Integral operator quality from low order interpolation or Sometimes nearest neighbor beats linear interpolation

Integral operator quality from low order interpolation
or
Sometimes nearest neighbor beats linear interpolation

Stewart A. Levin

Abstract:

In most discussions of interpolation methods, it is the worst-case behavior that dominates the analysis. From a systems point of view, one really should analyze how that interpolation is used in producing an end product in order to determine the interpolation's suitability. In this report I look at the summation operators slant stack, NMO and Kirchhoff migration as the ``systems'' and determine that their output quality can be significantly better than the traditional take on interpolation would suggest. In one scenario, I even found nearest neighbor interpolation did the job even better than linear interpolation.