Deblending using normal moveout and median filtering in common-midpoint gathers

Conclusions

We have proposed a basic workflow for dealing with blended data. The processing domain is in CMG, where blending noise appears to be incoherent and seismic reflections appear as hyperbolic events. By applying median filtering along the spatial direction after NMO in CMG, we can easily remove the blending noise without harming useful signal. By applying inverse NMO, we can get the deblended data. In order to obtain convincing velocity estimation, we use a recursive strategy. We polish both deblended result and velocity estimation by deblending using the updated velocity estimation and velocity scanning using the updated deblended result. It is readily implemented because we use a conventional version of median filtering and don't use any other sophisticated technique to aid in the deblending process. The migrated image of the deblended data shows cleaner structure than that of blended data, which confirms the effectiveness of the proposed deblending approach.