Inversion of Rayleigh wave dispersion curves based on particle swarm and ant colony hybrid optimization
WANG Yiming1, SONG Xianhai1,2, ZHANG Xueqiang1
1. Institute of Geophysics & Geomatics, China University of Geosciences (Wuhan), Wuhan, Hubei 430074, China; 2. Hubei Subsurface Multiscale Image Key Laboratory, China University of Geosciences (Wuhan), Wuhan, Hubei 430074, China
Abstract:Inversion of the Rayleigh surface-wave dispersion curve to obtain the shear-wave (S-wave) velocity profile of an underground medium is one of the important steps in surface-wave exploration. The traditional linear inversion method cannot meet the needs of geophysical exploration, and the nonlinear inversion method has instead become a research hotspot. In this paper, a nonlinear optimization algorithm based on the particle swarm optimization (PSO) algorithm and the ant colony optimization (ACO) algorithm is applied to the nonlinear inversion of the Rayleigh surface-wave dispersion curve to obtain the underground S-wave velocity profile. This algorithm uses the pheromone guidance mechanism to update the positions of particles in the early stage. It fully combines the guidance strategy of the PSO algorithm for the global optimal solution with the local search ability of the ACO algorithm. Meanwhile, it overcomes the shortcoming of the PSO algorithm that particle swarm update comes to a standstill when the population is in a state of equilibrium and the defect of the ACO algorithm that convergence is premature when it is applied to solve the multi-extremum function. The effectiveness and stability of the proposed algorithm are examined by the inversion of the dispersion curves of various theoretical mo-dels; the comparison of the inversion results of this algorithm with those of the ACO and PSO algorithms alone verifies its superiority; Inversion results of measured data further test the practicability of this algorithm.
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WANG Yiming, SONG Xianhai, ZHANG Xueqiang. Inversion of Rayleigh wave dispersion curves based on particle swarm and ant colony hybrid optimization. Oil Geophysical Prospecting, 2022, 57(2): 303-310,356.
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