Reliability of data-driven wavefront attributes in laterally heterogeneous media

Xie, Yujiang and Gajewski, Dirk (2019) Reliability of data-driven wavefront attributes in laterally heterogeneous media. Geophysics, 84 (3), O49-O62. (doi:10.1190/geo2018-0382.1).

Abstract

3D wavefront attributes play a major role in many processing steps, such as prestack data enhancement, diffraction separation, and wavefront tomography. For the determination of the 3D wavefront attributes, various stacking operators can be used by adopting semblance optimization. These operators are derived for laterally homogeneous media. In praxis, however, they are applied in real geologic environments with even strong lateral velocity variations such as salt structures. This leads to the question of the quality of the 3D wavefront attributes using these operators when determined in the presence of strong lateral velocity changes. We compared the 3D wavefront attributes determined by 3D common-reflection-surface (CRS) operator (called data-driven wavefront attributes) with the 3D wavefront attributes computed by 3D kinematic and dynamic ray tracing (called model-driven wavefront attributes). For the determination of the 3D CRS wavefront attributes, we have developed a global optimization scheme based on differential evolution. Reflection seismic data of the laterally heterogeneous 3D SEG C3WA salt model are considered, and the model-driven wavefront attributes are computed for a smoothed version of the 3D SEG salt model. The comparison reveals that the wavefront attributes for the normal-incidence-point ray indicate a very good match not only in areas of mild lateral velocity variation but even in regions with strong lateral velocity variations. Approximately 80%–90% of the total picks indicate the good match with a relative error of less than 10% when a semblance threshold of 0.1 is considered in the automatic picking process. This confirms the validity of the determined wavefront attributes even in the presence of strong lateral velocity changes. Using a higher semblance threshold in the automatic picking leads to fewer picks but with an even better match between model- and data-driven wavefront attributes.

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