Inversion of ambient seismic noise based on particle swarm optimization combined with local optimization

Extracting the Rayleigh wave dispersion curve from the ambient seismic noise and obtaining the shear wave velocity structure through inversion has been extensively used in large-scale underground structure detection and small-scale engineering exploration. The inversion misfit function based on the dispersion function can effectively solve the mode misjudgment problem in the joint inversion of multi-mode dispersion curves. However, its widely distributed local extrema lead to more serious multi-solution when searching in a wide range of parameters. It is sometimes difficult to obtain the optimal solution, and an optimization algorithm with strong global search performance is required. This paper proposes a particle swarm optimization algorithm combined with local optimization (PSOG). It introduces a local method in the particle iteration process to increase the diversity of the population, avoid falling into local extreme values and speed up the convergence speed. In order to verify the effectiveness of the new algorithm, theoretical synthetic data are inverted, combined with the objective function based on the secular function. The results show that the stability and accuracy of PSOG algorithm are significantly improved compared with the traditional algorithm. The ambient seismic noise of the Suzhou River region in Shanghai was processed, and the weak layer caused by the cutting of the ancient channel was successfully imaged. The combination of the PSOG algorithm and the new inversion misfit function has great potential in the engineering application of ambient seismic noise exploration.