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On the CGG Green Canyon 3-D dataset, LSJIMP again demonstrated an excellent
ability to separate primaries and multiples. The data subset shown in the
thesis came from a sedimentary minibasin, which boasts a simple velocity
profile, high signal-to-noise ratio, and fairly mild (but non-trivial) dips.
The full dataset released by CGG contains much data recorded over salt. In
preliminary tests on one salt region, NMO and HEMNO proved unable to correctly
image primaries or multiples. The salt bodies often exhibit crossline dips of
over 30 degrees, which, when combined with the data's inherent sparsity,
severely test even the most advanced imaging techniques.
The data subset is particularly well-suited for Radon demultiple, with its
large velocity gradient and gentle geology. I tested least-squares hyperbolic
Radon demultiple (LSHRTD) and found that LSJIMP compares quite favorably, both
in terms of computational efficiency, multiple separation, and amplitude
preservation.
The quantitative study of prestack reflection amplitudes in section
confirmed what was suspected; LSJIMP's ability to remove
multiples and random noise, as well as its ability to use multiples and other
constraints to interpolate missing traces, greatly improve prestack amplitude
analysis.
Next: HEMNO Equivalence with Levin
Up: Conclusions \label>chapter:conclusions>
Previous: Conclusions on the 2-D
Stanford Exploration Project
5/30/2004