Differential evolution-based optimization procedure for automatic estimation of the common-reflection surface traveltime parameters

Abstract

The common-reflection surface (CRS) method is a sophisticated alternative to the traditional common-midpoint stacking because its traveltime approximation allows for the use of more traces than the normal moveout. This in turn requires more parameters for the moveout description, thus increasing the computational burden of the parameter estimation. In the literature, a suboptimal strategy is often used, which decreases the complexity but, as we found in this work, compromises the accuracy of the parameters in some cases. To cope with this problem, in this work, we have devised detailed information for efficient estimation of the CRS parameters using the differential evolution (DE) global optimization algorithm. Because we used data sets with low fold and low signal-to-noise ratio, from which no reliable velocity analysis could be easily performed, we applied this algorithm in a fully automatic global search, i.e., without any velocity guide. The results for a 2D real data set from Brazil indicated that the global strategy yielded good results, both in terms of image quality as in the quality of the parameter volumes, especially the stacking velocity estimates, while keeping the computational costs relatively low. We also developed a convergence and a sensitivity analysis of the DE that shows its computational efficiency and the robustness of the optimization method with respect to the choice of the control parameters of the algorithm.

Common‐offset common‐reflection‐surface attributes estimation with differential evolution

“…As is done for the ZO CRS method, this search strategy is referred to as sequential CO CRS search (Barros et al . ).…”

“…; Barros et al . ), where it was shown that the joint estimation of the CRS variables generates better results than those from the sequence of one‐variable searches at the expense of higher computational costs. Barros et al .…”

“…As shown in Barros et al . (), one important benefit of the global approach is that it involves fewer approximations, in comparison with the sequential approach. Recently, global strategies for estimating the CRS attributes have been investigated for the ZO CRS (Santos et al .…”

Stretch‐free generalized normal moveout correction

“…Parameter estimation is performed by the heuristic algorithm differential evolution (DE) based on the same strategy made by Barros et al . (). The stretch‐free parameter is estimated by an exhaustive search, considering semblance computations for each time sample.…”

Fast and robust common-reflection-surface parameter estimation

“…1 brute-force approach, but they are not guaranteed to find the global optimum. The most popular are simulated annealing (Müller, 2003;Garabito et al, 2012;Minato et al, 2012) and differential evolution (Barros et al, 2015).…”