Data-driven demultiple techniques such as the common surface-related multiple elimination method (SRME) rely on dense, regular data input, a requirement that is too strict to be met by towed-streamer acquisition. Full-azimuth, long offset acquisition is required to properly image complex structures in the Gulf of Mexico and other complex geologic settings. The move to these more complete geometries, such as circular dual-coil shooting, has increased the offset and azimuth scope of the input required by SRME.

Generalized surface multiple prediction (GSMP) is an efficient 3D, true-azimuth variant of SRME that reduces the burden on input data regularization and interpolation by applying "on-the-fly" interpolation. This interpolation can be combined with increased data density prior to GSMP to achieve optimal predictions for demanding full-azimuth, long-offset geometries such as circular shooting. GSMP selects input traces for interpolation by minimizing the distance to desired traces, where distance is measured in four dimensions: midpoint x and y, offset, and azimuth. This distance function can be analyzed to show potential uplift from various input geometries. In this paper, we show the improvement of circular-shooting multiple prediction through the addition of wide azimuth data, then analyze the input error trend to assess potential for further improvement.