Deblending by iterative orthogonalization and seislet thresholding

Abstract

It has been shown previously that blended simultaneoussource data can be successfully separated using an iterative seislet thresholding algorithm. I combine the iterative seislet thresholding with the recently proposed local signal-and-noise orthogonalization via the shaping regularization framework. During the initial iterations, the deblended data and its blending noise section are not orthogonal to each other, indicating that the noise section contains significant coherent useful energy. Although the leakage of useful energy can be retrieved by updating the deblended data from the data misfit during many iterations, I propose to accelerate the retrieval of the leakage energy using iterative orthogonalziation. Simulated synthetic and field data examples show superior performance of the proposed approach.