<p>Dynamic rupture modeling coupled with strong motion data fitting (dynamic source inversion) offers an insight into the rupture physics, constraining and enriching information gained from standard kinematic slip inversions. We utilize the Bayesian Monte Carlo dynamic source inversion method introduced recently by Gallovi&#269; et al. (2019), which, in addition to finding a best-fitting model, allows assessing uncertainties of the inferred parameters by sampling the posterior probability density function. The Monte Carlo approach requires running a large number (millions) of dynamic simulations due to the nonlinearity of the inverse problem. It is achieved by using GPU accelerated dynamic rupture simulation code FD3D_TSN (Premus et al., submitted) as a forward solver. We apply the inversion to the 2014 Mw6 South Napa, California, earthquake, employing strong motion data (up to 0.5 Hz) from the 10 closest stations. As an output, we obtain samples of the spatial distributions of dynamic parameters (prestress and parameters of the slip-weakening friction law). Regarding the rupture geometry, we consider two, presently ambiguous, fault planes (Pollitz et al., 2019), showing considerable differences in fitting seismograms in very close vicinity of the fault. We investigate properties of the rupture, especially in the region close to the free surface, and the viability of the model samples to explain the observed data in a broader frequency range (up to 5Hz).</p>
Probabilistic point source inversion of strong‐motion data in 3‐D media using pattern recognition: A case study for the 2008
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5.4 Chino Hills earthquake