Fast local primary-and-multiple orthogonalization for surface-related multiple estimation

Abstract

Surface-related multiple elimination (SRME) has already been proven as a solid multiple and primary estimation tool for decades due to its data-driven property and strong physics behind. However, surface-related multiple leakage is still commonly seen in the SRME processed results, which might arise from the imperfect sampling and the balancing effect of the adaptive subtraction. Local primary-and-multiple orthogonalization (LPMO) is recently proposed to mitigate the multiple leakage. LPMO framework includes two separate steps: an initial multiple and primary estimation step via conservative SRME and an external multiple leakage extraction step via LPMO. Although decent leakage extraction can be achieved, LPMO requires a large computational cost due to many conjugate-gradient iterations within the shaping regularization based inversion framework. Assuming that the scalar LPMO weight is closely related to its neighboring time-and-space points, a scaled point-by-point division can be used to avoid the iterative inversion of LPMO. Therefore, we propose a fast LPMO (FLPMO) for surface-related multiple estimation. Applications on two different field data sets demonstrate the nearly same multiple leakage extraction performances for both LPMO and FLPMO, while showing that, the FLPMO is much faster than LPMO.