Closed-Loop SRME with Data-reconstruction

To obtain an accurate surface multiple estimation via data-driven methods, dense source and receiver sampling is required. The traditional approach to this problem consists in performing data interpolation prior to multiple estimation. Though appropriate in many cases, this methodology fails when big data gaps are present or when relevant information (e.g. near-offset data in shallow-layer environments) is not recovered. We propose a solution in which multiple estimation is performed simultaneously with data reconstruction. For this purpose we propose to extend the recently introduced Closed-Loop SRME (CL-SRME) algorithm to account for the primary estimation in the case of coarsely sampled data. This is achieved by introducing a focal domain parameterization in a sparsity-promoting CL-SRME method. This algorithm will show its capacity to reconstruct large data gaps and provide reliable primary estimations, even in the presence of large under-sampling and missing near offsets.