Bayesian-based joint determination of earthquake hypocentres and 1-D velocity structures divided by a structural boundary

Abstract

SUMMARY We propose a Bayesian approach for jointly determining the locations of earthquake hypocentres and velocity structures in a region divided by a structural boundary, such as a fault zone. To accommodate the Markov chain Monte Carlo (MCMC) method in a region containing lateral velocity contrast, we incorporated a data-driven station clustering scheme when estimating earthquake hypocentre locations, a 1-D velocity model assigned to each cluster and station corrections for traveltimes. The proposed method realizes the clustering of stations reflecting velocity structures beneath the station network in the framework of the MCMC method. We verified the performances of the proposed method using numerical experiments with different distributions of earthquake hypocentres relative to the structural boundary. The results demonstrated that the proposed method reconstructed earthquake hypocentre locations and velocity structures involving lateral contrast robustly to earthquake distributions. Additionally, the number of 1-D velocity models (the number of station clusters) was optimized by comparing resultant posterior probability proportional to distances between the observed and theoretical traveltimes. We also applied the proposed method to estimate the earthquake hypocentre locations and velocity structures around the source area of the 2004 Mid-Niigata prefecture earthquake, Japan, to examine the applicability of the proposed method to real observation data. The stations were divided into east and west clusters, coinciding with the lateral velocity variation related to the geological setting. Therefore, the proposed method accurately determines earthquake hypocentres in a region divided by a structural boundary without any knowledge regarding the geological structure.