3D primary estimation by sparse inversion using the focal domain parameterization

Recently, a new approach to multiple removal has been intro- duced: estimation of primaries by sparse inversion (EPSI). Al- though based on the same relationship between primaries and multiples as surface-related multiple elimination (SRME), it involves quite a different process: instead of prediction and subtraction of multiples, in EPSI the unknown primaries are the parameters of a large-scale inversion process. Based on a sparseness constraint, primaries are estimated in such a way that - together with their corresponding surface multiples - they explain the input data. In this paper a new algorithm is proposed to extend the EPSI process to the full 3D case, in which data reconstruction and primary estimation are com- bined, based on parameterization of the primaries in the so- called focal domain. This algorithm will allow reconstruction of large data gaps and yields reliable primaries. Results of this algorithm for a simple 3D example are shown.IST/Imaging Science and TechnologyApplied Science