Near-surface velocities could vary with azimuth, impacting seismic data processing and interpretation. In this study, we developed a methodology to investigate the variations of near-surface velocities with azimuth, using 3D turning-ray tomography. The input data are the first arrivals selected from pre-defined azimuth sectors in terms of shot-receiverpair directions. The output velocities from tomography correspond to the selected azimuth sectors. A near-surface tomography study based on seismic data from a shallow heavy-oil reservoir in Canada has suggested that the observed azimuthal traveltime variations are not necessarily related to azimuthal (HTI) anisotropy induced by the stress field or fractures. It could also be caused by the nearsurface heterogeneity or acquisition footprint. Near-surface complexity could masquerade as anisotropy. Potentially this can influence statics and prestack imaging.
Geological discontinuities commonly occur in nature. Sharp jumps such as distinct layering or formation of localized bodies in the subsurface can occur due to various geological processes. However, the bandwidth of the geophysical signals such as seismic or electromagnetic probing the Earth often produces smooth representations of the model via imaging or inversion. Introducing informative priors through regularization process has been shown to produce discontinuous model. Among the various classes of the regularization operators that preserve discontinuity are total variation norm, compactness constraints and Lp norm such as sparse spike solutions. In this paper we focus on a Bayesian hyper model formulation originally developed for image processing applications to geophysical inversion of the data. Both the locations and the magnitude of the discontinuities are considered unknowns which are commonly encountered in practical applications. Using a series of examples we compare this approach with other well known approaches such as total variation and L1 inversion in the wavelet domain.