南方科技大学知识苑(SUSTech KC): Multi-objective particle swarm optimization for Rayleigh wave full waveform inversion

题名	Multi-objective particle swarm optimization for Rayleigh wave full waveform inversion
作者	Le，Zhao 1; Song，Xianhai 1; Zhang，Xueqiang 1; Shen，Chao 2; Shi，Xueming 1; Wang，Yiming 1; Yuan，Shichuan3
通讯作者	Song，Xianhai
发表日期	2023-08-01
DOI	10.1016/j.jappgeo.2023.105096
发表期刊	Journal of Applied Geophysics 影响因子和分区
ISSN	0926-9851
EISSN	1879-1859
卷号	215
摘要	Conventional full waveform inversion (FWI) of Rayleigh wave updates the earth model iteratively by minimizing the difference between measured and synthetic data using single-objective function. However, FWI of Rayleigh wave is challenging due to its high nonlinearity and multimodality. Thus, single-objective inversion may lead to incorrect results, and multi-objective inversion is attractive to obtain global optimal solution. In this paper, we propose a multi-objective particle swarm optimization (MOPSO) scheme for FWI of Rayleigh wave. During the MOPSO inversion, the model is optimized iteratively by using multiple criteria simultaneously. We adopt four objective functions in our proposed MOPSO strategy to characterize the observed data: waveform, peak time, waveform envelope and phase velocity spectrum. The concept of Pareto optimal solution set is introduced in the MOPSO strategy to avoid the contradiction of multi-objective functions. Each non-dominated solution is recorded on the Pareto optimal set for model iteration. Both synthetic data and real field data demonstrate the effectiveness and practicability of the MOPSO strategy.
关键词	Full waveform inversion Multi-objective optimization Particle swarm optimization Rayleigh wave
相关链接	[Scopus记录]
收录类别	SCI ; EI
语种	英语
学校署名	其他
资助项目	National Natural Science Foundation of China[41674142];National Natural Science Foundation of China[41874150];National Natural Science Foundation of China[42074164];
WOS研究方向	Geology ; Mining & Mineral Processing
WOS类目	Geosciences, Multidisciplinary ; Mining & Mineral Processing
WOS记录号	WOS:001039886300001
出版者	ELSEVIER
EI入藏号	20232814378719
EI主题词	Multiobjective optimization ; Optimal systems ; Pareto principle ; Particle swarm optimization (PSO) ; Rayleigh waves ; Waveform analysis
EI分类号	Seismology:484 ; Computer Software, Data Handling and Applications:723 ; Optimization Techniques:921.5 ; Numerical Methods:921.6 ; Systems Science:961
ESI学科分类	GEOSCIENCES
Scopus记录号	2-s2.0-85164221549
来源库	Scopus
引用统计	被引频次[WOS]：2
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/559784
专题	理学院_地球与空间科学系
作者单位	1.School of Geophysics and Geomatics,China University of Geosciences,Wuhan,Hubei,China 2.College of Metrology & Measurement Engineering,China Jiliang University,Zhejiang,Hangzhou,China 3.Department of Earth and Space Sciences,Southern University of Science and Technology,Shenzhen,Guangdong,China
推荐引用方式 GB/T 7714	Le，Zhao,Song，Xianhai,Zhang，Xueqiang,et al. Multi-objective particle swarm optimization for Rayleigh wave full waveform inversion[J]. Journal of Applied Geophysics,2023,215.
APA	Le，Zhao.,Song，Xianhai.,Zhang，Xueqiang.,Shen，Chao.,Shi，Xueming.,...&Yuan，Shichuan.(2023).Multi-objective particle swarm optimization for Rayleigh wave full waveform inversion.Journal of Applied Geophysics,215.
MLA	Le，Zhao,et al."Multi-objective particle swarm optimization for Rayleigh wave full waveform inversion".Journal of Applied Geophysics 215(2023).