Anti-aliasing multiple prediction beyond two dimensions

Yalei Sun

yalei@sep.stanford.edu

ABSTRACT

Theoretically, the Delft method of surface-related multiple elimination can be applied in three dimensions, 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 a huge computational cost. In this paper, I propose a more practical approach for the multi-streamer system. Instead of using large-volume missing-streamer interpolation, my method finds the most reasonable proxy from the collected dataset for each missing trace needed in the multiple prediction. Although this approach avoids missing-streamer interpolation, 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, the partially-stacked multiple contribution gather (PSMCG). Using multi-scale prediction-error filter (MSPEF) theory, this approach interpolates the PSMCG in the cross-line direction to remove the aliasing noise.