The common-offset (CO) common-reflection-surface (CRS) is a generalization of the zero-offset (ZO) CRS, traditionally used to provide a simulated zero-offset (ZO) image of the subsurface in time. The 2D CO-CRS traveltime is parametrized by five attributes. This generalization can be applied in any CO section of the pre-stack data and is particularly interesting once it allows to perform the CRS method in pre-stack data, enabling the benefits of the SNR enhancement in other pre-stack processing flows. On the other hand, one of the greatest challenges for the CRS method is the trade-off between the accuracy of the estimation of the traveltime parameters and the corresponding computational complexity. In this paper, we propose the usage of the bio-inspired heuristic differential evolution (DE) to estimate all the 2D CO-CRS parameters simultaneously. This algorithm can significantly speed up the convergence velocity for the CRS parameters estimation and it has a small set of parameters to be configured. We apply the DE algorithm to estimate the 2D CO-CRS parameters in the synthetic Marmousi data set blurred by noise. The recovered prestack data presented a significant SNR enhancement.


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