Focal deblending using smart subsets of towed streamer 5D data

Abstract

Blended data acquisition is gaining popularity, requiring - in most cases - a deblending process before further processing and imaging is applied. Recently, focal transform-based de- blending has been proposed. By applying subsurface-based fo- cal operators to both sources and receivers, reflection informa- tion can be sparsely represented and, thereby, separated in the focal domain from blending noise. Although focal deblending can be easily extended to handle 5D data, involving all four spatial axes in the focusing process will be computationally costly. Instead, we propose dividing the data in 'smart' sub- sets that emerge naturally from the acquisition type, and using focal operators designed for those subsets. In this way, each subset can be deblended independently from the rest. We de- fine such subsets for the case of a marine towed multi-streamer acquisition and test the deblending performance on a synthetic dataset.