Regularizing tomography with non-stationary filters

Robert G. Clapp

bob@sep.stanford.edu

ABSTRACT

The ideal regularizer is the inverse of the model covariance matrix. Often the model covariance matrix has a complicated structure that is difficult to characterize. Non-stationary prediction error filters (PEF) have the ability to describe complicated model behavior. Non-stationary filters are effective regularizers for missing data and tomography problems.