Particle swarm optimization for Rayleigh wave frequency-velocity spectrum inversion

Le，Zhao1; Song，Xianhai1; Zhang，Xueqiang1; Shen，Chao2; Ai，Hanbing1; Yuan，Shichuan3; Fu，Daiguang4

2024-03-01

10.1016/j.jappgeo.2024.105311

Journal of Applied Geophysics   影响因子和分区

0926-9851

Rayleigh wave exploration is capable of reconstructing seismic properties at high resolution. The dispersion curve inversion methods are mainly based on 1D layered-model assumptions and require picking the dispersion curves manually. The success is highly dependent on personal subjectivity. Full waveform inversion (FWI) has received particular attention in recent years due to the fact that it has no need for manual dispersion curve extraction and no restriction on medium distribution. However, there are still many challenges limiting the application of FWI in field data, e.g., cycle-skipping issues, dependence on the initial model, and difficulty in estimating the seismic source signature. We adapt a particle swarm optimization (PSO) algorithm for Rayleigh wave frequency-velocity spectrum (FVS) inversion. Compared with conventional dispersion curve inversion, we directly fit the FVS without manually extracting the dispersion curves. Compared with FWI, we avoid cycle-skipping issues by transforming the observed data from the space-time domain to the frequency-velocity domain. We reduce the dependence on the initial model by adopting a global optimal inversion strategy, and improve the inversion efficiency by introducing GPU parallel computing. In addition, we find that the seismic source dependence of FVS inversion is much less than that of FWI. Both synthetic and field examples verify the effectiveness of the method, making it a valuable tool for retrieving the subsurface structure.

Frequency-velocity spectrum inversion GPU parallel computing Low source dependence Particle swarm optimization Rayleigh wave

SCI ; EI

英语

其他

GEOSCIENCES

2-s2.0-85184141864

Scopus

1.School of Geophysics and Geomatics,China University of Geosciences,Wuhan,Hubei,China
2.College of Metrology & Measurement Engineering,China Jiliang University,Hangzhou,Zhejiang,China
3.Department of Earth and Space Sciences,Southern University of Science and Technology,Shenzhen,Guangdong,China
4.Key Laboratory of Geotechnical Mechanics and Engineering of the Ministry of Water Resources,Changjiang River Scientific Research Institute,Wuhan,Hubei,China

Le，Zhao,Song，Xianhai,Zhang，Xueqiang,et al. Particle swarm optimization for Rayleigh wave frequency-velocity spectrum inversion[J]. Journal of Applied Geophysics,2024,222.

Le，Zhao.,Song，Xianhai.,Zhang，Xueqiang.,Shen，Chao.,Ai，Hanbing.,...&Fu，Daiguang.(2024).Particle swarm optimization for Rayleigh wave frequency-velocity spectrum inversion.Journal of Applied Geophysics,222.

Le，Zhao,et al."Particle swarm optimization for Rayleigh wave frequency-velocity spectrum inversion".Journal of Applied Geophysics 222(2024).