Bayesian Inversion of Finite-Fault Earthquake Slip Model Using Geodetic Data, Solving for Non-Planar Fault Geometry, Variable Slip, and Data Weighting

A precise finite-fault model including the fault geometry and slip distribution is essential to understand the physics of an earthquake. However, the conventional linear inversion of geodetic data for a finite-fault model cannot fully resolve the fault geometry. In this study, we developed a Bayesian inversion framework that can comprehensively solve a non-planar fault geometry, the corresponding fault slip distribution with spatially variable directions, and objective weighting for multiple data types. In the proposed framework, the probability distributions of all the model parameters are sampled using the Monte Carlo method. The developed methodology removes the requirement for manual intervention for the fault geometry and data weighting and can provide an ensemble of plausible model parameters. The performance of the developed method is tested and demonstrated through inversions for synthetic oblique-slip faulting models. The results show that the constant rake assumption can significantly bias the estimates of fault geometry and data weighting, whereas additional consideration of the variability of slip orientations can allow plausible estimates of a non-planar fault geometry with objective data weighting. We applied the method to the 2013 M 6.5 Lushan earthquake in Sichuan province, China. The result reveals dominant thrust slips with left-lateral components and a curved fault geometry, with the confidence interval of the dip angles being between 20°–25° and 56°–58°. The proposed method provides useful insights into the scope of imaging a non-planar fault geometry, and could help to interpret more complex earthquake sources in the future.