Elastic internal multiple prediction using Marchenko and interferometric methods

Conventional seismic processing requires that data contain only primary reflections, while real seismic recordings also contain multiples. As such, it is desirable to predict, identify and attenuate multiples in seismic data. This task is even more challenging in elastic (solid) media. In this work, we develop a method to predict prestack internal multiples in general elastic media based on the Marchenko method and convolutional interferometry. It can be used to directly identify multiples in prestack data or migrated sections, as well as to attenuate internal multiples by adaptively subtracting them from the original dataset. We demonstrate the method on a synthetic dataset containing horizontal and vertical density and velocity variations. The full elastic method is computationally expensive and ideally uses data components that are not usually recorded. We therefore test an acoustic approximation to the method on the synthetic elastic data, and show that although the spatial resolution of the resulting image is reduced by this approximation, the multiples are still predicted accurately with minor artifacts. We conclude that in most cases where cost is a factor and we are willing to sacrifice some resolution, it may be sufficient to apply the acoustic version of this demultiple method.