断层参数反演的动态惯性因子的粒子群算法.

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. [ABSTRACT FROM AUTHOR]