Bayesian Framework for Inversion of Second-Order Stress Glut Moments: Application to the 2019 Ridgecrest Sequence Mainshock

Abstract

We present a fully Bayesian inverse scheme to determine second moments of the stress glut using teleseismic earthquake seismograms. The second moments form a low-dimensional, physically motivated representation of the rupture process that captures its spatial extent, source duration, and directivity effects. We determine an ensemble of second-moment solutions by employing Hamiltonian Monte Carlo and automatic differentiation to efficiently approximate the posterior. This method explicitly constrains the parameter space to be symmetric positive definite, ensuring the derived source properties have physically meaningful values. The framework accounts for the autocorrelation structure of the errors and incorporates hyperpriors on the uncertainty. We validate this methodology using a synthetic test and subsequently apply it to the 2019 M_w7.1 Ridgecrest earthquake using teleseismic data. The distributions of second moments determined for this event provide probabilistic descriptions of low-dimensional rupture characteristics that are generally consistent with results from previous studies. The success of this case study suggests that probabilistic and comparable finite source properties may be discerned for large global events regardless of the quality and coverage of local instrumentation.

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