3D surface multiple prediction using sparse inversion

Although the theory of iterative surface-related multiple elimination applies to both 2D and 3D wave-fields, the procedure is in practice only applied to 2D (or 2D subsets of 3D) datasets. The method involves (Kirchhoff) summations of the extrapolated input data (multiple contributions), that requires alias (and edge effect) protection, a requirement not easily met by 3D datasets. With current marine acquisition geometries the seismic data are acquired densely sampled in the in-line direction but (very) sparsely in the cross-line direction. Therefore, in this paper, we propose a method to predict surfacerelated multiples in a true 3D sense that takes the sparse (crossline) sampling of the seismic data into account. In this method the (sparse) cross-line Kirchhoff summation is replaced with a sparse parametric inversion that extracts the 3D multiple information (i.e. the Fresnel zones at the surface of the extrapolated wave-field), present in the sparsely sampled cross-line multiple contributions, which is then translated into (3D) predicted multiple traces.