Complementary Data-driven Methods for Interbed Demultiple of Land Data

Interbed multiples occur in all types of seismic datasets and are difficult to address. These multiples can appear very similar to primaries due to comparable velocities, and may be difficult to differentiate from primary energy and/or distort the amplitude of primary reflections. Hence, traditional methods based on velocity, dip discrimination, or periodicity, generally may not be capable of attacking these multiples but data-driven techniques such as SRME and IMP have provided better results. In this paper, complementary data-driven techniques, namely DID and XIMP, for predicting interbed demultiples have been discussed. These robust algorithms are suitable for all acquisition geometries, whether 2D or 3D, including land orthogonal geometries and provide an effective attenuation of interbed multiples.