The Common Reflection Surface (CRS) method is a powerful extension of the well established Common Midpoint (CMP) method in the sense that it is able to accept, at each trace location on the zero-offset (ZO) section to be constructed, reflection data from source and receiver pairs that are arbitrarily located around that point. The CRS method uses the general hyperbolic moveout, that depends, in the 2D situation considered in this work, on three parameters. One of these parameters is the classical NMO velocity. As in the single-parameter CMP method, the CRS parameters or attributes are estimated by a direct application of suitable coherence analysis to the input multicoverage data. The estimation of the three CRS parameters is generally performed in two steps. The first step has a global character and aims in obtaining an initial estimate of the parameters. The second step has a local character, trying to refine the previous initial values to more accurate values. Here we focus on the refinement step assuming that initial estimates have been already provided. We review and compare three of these methods and compare their performances on illustrative synthetic and real data examples. Comparisons with the application of the conventional CMP method are also provided.
CRS; Optimization; Stacking
