Conclusions

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.