B035 Surface Related Multiple Prediction from Incomplete Data F.J. Herrmann* (University of British Columbia) SUMMARY Incomplete data unknown source-receiver signatures and free-surface reflectivity represent challenges for a successful prediction and subsequent removal of multiples. In this paper a new method will be represented that tackles these challenges by combining what we know about wavefield (de-)focussing by weighted convolutions/correlations and recently developed curvelet-based recovery by sparsity-promoting inversion (CRSI). With this combination we are able to leverage recent insights from wave physics towards a nonlinear formulation for the multiple-prediction problem that works for incomplete data and without detailed knowledge on the surface


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