Velocity analysis using similarity-weighted semblance

Discussion

Velocity analysis is of exceptional importance to the whole seismic data processing tasks and the NMO based velocity picking can provide the very initial velocity model for all the other velocity analysis approaches. Most of the current velocity-picking procedures are based on manual endeavors and the resolution of the velocity spectrum greatly affects the finally picked velocity. The above two reasons make the subject of this paper significant. Even with automatic picking algorithms, the proposed similarity-weighted semblance can still be superior than the traditional semblance, which has been demonstrated by the third synthetic and the second field data examples.

When implementing the proposed algorithm, we also need to calculate the traditional semblance in order to obtain a stacked reference trace. The inappropriate traditional stacking result will result in inappropriate calculation of the similarity-weighted semblance. In practice, we need to iterate several times to obtain an acceptable reference trace. Because of the iteration and the calculation of the local similarity, the proposed semblance will have some extra computational cost. However, the computational efficiency is still acceptable, and considering that the semblance calculation can be parallelized CMP by CMP, the computational cost is not a serious issue.

The proposed similarity-weighted semblance can be conveniently implemented based on the traditional semblance calculation framework. The only change is to weight each trace using the local similarity. The local similarity is a robust local attribute that can be easily calculated and has found many applications in exploration geophysics field (Chen and Fomel, 2015; Liu et al., 2009). Thus, the proposed approach has the potential to be widely used in the industry.

Velocity analysis using similarity-weighted semblance