Bayesian Inversion for Large Scale Antarctic Ice Sheet Flow

Wednesday, 17 December 2014: 9:00 AM
Omar Ghattas1,2, Tobin Isaac2, Noemi Petra3 and Georg Stadler2,4, (1)The University of Texas at Austin, Jackson School of Geosciences, Austin, TX, United States, (2)The University of Texas at Austin, Institute for Computational Engineering & Sciences, Austin, TX, United States, (3)University of California Merced, School of Natural Sciences, Merced, CA, United States, (4)New York University, Courant Institute of Mathematical Sciences, New York, NY, United States
The flow of ice from the interior of polar ice sheets is the primary
contributor to projected sea level rise. One of the main difficulties
faced in modeling ice sheet flow is the uncertain spatially-varying
Robin boundary condition that describes the resistance to sliding at
the base of the ice. Satellite observations of the surface ice flow
velocity, along with a model of ice as a creeping incompressible
shear-thinning fluid, can be used to infer this uncertain basal
boundary condition. We cast this ill-posed inverse problem in the
framework of Bayesian inference, which allows us to infer not only the
basal sliding parameters, but also the associated uncertainty. To
overcome the prohibitive nature of Bayesian methods for large-scale
inverse problems, we exploit the fact that, despite the large size of
observational data, they typically provide only sparse information on
model parameters. We show results for Bayesian inversion of the basal
sliding parameter field for the full Antarctic continent, and
demonstrate that the work required to solve the inverse problem,
measured in number of forward (and adjoint) ice sheet model solves, is
independent of the parameter dimension, data dimension, and number of
processor cores.