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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