Bayesian differential moment tensor inversion: theory and application to the North Korea nuclear tests

Zhe Jia, Zhongwen Zhan, Donald Helmberger

Abstract: Moment tensors are key to seismic discrimination but often require accurate Green's functions for estimation. This limits the regions, frequency bands and wave types in moment tensor inversions. In this study, we propose a differential moment tensor inversion (diffMT) method that uses relative measurements to remove the path effects shared by clustered events, thereby improving the accuracy of source parameters. Using results from regular inversions as a priori distribution, we apply Bayesian Markov Chain Monte Carlo to invert the body- and surface wave amplitude ratios of an event pair for refined moment tensors of both events. Applications to three North Korea nuclear tests from 2013 to 2016 demonstrate that diffMT reduces the uncertainties substantially compared with the traditional waveform-based moment tensor inversion. Our results suggest high percentages of explosive components with similar double-couple components for the North Korea nuclear tests.