Hybridised Weighted Boot-Strap Differential Semblance

Abstract

Velocity analysis is often necessary in pre-stack seismic processing to produce a good estimate of subsurface velocities. It requires the picking of moveout velocities on the semblance spectra. The semblance spectra is hampered with noise and lack of resolution around the peak representing the best move out velocity approximation. In this paper we introduce a new semblance scheme to reduce spectral noise and increase resolution in the semblance domain. The new scheme is based on a simple amalgamation of previously developed semblance enhancement methods. These methods are; the local-similarity weighted semblance, velocity-sensitivity weighted semblance and boot-strapped differential semblance. Velocity sensitivity semblance weights all traces based on sensitivity in the semblance spectra to changes in velocity, the local similarity weighting accounts for correlation between the stacked gather trace and all traces within the gather on a temporally localised scale and the boot-strapped differential semblance scheme weights the semblance spectra based on enhancing sensitivity to lateral differences in amplitudes. We test the proposed scheme on synthetic and real trace gathers. The test results show that our approach significantly improves the resolution and noise attenuation in the semblance spectra albeit to other semblance schemes.