Deblending Using Focal Transformation with a Greedy Inversion Solver

Abstract

In this work, we adopt a greedy inversion solver to design a fast version of the double focal transform that we can use to eliminate blending noise in simultaneous source acquisition. The greedy inversion introduces a coherence-oriented mechanism to enhance focusing of significant model space, leading to a sparse model space and fast convergence rate. Synthetics and numerically blended field data examples demonstrate the validity of its application for deblending. We also tested different inversion parameters (percentile value and weights) influencing the choice of the model subspace. The results indicate that by setting the percentile carefully and using weights it is possible to get better deblending results.