bob@sep.stanford.edu

## ABSTRACTThe 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. |

- Introduction
- Background
- Discontinuities and steering filters
- Estimating a non-stationary filter
- Missing data
- Tomography
- Conclusions
- REFERENCES
- About this document ...

Stanford Exploration Project

4/28/2000