Bayesian Inversion of an Acoustic-Gravity Model for Predictive Tsunami Simulation

Explore a seminar on Bayesian inversion of an acoustic-gravity model for predictive tsunami simulation, presented by Stefan Henneking from the University of Texas at Austin. Delve into a novel approach for improving tsunami preparedness through early-alert systems and real-time monitoring. Learn about the coupled acoustic-gravity forward model, which relies on transient boundary data describing seafloor deformation. Understand the challenges of inferring parameter fields from sparse pressure data in the near-field, where strong hydroacoustic waves complicate hydrostatic pressure estimation. Examine the space-time model discretization using finite elements in space and finite differences in time. Discover approaches for using compact representations of the parameter-to-observable map to address the high computational complexity of the forward model and the infeasibility of rapidly solving the inverse problem for the fully discretized space-time operator.

On January 10, 2023, Stefan Henneking of the University of Texas at Austin presented “Bayesian Inversion of an Acoustic-Gravity Model for Predictive Tsunami Simulation.” To improve tsunami preparedness, early-alert systems and real-time monitoring are essential. We use a novel approach for predictive tsunami modeling within the Bayesian inversion framework. This effort focuses on informing the immediate response to an occurring tsunami event using near-field data observation. Our forward model is based on a coupled acoustic-gravity model e.g., Lotto and Dunham, Comput Geosci 2015 7—340. Similar to other tsunami models, our forward model relies on transient boundary data describing the location and magnitude of the seafloor deformation. In a real-time scenario, these parameter fields must be inferred from a variety of measurements, including observations from pressure gauges mounted on the seafloor. One particular difficulty of this inference problem lies in the accurate inversion from sparse pressure data recorded in the near-field where strong hydroacoustic waves propagate in the compressible ocean; these acoustic waves complicate the task of estimating the hydrostatic pressure changes related to the forming surface gravity wave. Our space-time model is discretized with finite elements in space and finite differences in time. The forward model incurs a high computational complexity, since the pressure waves must be resolved in the 3D compressible ocean over a sufficiently long time span. Due to the infeasibility of rapidly solving the corresponding inverse problem for the fully discretized space-time operator, we discuss approaches for using compact representations of the parameter-to-observable map.