oa Improving old tricks: FX interpolation beyond alias for irregularly sampled data, for data with large gaps and for data with spatially variant dips

For almost two decades, FX prediction filtering (Spitz, 1991) and FK interpolation (Gülünay, 2003) have provided a robust route to interpolate aliased seismic data. These are probably the only two interpolation methods that were built on sound and solid arguments for the interpolation of aliased events. They both have precise assumptions that one must honor to optimally interpolate seismic data. In essence, the data must be composed of a finite superposition of events with linear moveout. The latter is often validated by windowing the data. In addition, both methods require regularly sampled data and a good SNR in the low frequency portion of the spectrum where the vital non‐aliased Fourier coefficients reside.