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Multi-dimensional data regularization for modern acquisition
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 10th International Congress of the Brazilian Geophysical Society, Nov 2007, cp-172-00263
Abstract
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 show an improved continuity of the data and preservation of the data character even where we observe conflicting dips. Our one-pass approach can handle empty and overfold bins thus making use of all recorded traces and simplify the pre-stack processing flow.