P143 Multi-Dimensional Data Regularization for Modern Acquisition Geometries G. Poole* (CGGVeritas) & P. Herrmann (CGGVeritas) SUMMARY Data regularization is critical for the suppression of Kirchhoff migration noise and the production of a clean migration image. Techniques that perform data regularization simultaneously along two axes provide a solution where multiple passes of a 1D approach fail. We introduce a versatile two-dimensional Fourier reconstruction algorithm that regularizes the input data as well as filling gaps in the coverage. We validate the algorithm on a synthetic cross-spread gather example as well as demonstrating the technology on a real offset volume dataset. The results


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