Separation of simultaneous sources acquired with a high blending factor via coherence pass robust Radon operators
Rongzhi Lin, Mauricio D. Sacchi
Abstract
ABSTRACT We have developed an iterative method for simultaneous source separation (deblending) suitable for data acquired with a high blending factor. Our technique adopts the robust sparse Radon transform to define a coherence pass operator that is used in conjunction with the steepest-descent method to guarantee solutions that honor simultaneous source records. We find that an important improvement in convergence is attainable when the coherence pass projection is derived from a robust sparse Radon transform. This is a consequence of having an iterative deblending algorithm that applies intense denoising to erratic blending noise in its initial iterations. The coherence pass robust Radon operator acts as a data projection operator that preserves coherent signals and annihilates incoherent blending noise right from the start of the iterative process. We compare the algorithm with its nonrobust version and find that a coherence pass nonrobust Radon operator will only achieve high-quality results for acquisitions with a moderate blending factor.