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Shaping filters are closely related to cross-correlelograms, and
therefore can be used to calculate kinematic misalignments between two
similar datasets.
I demonstrate the use of shaping filters to calculate a dynamic
``warp'' function that maps one dataset to another.
Shaping filters can leverage the power of geophysical estimation
theory, which potentially may help avoid problems associated with
noisy data to provide improved estimates of multi-dimensional warp
functions.
I compared results of warping with a function derived from shaping filters
with results from a warp function derived from cross-correlelograms.
For the one-dimensional well-tie example, the shaping filters gave
encouraging results; however, for the two-dimensional example the
cross-correlation technique gave better results.

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Stanford Exploration Project

4/27/2000