Research and application of improved particle swarm optimization algorithm in fault sliding parameter inversion

doi:10.13474/j.cnki.11-2246.2024.0918

Abstract

Abstract: One of the main research issues in geodesy is to use ground geodetic data to invert dynamic parameters such as fault sliding rate.We propose an improved particle swarm optimization algorithm to compensate for the shortcomings of the standard particle swarm algorithm, which may have local optimal solutions, and conduct experimental verification through simulated data.Later, taking the two main faults in the Weihe Basin as research objects, the three-dimensional sliding rates of the northern Qinling Fault and the Lintong Chang'an Fault are inverted using ground GPS observation data, and the running time of the two algorithms is analyzed.The results show that the improved particle swarm optimization algorithm reduces the time consumption compared to the standard particle swarm optimization algorithm, and converges faster.The fault parameters obtained by the algorithm proposed in this article are more in line with real fault conditions and have certain practical application value.

Key words: geodetic inversion, faults slip velocity, dislocation theory model, improved particle swarm optimization algorithm, Weihe Basin

CLC Number:

LIU Jie, WANG Hongyu, WU Yanping. Research and application of improved particle swarm optimization algorithm in fault sliding parameter inversion[J]. Bulletin of Surveying and Mapping, 2024, 0(9): 101-105.

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