Multiple prediction through inversion: A fully data‐driven concept for surface‐related multiple attenuation

Abstract

This paper introduces a fully data‐driven concept, multiple prediction through inversion (MPI), for surface‐related multiple attenuation (SMA). It builds the multiple model not by spatial convolution, as in a conventional SMA, but by updating the attenuated multiple wavefield in the previous iteration to generate a multiple prediction for the new iteration, as is usually the case in an iterative inverse problem. Because MPI does not use spatial convolution, it is able to minimize the edge effect that appears in conventional SMA multiple prediction and to eliminate the need to synthesize near‐offset traces, required by a conventional scheme, so that it can deal with a seismic data set with missing near‐offset traces. The MPI concept also eliminates the need for an explicit surface operator, which is required by conventional SMA and is comprised of the inverse source signature and other effects. This method accounts implicitly for the spatial variation of the surface operator in multiple‐model building and attempts to predict multiples which are not only accurate kinematically but are also accurate in phase and amplitude.