FEM@LLNL | Bayesian Inversion of an Acoustic-Gravity Model for Predictive Tsunami Simulation

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) 19:327—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.

LLNL-VIDEO-844467

#LLNL #LLNLcomputing #AcousticGravityModel