Cubic-spline reconstruction of irregular seismic data using linear time shift

Abstract

Time shift technique and cubic spline interpolation are combined to reconstruct the irregularly sampled, aliased seismic data. The spatial aliasing is reduced by linear time shift, and the irregular sampling is handled by cubic spline interpolation. The method is applicable to both uniform sampling with missing traces and non-uniform sampling. It can handle linear, nonlinear and interfered events. The underling assumption is that the dip range of all events, within the whole data set or spatiotemporal window, is not too large. This method is feasible in practical applications since field data usually satisfy this assumption. As a one-pass and easily parallelized method, this technique has attractive computational cost and memory demand. For 3D seismic data, only 2D interpolation along spatial direction is required for each time slice. This shows great potential on huge volume data, especially for 3D marine data. Experiments on both synthetic and field data demonstrate the capability of the proposed method.