Iterative deblending with multiple constraints based on shaping regularization

Published as IEEE Geoscience and Remote Sensing Letters, 2015, doi: 10.1109/LGRS.2015.2463815

Iterative deblending with multiple constraints based on shaping regularization

Yangkang Chen
Bureau of Economic Geology
John A. and Katherine G. Jackson School of Geosciences
The University of Texas at Austin
University Station, Box X
Austin, TX 78713-8924

Abstract:

It has been shown previously that blended simultaneous-source data can be successfully separated using an iterative seislet thresholding algorithm. In this paper, I combine the iterative seislet thresholding with the local orthogonalization technique via the shaping regularization framework. During the 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 leakage energy using iterative orthogonalization. It is the first time that multiple constraints are applied in an underdetermined deblending problem and the new proposed framework can overcome the drawback of low-dimensionality constraint in the traditional 2D deblending problem. Simulated synthetic and field data examples show superior performance of the proposed approach.

Iterative deblending with multiple constraints based on shaping regularization