Anti-aliasing multiple prediction beyond 2-D

Yalei Sun^*, Stanford University

yalei@sep.stanford.edu

ABSTRACT

Theoretically, the Delft approach of surface-related multiple elimination can be applied in three dimension, as long as the source and receiver coverage is dense enough. In reality, such a dense coverage is still far from reach, using the available multi-streamer acquisition system. One way to fill the gap is to massively interpolate the missing sources and receivers in the survey, which requires huge computational cost.

In this paper, I propose a more practical approach for the multi-streamer system. Instead of the large-volume missing-streamer interpolation, this approach tries to find the most reasonable proxy from the collected dataset for each missing trace needed in the multiple prediction. Although the missing-streamer interpolation is avoided, another problem pops up in the multi-streamer case, the aliasing noise caused by the sparse sampling in the cross-line direction. To solve this problem, I introduce a new concept, partially-stacked multiple contribution gather (PSMCG). Using the multi-scale PEF theory, this approach interpolates the PSMCG in the cross-line direction to remove the aliasing noise.