Interpolating Big Gaps Using Inversion With Slope Constraint

Abstract

Seismic data interpolation or reconstruction plays an important role in seismic data processing. Many processing steps, such as high resolution processing, wave-equation migration, amplitude-versus-offset and amplitude-versus-azimuth analysis, require regularly sampled data. The reconstruction can be posed as an inverse problem, which is known to be ill posed and requires constraints to obtain unique and stable solutions. In this letter, we propose an iterative scheme to interpolate the big gaps with a slope constraint. In the first iteration, the smooth radius must be large to estimate the smooth dip from the decimated data, and a large scaling parameter can guarantee the stability of the inversion. In the later iterations, the smooth radius will be shortened in order to get a more accurate dip estimation from the updated result. When the dip estimation is accurate, a small scaling parameter can not only guarantee the convergence of the inversion but also obtain a result with high signal-to-noise ratio. We compare the proposed method with the well-known projection-onto-convex-sets method on synthetic and field data examples. The interpolation results illustrate the advantage of the proposed method in interpolating the big gaps.