Particle Swarm Optimization Algorithm with Dynamic Inertia Factors for Inversion of Fault Parameters

WANG Leyang; JIN Xibo; XU Guangyu

doi:10.13203/j.whugis20190321

Volume 46 Issue 4

Apr. 2021

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Geomatics and Information Science of Wuhan University > 2021 > 46(4): 510-519. > DOI: 10.13203/j.whugis20190321

WANG Leyang, JIN Xibo, XU Guangyu. Particle Swarm Optimization Algorithm with Dynamic Inertia Factors for Inversion of Fault Parameters[J]. Geomatics and Information Science of Wuhan University, 2021, 46(4): 510-519. DOI: 10.13203/j.whugis20190321

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Particle Swarm Optimization Algorithm with Dynamic Inertia Factors for Inversion of Fault Parameters

1.
Faculty of Geomatics, East China University of Technology, Nanchang 330013, China
2.
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

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The National Natural Science Foundation of China 41874001

The National Natural Science Foundation of China 41664001

the Natural Science Foundation of Jiangxi Province 20202BABL204070

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Author Bio:
WANG Leyang, PhD, professor, specializes in geodetic inversion and geodetic data processing. E-mail: wleyang@163.com
Received Date: August 08, 2020
Published Date: April 04, 2021

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Abstract

Abstract

Objectives Inversion of seismic fault parameters using geodetic observation data is a hotspot in geodetic inversion, and it is also the focus of studying the mechanism of earthquake occurrence. Aiming at the low accuracy of particle swarm optimization (PSO) currently used in fault parameter inversion, this paper analyzes the nonlinear characteristics of seismic fault parameter inversion and the characteristics of basic PSO. Basic PSO is easy to fall into a local optimal solution in highly nonlinear problems, and the local optimal solution and the global optimal solution may affect each other during the PSO solution process. This paper proposed a new particle swarm algorithm for inversion of fault parameters to solve the local optimization.
Methods In this paper, we adopted the strategy of segmentally and dynamically adjusting the parameters, including the inertia factor that affects the particle velocity and the acceleration factors that affect the local and global optimal solutions.
Results The proposed algorithm was applied to inverse the fault parameters for the simulation earthquake and the L?Aquila earthquake. The results of the simulation earthquake show that the proposed algorithm is stable, and the inclination and sliding angle obtained by the proposed algorithm are closer to the true value. The results of the L?Aquila earthquake show that the root mean squared error (RMSE) of surface observations and deformation variables obtained by the proposed algorithm is 5.2 mm, which is better than 6.7 mm obtained by multi-peak particle swarm optimization (MPSO).
Conclusions The experiment results show that the fault model obtained by the proposed algorithm is more consistent with the true fracture condition, and the proposed algorithm has practical application value.
- fault parameter inversion,
- particle swarm optimization (PSO),
- earthquake,
- nonlinear,
- dyna‐mic inertial factor

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References

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Particle Swarm Optimization Algorithm with Dynamic Inertia Factors for Inversion of Fault Parameters

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