A new method of variational Bayesian slip distribution inversion

For slip distribution inversion with Bayesian theory, traditionally, the Markov Chain Monte Carlo (MCMC) method is well applied to generate a posterior probability density function with a sampling strategy. However, its computational cost may be expensive, and it fails to accommodate large volume data sets and estimate higher dimensional parameters of interest. In this study, we introduce variational inference theory into the study of coseismic slip distribution, and present a variational Bayesian slip distribution inversion approach. Furthermore, synthetic tests show that the variational Bayesian approach can efficiently and accurately invert the designed slip distribution; therefore, we conclude that the proposed algorithm is appropriate to invert the slip distribution parameters, which might be superior to MCMC sampling due to its excellent convergence speed and low computational burden. Taking the Dingri earthquake on March 20, 2020, as an example, we further verify the practicability of the variational Bayesian method in actual earthquakes. Additionally, the inversion results show that the main fault slip region of the Dingri earthquake occurs at depths of 2 ~ 8 km on the surface, the maximum slip amount is 0.54 m, and the coseismic release seismic moment is 5.58 × 10 17 Nm, corresponding to a moment magnitude of Mw 5.79.