antoine@sep.stanford.edu

## ABSTRACTInverse theory teaches us that the residual, or misfit function, should be weighted by the inverse covariance matrix of the noise. Because the covariance operator is often difficult to estimate, we can approximate it with a diagonal weight that can be more easily computed. This paper investigates the possible choices of weighting functions for the data residual when prediction-error filters are estimated. Examples with 2-D and 3-D field data prove that it is better to weight the residual than to weight the data before starting the inversion. |

- Introduction
- Theory of PEF estimation
- Signal-noise separation with weighted PEFs
- conclusion
- REFERENCES
- About this document ...

7/8/2003