Stochastic Emulation for Efficient Onshore Probabilistic Tsunami Hazard Assessment

This is the authors' abstract. We don't add key points for this paper.

Machine learning is emerging as a promising strategy for modeling tsunami inundation at reduced computational cost. To enable probabilistic outputs that capture emulator uncertainty, we employ an ensemble of stochastic encoder‐decoder emulators with dropout‐based stochastic forward pass. Training and testing is conducted on an extensive Mediterranean Sea tsunami simulation data set comprising multiple earthquake source regions with variable magnitude, location, and focal mechanism, including near‐field events with local coseismic deformation. For the two target sites Catania and Siracusa in Sicily, we achieve good performance for relatively sparse training sets of only a few hundred events. We demonstrate the potential of our emulators to accurately model inundation from a range of tsunami sources needed to perform probabilistic tsunami hazard assessment (PTHA), reducing the computational cost of PTHA by a factor of 30–100, and estimating the uncertainty introduced by the emulator. Comparing probabilistic hazard maps and curves from our emulation with those from the full simulation ensemble and importance sampling Monte Carlo approach, we demonstrate the efficacy of emulators in capturing the spatial distribution and magnitude of inundation needed in overland tsunami hazard assessment.