Accuracy evaluation on five non-linear methods for fault parameter inversion of different-type fault earthquakes

Abstract

Accurate inversion of seismic fault parameters has been a challenge in the studies of geophysical non-linear inversion problems. Many non-linear methods such as Simulated Annealing (SA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Multipeaks Particle Swarm Optimization (MPSO), have usually been applied to inverse the fault parameters from geodetic observation data. However, their accuracy and availability can vary from different-type fault earthquakes (pure strike-slip, pure dip-slip fault, oblique-slip fault earthquakes). In order to evaluate the accuracy and availability of these non-linear methods on inversion for fault parameters of different-type fault earthquakes, we applied the SA, GA, PSO, MPSO methods and a new non-linear method—Black Hole Particle Swarm Optimization (BHPSO), to inverse fault parameters of different-type earthquakes from synthetic and observed GPS and InSAR data. We found that the MPSO and BHPSO performed better than SA, GA, and PSO for inversion from both the synthetic and observed data. The synthetic data simulation results showed that the Root-Mean-Square Errors (RMSEs) of MPSO and BHPSO methods were 0.01–0.06 m, smaller than those of SA, GA and PSO. We then applied these five methods to inverse fault parameters of two real earthquakes—the 2020 Nevada Mw 6.4 earthquake and 2021 Maduo Mw 7.4 earthquake, from observed GPS and InSAR data. We found that the RMSEs of MPSO and BHPSO were 0.005–0.195 m, also smaller than those of SA, GA, and PSO, and the MPSO and BHPSO performed better than SA, GA, and PSO. The results in this study demonstrated that the MPSO and BHPSO, can hold high accuracy and availability for inversion of fault parameters of different-type fault earthquakes.