Closed-Loop Surface Related Multiple Estimation

Abstract

Surface-related multiple elimination (SRME) is one of the most commonly used methods for suppressing surface multiples. However, in order to obtain an accurate surface multiple estimation, dense source and receiver sampling is required. The traditional approach to this problem is 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 is not recovered, e.g. near-offset data in shallow-water environments. We propose a solution in which multiple estimation is performed simultaneously with data reconstruction, such that data reconstruction helps obtaining better multiple estimates and in which the physical primary-multiple relationship helps constraining the data interpolation. To accomplish this we propose to extend the recently introduced Closed-Loop SRME (CL-SRME) algorithm to account for primary estimation in the case of coarsely sampled data. This is achieved by introducing a focal domain parameterization of the primaries in a sparsity-promoting CL-SRME method. Results proof that the method is capable of reliably estimating primaries data in case of shallow water and with large undersampling factors.