sean@sep.stanford.edu

## ABSTRACT
Building on the notions of time-variable filtering
and the helix coordinate system,
we develop software for filters that
are smoothly variable in multiple dimensions.
Multiscale prediction-error filters (PEFs) can estimate dips
from recorded data and use the dip information to fill in
unrecorded shot or receiver gathers.
The data are typically divided into patches with approximately constant dips.
Instead, we estimate a set of smoothly varying filters,
up to one PEF for every data sample.
They are more memory-intensive to estimate,
but the smoothly varying filters do give more accurate
interpolation results than discrete patches.
Finally, we offer an improved method of controlling
the smoothness of the filters.
We design filters like directional derivatives
that we call ``flag filters''.
They destroy dips in easily adjusted directions.
We use them in residual space to encourage dips in the specified directions.
We develop the notion of ``radial-flag filters'',
i.e., flag filters oriented in the radial direction
(lines of constant |

- INTRODUCTION
- TIME- AND SPACE-VARYING PEFS
- INTERPOLATING MISSING TRACES
- FUTURE WORK
- CONCLUSIONS
- REFERENCES
- About this document ...

7/5/1998