Anti-aliasing multiple prediction beyond two dimensions

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 largevolume 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 partiallystacked 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. INTRODUCTION The Delft approach to surface-related multiple elimination (Berkhout and Vershcuur, 1997; Vershcuur and Berkhout, 1997) formulated the demultiple process as a two-step inversion problem based on the Huygens principle, that is, first predicting the multiple and then subtracting it from the original dataset. The multiple prediction step, crucial for the success of the whole algorithm, involves one important assumption about the data acquisition geometry, namely, a source/receiver pair is needed wherever a multiple reflects. The Delft approach is quite successful in solving 2-D problems (Verschuur and Prein, 1999), in which the assumption is relatively easily satisfied. However, in many 3-D surveys, there is a large gap between this assumption and the reality (Dragoset and Jeričević , 1998). Two different directions have been taken to solve the problem. One is to interpolate the trace at the missing source and receiver positions massively to attain a dense coverage 1email: [email protected] 159