Global Optimization of the Parameters of the Common Reflection Surface Traveltime Using Differential Evolution

Abstract

Summary The common-reflection surface (CRS) method is a sophisticated alternative to the traditional common-midpoint (CMP) stacking, as its traveltime approximation is more accurate than the normal moveout. This in turn requires more parameters for the moveout description, implying in the increase of the computational burden. In fact, the great challenge to make this method widely used in seismic data analysis has being the trade-off between the accuracy of the estimation of the traveltime parameters and the corresponding computational complexity. To cope with this problem, in this work we estimate the CRS parameters using the Differential Evolution (DE) global optimization algorithm. As we aim at critical data sets, from which no reliable prior information can be easily extracted, we propose to apply this algorithm in a fully automatic global search without any guide. The results for a 2D real data set from Brazil indicates that the global strategy yields good results, both in terms of image quality as in the quality of the parameters, especially the stacking velocity estimates, while keeping the computational costs relatively low.