Efficient dip-angle angle-domain common-image gather estimation using Poynting vector in acoustic reverse time migration and its application in noise suppression

In reverse time migration, we can produce angle-domain common-imaging gathers in either the scattering-angle domain or the dip-angle domain. The latter, although not used as widely as the former, still provides a unique view to look into seismic imaging. The reverse time migration dip-angle angle-domain common-image gather can be computed via the subsurface offset extension, which is a bit costly in storage. We here obtain dip-angle angle-domain common-imaging gathers efficiently in acoustic reverse time migration by using the normalized Poynting vectors. Poynting vector, an indicator of the directional energy flux, is easy to compute during wavefield propagation. Similar to the subsurface-offset method, our method also has dip-angle angle-domain common-imaging gathers of blocky horizontal coherence. In the theory of local semblance analysis, the signal with better horizontal coherence has a higher semblance score, and vice versa. Based on the estimated semblance scores, we can thus design a specularity filter to preserve valid signals and suppress noises. We validate our method with two data sets. Both the Marmousi data and the real data show that our method works effectively in eliminating noises and enhancing resolution in the acoustic reverse time migration imaging.