Velocity Analysis of Simultaneous-source Data Using Similarity-weighted Semblance

Direct imaging of simultaneous-source data, without the need of deblending, requires a precise subsurface velocity model. In this paper, we focus on velocity analysis of simultaneous-source data using the NMO based velocity picking approach. We demonstrate that it is possible to obtain a precise velocity model directly from the blended data in the common-midpoint (CMP) domain. More specifically, the similarity-weighted semblance can help us to obtain much better velocity spectrum with higher resolution and higher reliability. We use both synthetic and field data examples to demonstrate the performance of the proposed approach.