4

(1.中南大学有色金属成矿预测与地质环境监测教育部重点实验室, 湖南长沙 410083)
(2.有色资源与地质灾害探查湖南省重点实验室, 湖南长沙 410083)
(3.中南大学地球科学与信息物理学院, 湖南长沙 410083)
(4.南方科技大学地球与空间科学系, 广东深圳 518055)

【摘要】为了提高瑞雷面波频散曲线的反演精度,减少反演过程中的多解性,获取更准确的地下横波速度结构,本文从反演算法入手,对基本的粒子群算法进行改进,提出了一种能同步提高全局和局部搜索能力的自适应混沌遗传粒子群算法(ACGPSO):即先采用自适应惯性权重,并设置粒子的节速度,再引入遗传算法的交叉和变异操作及单维全分量的混沌局部搜索。利用该算法对理论模型的无噪和含噪基阶频散曲线进行反演,且针对含噪数据加入二阶与三阶频散曲线进行联合反演。所得反演结果与常规粒子群算法反演结果的对比表明:ACGPSO算法具有更好的稳定性和抗噪性,且基于该算法的联合反演能有效降低解的多解性,显著提高解的精度。对实际数据所做的两步法反演的效果进一步验证了该算法的适用性。

【关键词】 瑞雷面波;频散曲线;粒子群算法;自适应混沌遗传粒子群算法;联合反演;

【DOI】

【基金资助】 国家自然科学基金项目“近地表三维复杂介质中瑞雷波传播特性研究”(41274123); 国家重点研发计划“深地资源勘查开采”重点专项“深部资源勘查数据处理、解释软件平台开发及综合示范”(2018YFC0603600); 中南大学中央高校基本科研业务费专项资金资助项目“快速多模式瑞雷面波频散曲线正反演研究”(502211928);

Rayleigh surface-wave dispersion curve inversion based on adaptive chaos genetic particle swarm optimization algorithm

YANG Bo1,2,3 XIONG Zhangqiang1,2,3 ZHANG Dazhou1,2,3 YANG Zhentao4

(1.Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha, Hunan Province, China 410083)
(2.Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Changsha, Hunan Province, China 410083)
(3.School of Geosciences and Info-physics, Central South University, Changsha, Hunan Province, China 410083)
(4.Department of Earth and Space Sciences, Southern University of Sciences and Technology, Shenzhen, Guangdong, China 518055)

【Abstract】To improve the inversion accuracy of Rayleigh wave dispersion curves, reduce multi-solutions in the inversion, and obtain a more accurate subsurface shear wave velocity, we propose an adaptive chaotic genetic particle swarm optimization algorithm (ACGPSO) which can simultaneously improve the global and local search capabilities. ACGPSO adopts adaptive inertia weights, sets knots of particles, and introduces the crossover and mutation operation of genetic algorithm as well as the single-dimensional and full-component chaotic local search. With the proposed algorithm, fundamental dispersion curves of a theoretical geological model without noise and with noise are inverted, and the first-order and second-order dispersion curves with noise are jointly inverted. Based on the numerical test, the proposed ACGPSO algorithm has better stability and better noise-resistance than conventional algorithms, and its joint inversion can effectively reduce multi-solutions. Real data tests prove the applicability of the proposed algorithm.

【Keywords】 Rayleigh surface wave; dispersion curve; particle swarm optimization algorithm; adaptive chaos genetic particle swarm optimization algorithm (ACGPSO); joint inversion;

【DOI】

【Funds】 National Natural Science Foundation of China (41274123); National Key R&D Program of China (2018YFC0603600); Fundamental Research Funds for the Central Universities of Central South University (502211928);

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This Article

ISSN:1000-7210

CN: 13-1095/TE

Vol 54, No. 06, Pages 1217-1227+1172

December 2019

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Article Outline

Abstract

  • 0 Introduction
  • 1 Principle and modification of PSO
  • 2 Calculation of theoretical model
  • 3 Inversion of field data
  • 4 Conclusions
  • References