Focal deblending using smart subsets of OBN 5D data

Abstract

Although theoretically straightforward, adapting focal deblending to realistic 5D acquisition scenarios can be challenging in practice. The two main issues that have to be dealt with are insufficiently sampled spatial dimensions and the computational effort needed for the deblending inversion. In order to deal with both issues, we propose dividing the data in 'smart' subsets, specialized for the acquisition type. Then, the deblending problem can be divided into a number of smaller problems that can be solved independently. The focal transform used for the deblending is also redefined to fit the geometry of the subsets. We examine the case of OBN acquisition and test the performance of the proposed scheme on numerically blended field data.