Rayleigh surface-wave dispersion curve inversion based on adaptive chaos genetic particle swarm optimization algorithm
YANG Bo1,2,3, XIONG Zhang-qiang1,2,3, ZHANG Dazhou1,2,3, YANG Zhen-tao4
1. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Central South University, Changsha, Hunan 410083, China; 2. Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Changsha, Hunan 410083, China; 3. School of Geosciences and Info-Physics, Central South University, Changsha, Hunan 410083, China; 4. Department of Earth and Space Sciences, Southern University of Sciences and Technology, Shen-zhen, Guangdong 518055, China
Abstract：In order to improve the accuracy of Rayleigh surface wave dispersion curve inversion,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,and the single dimensional 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.
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