Efficiency in multiple prediction, leveraging the CMP gather

Matt Eaid and Kris Innanen


Geophysics has seen a shift from mapping large-scale obvious features, to the mapping of subtle features, and accurate inversions for subsurface parameters. The presence of large amplitude multiples in the data makes both of these a challenging task, motivating the need for more robust methods of internal multiple prediction. The most successful method of internal multiple prediction is the fully data-driven algorithm based on the inverse scattering series, which has proven very successful on synthetic data, and is currently being adapted to work on land and marine data. However, one of the main hurdles obstructing its successful application is its computational expense. Typically, computationally expensive 2D algorithms must be applied when the underlying geology contains any structure, in order to produce an accurate prediction. By leveraging properties of the CMP gather we show that the 1.5D algorithm may be applied in the presence of moderate dip, greatly improving efficiency, wile maintaining a high level of accuracy.

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