With higher capacity recording systems, long-offset surveys are becoming common in seismic exploration plays. Long offsets provide leverage against multiples, have greater sensitivity to anisotropy, and are key to accurate inversion for shear impedance and density. There are two main issues associated with preserving the data fidelity contained in the large offsets: (1) nonhyperbolic velocity analysis and (2) mitigating the migration/NMO stretch. Current nonhyperbolic velocity analysis workflows first estimate moveout velocity Vnmo based on the offset-limited gathers, then pick an effective anellipticity ηeff using the full-offset gathers. Unfortunately, estimating Vnmo at small aperture may be inaccurate, with picking errors in Vnmo introducing errors in the subsequent analysis of effective anellipticity. We have developed an automated algorithm to simultaneously estimate the nonhyperbolic parameters. Instead of directly seeking an effective stacking model, the algorithm finds an interval model that gives the most powerful stack. The searching procedure for the best interval model was conducted using a direct, global optimization algorithm called differential evolutionary. Next, we applied an antistretch workflow to minimize stretch at a far offset after obtaining the optimal effective model. The automated velocity analysis and antistretch workflow were tested on the data volume acquired over the Fort Worth Basin, USA. The results provided noticeable improvement on the prestack gathers and on the stacked data volume.

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