Common-reflection-surface stack: Image and attributes

The common-reflection-surface stack provides a zero-offset simulation from seismic multicoverage reflection data. Whereas conventional reflection imag ing methods (e.g. the NMO/dip moveout/stack or prestack migration) require a sufficiently accurate macrovelocity model to yield appropriate results, t he common-reflection-surface (CRS) stack does not depend on a macrovelocity model. We apply the CRS stack to a 2-D synthetic seismic multicoverage dataset. We show that it not only provides a high-quality simulated zero-offset sectio n but also three important kinematic wavefield attribute sections, which ca n be used to derive the 2-D macrovelocity model. We compare the multicovera ge-data-derived attributes with the model-derived attributes computed by fo rward modeling. We thus confirm the validity of the theory and of the data- derived attributes. For 2-D acquisition, the CRS stack leads to a stacking surface depending on three search parameters. The optimum stacking surface needs to be determin ed for each point of the simulated zero-offset section. For a given primary reflection, these are the emergence angle (alpha Of the zero-offset ray, a s well as two radii of wave-front curvatures R-N and R-NIP They all are ass ociated with two hypothetical waves: the so-called normal wave and the norm al-incidence-point wave. We also address the problem of determining an opti mal parameter triplet (alpha, R-NIP, R-N) in order to construct the sample value (i.e., the CRS stack value) for each point in the desired simulated z ero-offset section. This optimal triplet is expected to determine for each point the best stacking surface that can be fitted to the multicoverage pri mary reflection events. To make the CRS stack attractive in terms of computational costs, a suitabl e strategy is described to determine the optimal parameter triplets for all points of the simulated zero-offset section. For the implementation of the CRS stack, we make use of the hyperbolic second-order Taylor expansion of the stacking surface. This representation is not only suitable to handle ir regular multicoverage acquisition geometries but also enables us to introdu ce simple and efficient search strategies fur the parameter triple. In spec ific subsets of the multicoverage data (e.g., in the common-midpoint gather s or the zero-offset section), the chosen representation only depends on on e or two independent parameters, respectively.