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IAMCS Workshop in Large-Scale Inverse Problems and Uncertainty Quantification

Carsten Burstedde, University of Texas at Austin
Scalable Algorithms for Large-Scale Inverse Problems Under Uncertainty


We consider algorithms for inverse problems in seismic wave propagation with the goal of achieving large-scale parallel scalability. We present inexact Newton-Krylov iterative methods, where the Hessian is applied via the solution of forward and adjoint problems. These are solved in parallel using a discontinuous Galerkin method, where mesh adaptivity is applied to both the state and parameter fields. Finally, we link deterministic inversion to the Bayesian framework to quantify the uncertainty of the inversion, at the cost of a manageable number of forward and adjoint solves.