morgan@sep.stanford.edu

## ABSTRACTThe spectrum of a prediction-error filter (PEF) tends toward the inverse spectrum of the data from which it is estimated. I compute 2-D PEF's from known ``training images'' and use them to synthesize similar-looking textures from random numbers via helix deconvolution. Compared to a similar technique employing Fourier transforms, the PEF-based method is generally more flexible, due to its ability to handle missing data, a fact which I illustrate with an example. Applying PEF-based texture synthesis to a stacked 2-D seismic section, I note that the residual error in the PEF estimation forms the basis for ``coherency'' analysis by highlighting discontinuities in the data, and may also serve as a measure of the quality of a given migration velocity model. Last, I relate the notion of texture synthesis to missing data interpolation and show an example. |

- Introduction
- Fourier transform method
- PEF-Based method
- Why use the PEF?
- Applications
- Discussion
- Acknowledgements
- REFERENCES
- About this document ...

4/20/1999