1D internal multiple prediction in a multidimensional world: errors and recommendations

Pan Pan and Kris Innanen

ABSTRACT

Internal multiples are more difficult to estimate and eliminate than free surface multiples. To eliminate these effects, internal multiple prediction becomes a necessity. In this paper, we employ the 1D internal multiple algorithm due to Weglein and collaborators in the 1990s. We review the basic principles of the internal multiple prediction algorithm. The key characteristic of the inverse scattering series based method is that information from the subsurface is not a requirement, because they are fully data-driven. Internal multiples from all possible generators are computed and shown in the output. Its performance is demonstrated using complex synthetic data sets. Then we systematically study how the presence of offset and the existence of dipping angle in the reflectors affect the 1D internal multiple prediction algorithm. Finally, we give some recommendations for the applications of this method.

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