1887

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.

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/content/papers/10.3997/2214-4609-pdb.172.SBGF0265_07
2007-11-19
2024-03-29
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609-pdb.172.SBGF0265_07
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