A POCS method for iterative deblending constrained by a blending mask

- A novel inversion strategy is proposed to improve the deblending performance.
- Multiple convex constraints are utilized in the POCS framework.
- A significant amount of true clean data can be extracted from the blended data.
- Both synthetic and field data examples validate the effectiveness of the proposed method.

A recently emerging seismic acquisition technology called simultaneous source shooting has attracted much attention from both academia and industry. The key topic in the newly developed technique is the removal of intense blending interferences caused by the simultaneous ignition of multiple airgun sources. In this paper, I propose a novel inversion strategy with multiple convex constraints to improve the deblending performance based on the projection onto convex sets (POCS) iterative framework. In the POCS iterative framework, as long as the multiple constraints are convex, the iterations are guaranteed to converge. In addition to the sparse constraint, I seek another important constraint from the untainted data. I create a blending mask in order to fully utilize the useful information hidden behind the noisy blended data. The blending mask is constructed by numerically blending a matrix with all its entries set to be one and then setting the non-one entries of the blended matrix zero. I use both synthetic and field data examples to demonstrate the successful performance of the proposed method.