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Title: Signal-Preserving Erratic Noise Attenuation via Iterative Robust Sparsity-Promoting Filter

Abstract

Sparse domain thresholding filters operating in a sparse domain are highly effective in removing Gaussian random noise under Gaussian distribution assumption. Erratic noise, which designates non-Gaussian noise that consists of large isolated events with known or unknown distribution, also needs to be explicitly taken into account. However, conventional sparse domain thresholding filters based on the least-squares (LS) criterion are severely sensitive to data with high-amplitude and non-Gaussian noise, i.e., the erratic noise, which makes the suppression of this type of noise extremely challenging. Here, in this paper, we present a robust sparsity-promoting denoising model, in which the LS criterion is replaced by the Huber criterion to weaken the effects of erratic noise. The random and erratic noise is distinguished by using a data-adaptive parameter in the presented method, where random noise is described by mean square, while the erratic noise is downweighted through a damped weight. Different from conventional sparse domain thresholding filters, definition of the misfit between noisy data and recovered signal via the Huber criterion results in a nonlinear optimization problem. With the help of theoretical pseudoseismic data, an iterative robust sparsity-promoting filter is proposed to transform the nonlinear optimization problem into a linear LS problem through anmore » iterative procedure. The main advantage of this transformation is that the nonlinear denoising filter can be solved by conventional LS solvers. Lastly, tests with several data sets demonstrate that the proposed denoising filter can successfully attenuate the erratic noise without damaging useful signal when compared with conventional denoising approaches based on the LS criterion.« less

Authors:
 [1]; ORCiD logo [2];  [2]; ORCiD logo [3]
  1. China University of Petroleum (East China), Qingdao (China). CNPC Key Laboratory of Geophysical Prospecting; Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao (China)
  2. China University of Petroleum (East China), Qingdao (China). CNPC Key Laboratory of Geophysical Prospecting; Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao (China)
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). National Center for Computational Sciences
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1435185
Grant/Contract Number:
AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
IEEE Transactions on Geoscience and Remote Sensing
Additional Journal Information:
Journal Name: IEEE Transactions on Geoscience and Remote Sensing; Journal ID: ISSN 0196-2892
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Erratic noise; incoherent noise attenuation; nonlinear optimization; robust misfit criterion; signal preserving

Citation Formats

Zhao, Qiang, Du, Qizhen, Gong, Xufei, and Chen, Yangkang. Signal-Preserving Erratic Noise Attenuation via Iterative Robust Sparsity-Promoting Filter. United States: N. p., 2018. Web. doi:10.1109/TGRS.2018.2802462.
Zhao, Qiang, Du, Qizhen, Gong, Xufei, & Chen, Yangkang. Signal-Preserving Erratic Noise Attenuation via Iterative Robust Sparsity-Promoting Filter. United States. doi:10.1109/TGRS.2018.2802462.
Zhao, Qiang, Du, Qizhen, Gong, Xufei, and Chen, Yangkang. Fri . "Signal-Preserving Erratic Noise Attenuation via Iterative Robust Sparsity-Promoting Filter". United States. doi:10.1109/TGRS.2018.2802462.
@article{osti_1435185,
title = {Signal-Preserving Erratic Noise Attenuation via Iterative Robust Sparsity-Promoting Filter},
author = {Zhao, Qiang and Du, Qizhen and Gong, Xufei and Chen, Yangkang},
abstractNote = {Sparse domain thresholding filters operating in a sparse domain are highly effective in removing Gaussian random noise under Gaussian distribution assumption. Erratic noise, which designates non-Gaussian noise that consists of large isolated events with known or unknown distribution, also needs to be explicitly taken into account. However, conventional sparse domain thresholding filters based on the least-squares (LS) criterion are severely sensitive to data with high-amplitude and non-Gaussian noise, i.e., the erratic noise, which makes the suppression of this type of noise extremely challenging. Here, in this paper, we present a robust sparsity-promoting denoising model, in which the LS criterion is replaced by the Huber criterion to weaken the effects of erratic noise. The random and erratic noise is distinguished by using a data-adaptive parameter in the presented method, where random noise is described by mean square, while the erratic noise is downweighted through a damped weight. Different from conventional sparse domain thresholding filters, definition of the misfit between noisy data and recovered signal via the Huber criterion results in a nonlinear optimization problem. With the help of theoretical pseudoseismic data, an iterative robust sparsity-promoting filter is proposed to transform the nonlinear optimization problem into a linear LS problem through an iterative procedure. The main advantage of this transformation is that the nonlinear denoising filter can be solved by conventional LS solvers. Lastly, tests with several data sets demonstrate that the proposed denoising filter can successfully attenuate the erratic noise without damaging useful signal when compared with conventional denoising approaches based on the LS criterion.},
doi = {10.1109/TGRS.2018.2802462},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
number = ,
volume = ,
place = {United States},
year = {Fri Apr 06 00:00:00 EDT 2018},
month = {Fri Apr 06 00:00:00 EDT 2018}
}

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