Performance of Stochastic Tsunami Source Models Compared with Observations and Finite-Fault Inversions

Various approaches exist to generate physically plausible tsunami source models in order to quantify source uncertainty in tsunami hazard assessments. Among these, stochastic slip models are widely used due to their low computational cost. Finite-fault models derived from inversion results are also sometimes used to anticipate the slip distributions of future ruptures.

In this study, we compare synthetic tsunamis generated using a stochastic slip model with far-field DART observations from 14 tsunami events. We construct eight classes of source models based on combinations of two scaling relations (Murotani et al., 2013; Strasser et al., 2010), circular and rectangular rupture geometries, and depth-independent versus depth-dependent rigidity and coupling. For each class, we simulate a number of synthetic tsunamis using stochastic slip distributions of earthquakes of magnitude and location similar to those of the earthquakes that generated the 14 tsunamis. The results indicate that nearly all source model classes exhibit a mild—though not statistically significant—tendency to generate synthetic tsunamis which overestimate the observed tsunami amplitudes.

We further conduct a quantitative comparison of slip distributions and tsunami time series from the best-fitting stochastic scenarios with those obtained from finite-fault teleseismic inversion models, some of which are constrained also by tsunami data. Overall, the best-fitting stochastic models reproduce observed tsunami waveforms more accurately than models derived from teleseismic-only inversions. However, for some events, specific slip patterns inferred from inversion models, such as an annular shape, cannot be adequately reproduced by the stochastic approach, leading to poorer fits to the observations.