The common-reflection surface (CRS) method represents a multidimensional stacking approach; i.e., the stacking surface is determined in the midpoint and offset directions. In the 2D case, three attributes span the stacking surface, thus requiring a three-parameter search contrary to a one-parameter search in the classic common-midpoint stack. However, CRS wavefront attributes use data redundancy in the midpoint direction as well, which makes them very useful in several seismic applications, e.g., data preconditioning, velocity model building, and migration. Contrary to previous works, we simultaneously estimate CRS attributes using differential evolution in subcubes of the 3D search space. Differential evolution is a global optimization technique that performs particularly well when the objective function is unknown. Because we apply DE for each subcube, we could find local maxima, additionally to the global maximum. Therefore, conflicting dips are recognized and can be used for the stack and subsequent CRS attribute-based processing, which has been an issue in the past. Our land data results from the Donbas Foldbelt in southeast Ukraine demonstrate that our method reduces coherent steep dipping noise and reveal more subsurface structures. Application of the CRS attributes for prestack data enhancement shows that velocity analysis can be carried out more reliably.
A new evolutionary search strategy for global optimization of high-dimensional problems
