Presentation Information
[_yrsOa95-01]Real time Bayesian inversion, prediction, and OED for tsunami
early warning
*Omar Ghattas
We address real-time Bayesian inverse problems governed by
time-shift-invariant wave equations, with particular focus on tsunami
source inversion and forecasting, and sensor network optimal experimental
design. Efforts are underway to instrument subduction zones with ocean
bottom 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, 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.
In the context of the Cascadia Subduction Zone, a single forward wave
propagation requires ~1 hour on a supercomputer. The Bayesian inverse
problem, with a billion uncertain parameters, formally requires
hundreds of thousands of adjoint wave propagations; thus real time
inference appears to be intractable. We propose a novel approach to
enable exact solution of the inverse and prediction problems in real
time. The key is to exploit the time-shift-invariance of the
parameter-to-observable map, which permits FFT diagonalization and
fast GPU implementation. The result is that tsunami inverse problems
with a billion parameters can be solved exactly in a fraction of a
second.
This fast Bayesian inversion capability is then exploited to
solve the optimal experimental design problem of placement of seafloor
pressure sensors to maximize expected information gain in predictive
quantities of interest. Time permitting, we discuss data-driven prior
construction, goal-oriented dimension reduction, and construction of
fast surrogates for the nonlinear shallow water equations, which are
needed for representing nearshore tsunami dynamics.
This work is joint with Stefan Henneking, Sreeram Venkat, Bowen Shi,
and Yuhang Li at UT Austin, and Alice Gabriel at UCSD.
time-shift-invariant wave equations, with particular focus on tsunami
source inversion and forecasting, and sensor network optimal experimental
design. Efforts are underway to instrument subduction zones with ocean
bottom 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, 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.
In the context of the Cascadia Subduction Zone, a single forward wave
propagation requires ~1 hour on a supercomputer. The Bayesian inverse
problem, with a billion uncertain parameters, formally requires
hundreds of thousands of adjoint wave propagations; thus real time
inference appears to be intractable. We propose a novel approach to
enable exact solution of the inverse and prediction problems in real
time. The key is to exploit the time-shift-invariance of the
parameter-to-observable map, which permits FFT diagonalization and
fast GPU implementation. The result is that tsunami inverse problems
with a billion parameters can be solved exactly in a fraction of a
second.
This fast Bayesian inversion capability is then exploited to
solve the optimal experimental design problem of placement of seafloor
pressure sensors to maximize expected information gain in predictive
quantities of interest. Time permitting, we discuss data-driven prior
construction, goal-oriented dimension reduction, and construction of
fast surrogates for the nonlinear shallow water equations, which are
needed for representing nearshore tsunami dynamics.
This work is joint with Stefan Henneking, Sreeram Venkat, Bowen Shi,
and Yuhang Li at UT Austin, and Alice Gabriel at UCSD.
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