We consider a class of inverse problems where it is possible to aggregate the results of multiple experiments. This class includes problems where the forward model is the solution operator to linear ODEs or PDEs. The tremendous size of such problems motivates the use dimensionality reduction (DR) techniques based on randomly mixing experiments. These techniques break down, however, when robust data-fitting formulations are used, which are essential in cases of missing data, unusually large errors, and systematic features in the data unexplained by the forward model. We survey robust methods within a statistical framework, and propose a sampling optimization approach that allows DR. The efficacy of the methods are demonstrated for a large-scale seismic inverse problem using the robust Student's t-distribution, where a useful synthetic velocity model is recovered in the extreme scenario of 60% corrupted data. The sampling approach achieves this recovery using 20% of the effort required by a direct robust approach.
['Aleksandr Y. Aravkin', 'Michael P. Friedlander', 'Tristan van Leeuwen']
Robust inversion via semistochastic dimensionality reduction