Massively parallel Bayesian estimation with Sequential Monte Carlo sampling for simultaneous estimation of earthquake fault geometry and slip distribution

Abstract

In inverse analysis, Bayesian estimation is useful for understanding the reliability of the estimation result or prediction based on it because it can estimate not only optimal parameters but also their uncertainties. The estimation of an earthquake source fault based on observed crustal deformation is a typical inverse problem in the field of earthquake research. In this study, a method for simultaneous Bayesian estimation of earthquake fault plane geometry and spatially variable slip distribution on the plane has been developed. The developed method can be applied to stochastic models with arbitrary probability distribution settings, and it enables to incorporate appropriate constraints for slip distribution in the estimation process, which can lead to enhanced robustness and stability of estimation. Since this method is computationally more expensive than conventional methods, large-scale parallel computing was introduced to cope with the increased computational cost and a supercomputer was used for the analysis. To validate the proposed method, simultaneous Bayesian estimation of fault geometry and slip distribution with slip constraints was performed using the crustal deformation observed in the 2018 Hokkaido Eastern Iburi earthquake. Hierarchical parameterization and massively parallelized Bayesian inference used in this study have broad applicability not only in earthquake research but also in other scientific and engineering fields.