Two different prestack interpolation techniques are applied to a sparsely acquired and heavily structured 3D land data set from the Canadian Foothills. The first of these is the well-known 5D Minimum Weighted Norm Interpolation (5DMWNI) algorithm which is based on Fourier reconstruction and the second is a dip-scan-based data synthesis approach we call “DSINTERP”. The sparse nature of the acquisition was a result of mountainous topography which prevented optimal and efficient placement of both shots and receivers, and the data reconstruction task entailed both (i) regular upsampling along the crossline midpoint coordinate and (ii) gap-filling along certain shot and receiver lines which were truncated in the field because of the rugged terrain. The presentation will focus on the real data testing of both 5DMWNI and DSINTERP interpolation approaches and will showcase the relative algorithmic strengths and weaknesses. This practical case study will draw on the theory presented in a companion “Part I” paper, and it reveals several key findings: first, we note that the 5DMWNI encounters difficulties in performing the regular midpoint upsampling, a finding which is consistent with our theoretical understanding of the algorithm; second, DSINTERP has trouble infilling large gaps, a consequence of its utilization of extremely localized spatial operators; third, judicious cascading of the two approaches produces a good result, in effect combining the best of both worlds.


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