Interval Determination Strategy for Bayesian Inversion of Seismic Source Parameters Under Uncertain Interval Conditions

Using a Bayesian framework to invert earthquake source parameters from multi-source geodetic data has become an important research direction. To address the issue of Markov Chain Monte Carlo (MCMC) algorithms getting stuck in local optima during nonlinear inversion of fault geometric parameters, which is often caused by improperly set parameter bounds or large deviations in the initial values, this study proposes two strategies: ‘CFI (Converge First, Then Interval)’ and ‘IVI (Interval Value Iteration)’. Tests with 12 different experimental setups show that both strategies can prevent the chain from getting trapped in local optima. Among them, the ‘IVI’ strategy, when used with MCMC algorithms where the step size follows a normal distribution, can also significantly reduce the root-mean-square error. To verify its applicability, the ‘IVI’ strategy was applied to the Bayesian inversion of the 2022 Menyuan Mw6.6 earthquake. The results show that the inverted values for fault depth, strike, dip, and rake angles are closer to the GCMT results, with ascending and descending track fitting residuals of 2.71 cm and 2.64 cm, respectively. The conclusion of this paper is to recommend the ‘IVI’ strategy when the range of source parameters is unclear. If the approximate range of parameters is known, the ‘CFI’ strategy can be applied. The original interval constraint method is recommended when the parameter bounds are fully determinable and a reliable initial model of seismic source parameters is obtainable.