Robust inversion, dimensionality reduction, and randomized sampling

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Abstract

We consider a class of inverse problems in which the forward model is the solution operator to linear ODEs or PDEs. This class admits several dimensionality-reduction techniques based on data averaging or sampling, which are especially useful for large-scale problems. We survey these approaches and their connection to stochastic optimization. The data-averaging approach is only viable, however, for a least-squares misfit, which is sensitive to outliers in the data and artifacts unexplained by the forward model. This motivates us to propose a robust formulation based on the Student's t-distribution of the error. We demonstrate how the corresponding penalty function, together with the sampling approach, can obtain good results for a large-scale seismic inverse problem with 50% corrupted data.

BiBTeX

@article{AravkinFHV:2012,
  author = {A. Aravkin and M. P. Friedlander and F. Herrmann and T. van Leeuwen},
  title = {Robust inversion, dimensionality reduction, and randomized sampling},
  journal = {Mathematical Programming},
  volume = 134,
  number = 1,
  pages = {101-125},
  year = {2012}
  DOI = {10.1007/s10107-012-0571-6}
}