A HYBRID ML-PHYSICAL MODELLING APPROACH FOR EFFICIENT PROBABILISTIC TSUNAMI HAZARD AND RISK ASSESSMENT

Probabilistic tsunami hazard and risk assessment (PTHA/PTRA) are vital tools for understanding tsunami risk and planning measures to mitigate impacts. At large-scales their use and scope are currently limited by the computational costs of numerically intensive simulations which are not always feasible without large computational resources like HPCs and may require reductions in resolution, number of scenarios modelled or use of simpler approximation schemes. To conduct the PTHA/PTRA for large proportions of a coast, we need therefore to develop concepts and algorithms for reducing the number of events simulated and more rapidly approximating the needed simulation results. This case study for a coastal region of Tohoku, Japan utilizes a limited number of tsunami simulations from submarine earthquakes along the subduction interface to generate a wave propagation and inundation database at different depths and fits these simulation results to a machine learning (ML) based variational autoencoder model to predict the intensity measure (water depth, velocity, etc.) of the tsunami at the location of interest. Such a hybrid ML-physical model can be further extended to compute the inundation for probabilistic tsunami hazard and risk onshore.