Prof. Omar Ghattas is presenting in the upcoming Geophysics Seminar on May 28th, Wednesday. The seminar will be hosted at the Munk conference room at 3 pm.
Title:
Real Time High Fidelity Bayesian Inference and Forecasting of Tsunamis
Abstract:
Efforts are underway to instrument subduction zones with seafloor acoustic pressure sensors to provide tsunami early warning. Our goal is to create a physics-based early-warning system that employs this pressure data, along with the 3D coupled acoustic–gravity wave equations forward model, to infer the earthquake-induced spatiotemporal seafloor motion in real time. The Bayesian solution of this inverse problem then provides the seafloor forcing to forward propagate the tsunamis toward populated areas along coastlines and issue forecasts with quantified uncertainties.
Solution of a single forward problem alone entails severe computational costs stemming from the need to resolve ocean acoustic waves in a subduction zone of length ~1000 km and width ~200 km. This can require 1 hour on a supercomputer. The Bayesian inverse problem, with a billion uncertain parameters, formally requires hundreds of thousands of such forward and adjoint wave propagations; thus real-time inference appears to be intractable. We propose a novel approach to enable accurate solution of the inverse and prediction problems in real time on a GPU cluster. The key is to exploit the structure of the parameter-to-observable map, namely that it is a shift-invariant operator and its discretization can be recast as a block Toeplitz matrix, permitting FFT diagonalization and fast GPU implementation. We discuss the Bayesian formulation and real-time GPU solution, and demonstrate that tsunami inverse problems with O(10^9) parameters can be solved exactly in a fraction of a second.
This work is joint with Sreeram Venkat (UT Austin), Stefan Henneking (UT Austin), and Alice Gabriel (UCSD).