Wavefront attribute estimation for 3D laterally heterogeneous media

Wavefront attributes play a major role in many processing steps like wavefront tomography. For the determination of wavefront attributes various stacking operators can be used by adopting semblance optimization. These operators are initially designed for media with small lateral velocity variations. In praxis, however, they are also applied to media with strong lateral velocity variations like salt structures. This leads to the question of the quality of the wavefront attributes when determined in the presence of strong lateral velocity changes. We compare data-driven and model-driven wavefront attributes where the data-driven attributes are determined by the common-reflection-surface (CRS) operator and the model-driven attributes are computed by kinematic and dynamic ray tracing. Reflection seismic data of the laterally heterogeneous 3D SEG C3WA salt model are considered and the model-driven attributes are computed for a smoothed version of the SEG salt model. The comparison reveals that the wavefront attributes and particularly the wavefront curvature of the normal-incidence-point (NIP) wave shows a very good match not only in areas of mild lateral velocity variation but even in regions with strong lateral velocity variations. Approximately 80% to 90% of the total NIPs show the good match with a relative error (ER) of less than 10%. This is a satisfying result and confirms the use of the NIP wavefront curvature for wavefront tomography even in media with strong lateral velocity variations. The normal (N) wavefront curvature observed for the strong lateral heterogeneous medium, however, displays a big deviation between the data- and model-driven fashion.