General Surface-related Multiple Prediction with Model-driven MCG Aperture Determination

The surface-related multiple elimination is one of challenging tasks in marine seismic data processing. It has been proven that 3D SRME is a highly effective method to attenuate both of in-plane and out-of-plane surface-related multiples. However, the conventional 3D SRME requires seismic data regularization and interpolation prior to multiple prediction, so the corresponding multiple prediction workflow is very tedious. In addition, the conventional 3D SRME is a highly memory-bounded algorithm in terms of multiple prediction, that limits its application to large seismic gathers. Combined general surface-related multiple prediction and model-driven MCG aperture determination, an improved SRME is introduced in this paper. The real data examples show that the proposed method can greatly improve the efficiency and reduce the memory requirement of multiple prediction without obvious loss of multiple prediction accuracy.