We have developed a new iterative method for simultaneous source separation (deblending). The proposed technique adopts the robust sparse Radon transform to define a coherence pass operator, which, used in conjunction with the steepest descent method, guarantees solutions that honor simultaneous source records. We show that a substantial improvement in convergence is attainable when we adopt a robust coherence pass projection, such as the robust sparse Radon transform by ADMM method. The improvement 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 conserves coherent signals and annihilates incoherent blending noise right from the start of the iterative process. We compare the algorithm with its non-robust version and show that a coherence pass robust Radon operator can attain high-quality results for both synthetic and real data examples.
Presentation Date: Monday, October 12, 2020
Session Start Time: 1:50 PM
Presentation Time: 1:50 PM
Location: Poster Station 11
Presentation Type: Poster
