Nonlocal Weighted Robust Principal Component Analysis for Seismic Noise Attenuation

Liu, Xingye; Chen, Xiaohong; Li, Jingye; Chen, Yangkang

doi:10.1109/tgrs.2020.2996686

Cited by 34 publications

(6 citation statements)

References 60 publications

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“…Nonetheless, since hyperspectral images consist of many channels, this method is not suitable for grayscale images. Liu et al [38] proposed a new denoising method based on nonlocal weighted robust principal component analysis (RPCA). They used the local similarity to build the objective function of RPCA and solved the problem by using an iterative log-thresholding algorithm to achieve the denoising function.…”

Section: Image Denoising Literature Reviewmentioning

confidence: 99%

An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization

et al. 2023

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In real-world scenarios, images may be affected by additional noise during compression and transmission, which interferes with postprocessing such as image segmentation and feature extraction. Image noise can also be induced by environmental variables and imperfections in the imaging equipment. Robust principal component analysis (RPCA), one of the traditional approaches for denoising images, suffers from a failure to efficiently use the background’s low-rank prior information, which lowers its effectiveness under complex noise backgrounds. In this paper, we propose a robust PCA method based on a nonconvex low-rank approximation and total variational regularization (TV) to model the image denoising problem in order to improve the denoising performance. Firstly, we use a nonconvex γ-norm to address the issue that the traditional nuclear norm penalizes large singular values excessively. The rank approximation is more accurate than the nuclear norm thanks to the elimination of matrix elements with substantial approximation errors to reduce the sparsity error. The method’s robustness is improved by utilizing the low sensitivity of the γ-norm to outliers. Secondly, we use the l1-norm to increase the sparsity of the foreground noise. The TV norm is used to improve the smoothness of the graph structure in accordance with the sparsity of the image in the gradient domain. The denoising effectiveness of the model is increased by employing the alternating direction multiplier strategy to locate the global optimal solution. It is important to note that our method does not require any labeled images, and its unsupervised denoising principle enables the generalization of the method to different scenarios for application. Our method can perform denoising experiments on images with different types of noise. Extensive experiments show that our method can fully preserve the edge structure information of the image, preserve important features of the image, and maintain excellent visual effects in terms of brightness smoothing.

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Section: Image Denoising Literature Reviewmentioning

confidence: 99%

An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization

et al. 2023

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“…Unfortunately, when dealing with DAS recordings containing complex noise, researchers have difficulty in obtaining optimal filtering parameters, which leads to noise residuals and loss of amplitude of the effective signal. In addition, many other methods have been widely used in seismic data processing including singular value decomposition (SVD) (Oropeza and Sacchi, 2011), dictionary learning methods (Chen et al, 2016;Yarman et al, 2018;Wang and Ma, 2020), robust principal component analysis (RPCA) (Cheng et al, 2015;Liu et al, 2021), but the application of these methods in DAS data denoising is rarely reported. It is difficult for conventional methods to provide a better processing effect when the DAS data is seriously disturbed by noise, and give consideration to SNR and resolution.…”

Section: Introductionmentioning

confidence: 99%

“…Supervised learning-based denoising methods need to label a large number of clean seismic data to fit the network, which will increase labor and computational costs. Therefore, some denoising models based on unsupervised learning or self-supervised learning have been proposed to address the lack of paired data in seismic signal processing (Wang et al, 2022;Yang et al, 2021;Liu et al, 2021;Qiu et al, 2022). Meanwhile, deep learning-based algorithms have also achieved good results in denoising DAS records (Zhao et al, 2022;Wang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Multi-scale interactive network in the application of DAS seismic data processing

Wang

Lin²,

Shao³

et al. 2023

Front. Earth Sci.

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Distributed acoustic sensing (DAS) is regarded as a novel acquisition technology for seismic data. Compared with conventional electrical geophones, DAS has a series of obvious advantages including low-cost, high spatial resolution, good coverage, and strong resistance to the harsh environment. Noise attenuation is an essential step in seismic data processing. However, there are two main difficulties faced by the denoising task of DAS seismic data. On the one hand, some background noise in DAS seismic data, such as optical low-frequency noise, horizontal noise, and fading noise, is unique and not presented in the conventional seismic data; on the other hand, the signal-to-noise ratio (SNR) of DAS seismic data is relatively low. Recently, a convolutional neural network (CNN) has shown superior denoising performance compared to the traditional method. To follow this promising trend, we propose a multi-scale interactive convolutional neural network (MSI-Net) and apply it to denoise the challenging DAS seismic data. Different from most of the existing CNN architecture used in seismic data denoising, the MSI-Net considers both coarse-scale and fine-scale features by improving the inherent serial convolution to multi-scale parallel convolution, which is beneficial to recover detailed information. Moreover, we utilize some connections to achieve the information interaction between different scales, which promotes the flow of information and enables the network to extract more informative multi-scale features from the DAS seismic data. Moreover, both synthetic and real examples demonstrate that the proposed MSI-Net can effectively attenuate a variety of unique DAS background noise and also completely recover the weak signals. Compared with conventional CNN architecture, MSI-Net exhibits better performance in global SNR and local details.

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“…The noise can be suppressed by reducing the rank of noisy seismic data with kernel norm optimization. Typical denoising algorithms based on rank reduction include multichannel singular spectrum analysis (MSSA) [7], principal component analysis [8], etc. Although the traditional denoising methods have made excellent performance, the effect of noise reduction is dependent on noise model assumptions and selecting parameters.…”

Section: Introductionmentioning

confidence: 99%

Self-supervised deep learning for multi-profile seismic data denoising

Chuanchao,

Song,

Zhang

et al. 2023

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023)

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Self-supervised deep learning has been widely exploited in seismic data denoising. However, most of the current methods take a single seismic profile to reconstruct two new similar profiles by sampling. Then, the two new similar profiles are conducted as input and label respectively to train the model. In fact, there obviously exists similarity and more structural information among adjacent profiles. Therefore, we want to use adjacent multiple profiles to denoise seismic data. But this ideal requires that the neural network model can process multiple profiles. In order to solve this problem, we introduce a supervised model which can input five continuous profiles and denoise the middle profile. In addition, a self-supervised training strategy is proposed for the supervised model to train with no clean profiles. The experimental results on synthetic noise show that our method can achieve a higher Signal-to-Noise Ratio (SNR). According to the experiment on real noise, the proposed algorithm also obtains cleaner and smoother denoising profiles.

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Nonlocal Weighted Robust Principal Component Analysis for Seismic Noise Attenuation

Cited by 34 publications

References 60 publications

An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization

An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization

Multi-scale interactive network in the application of DAS seismic data processing

Self-supervised deep learning for multi-profile seismic data denoising

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