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

Abstract

Surface-related multiple elimination (SRME) has proven to be a robust surface multiple and primary estimation tool for decades. However, surface-related multiple leakage is still commonly observed in SRME-processed results due to imperfect multiple predictions. Usually, adaptive subtraction cannot fully correct for these effects without primary damage. Local primary-and-multiple orthogonalization (LPMO) has recently been proposed to partially mitigate surface multiple leakage, by multiplication of the estimated primaries with a weight function that scales down residual multiples while preserving primaries. The weight function is determined by shaping regularization followed by thresholding and median filtering. Although effective leakage extraction can be achieved, LPMO has a large computational cost due to many conjugate-gradient iterations within the shaping regularization-based inversion framework. Using information on the typical local coherency length of primaries and multiples, a spatially constrained scaled point-by-point division can be used to avoid the iterative inversion within the LPMO method. Based on this, we have adopted a fast LPMO (FLPMO) for surface-related multiple leakage estimation and extraction. Applications on two different field data sets demonstrate the very similar surface multiple leakage extraction performance for LPMO and FLPMO, while showing that the scaled point-by-point division in FLPMO is approximately 40 times faster on real data sets than the shaping regularization-based inversion in LPMO.