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.