Bayesian ground‐roll separation by curvelet‐domain sparsity promotion

Abstract

The removal of coherent noise generated by surface waves in land based seismic is a prerequisite to imaging the subsurface. These surface waves, termed as ground roll, overlay important reflector information in both the t‐x and f‐k domains. Standard techniques of ground‐roll removal commonly alter reflector information. We propose the use of the curvelet domain as a sparsifying transform in which to preform signal‐separation techniques that preserves reflector information while increasing ground‐roll removal. We look at how this method preforms on synthetic data for which we can build quantitative results and a real field data set.