Hybrid-Sparsity Constrained Dictionary Learning for Iterative Deblending of Extremely Noisy Simultaneous-Source Data

Zu, Shaohuan; Wu, Ru‐Shan; Mao, Weijian; Chen, Yangkang

doi:10.1109/tgrs.2018.2872416

Cited by 63 publications

(12 citation statements)

References 59 publications

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“…Note that, the signal is coherent and the blending noise is incoherent for each blended record in the common-receiver domain, then the signal can be recovered using the iterative method based on the sparsity constraint in some sparse transform domains [58], [59]. We apply the inverse dithering operator 1 …”

Section: A Inversion-based Deblendingmentioning

confidence: 99%

Iterative Deblending of off-the-Grid Simultaneous Source Data

et al. 2021

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Simultaneous source acquisition can enhance the seismic data quality or improve the field acquisition efficiency. However, one of the disadvantages is that the simultaneous source data are often obtained on a non-uniform sampled grid in realistic acquisition. Except for the blending noise introduced by the temporal overlap, the non-uniform samplings usually causes serious artifacts which also greatly reduces the data quality and imaging resolution, such that data regularization must be implemented during the deblending. At present, most deblending algorithms are suitable for the uniformly sampled data. Thus, we propose the nonequispaced fast discrete curvelet transform-based deblending approach to deal with the nonuniformly sampled simultaneous source data. In order to enhance the deblending efficiency, we introduce the iterative shrinkage thresholding algorithm to solve the undetermined problem via a soft thresholding operator. Once the coefficients in the curvelet domain are acquired, uniformly sampled deblended data can be obtained via inverse curvelet transform. During the iterations, the threshold is decreased from a large value to a small value according to the new exponential function. Compared with the existing deblending methods that are applied on uniform grids, the key contribution is that the proposed method can directly work on the non-uniformly sampled simultaneous source data. The numerical examples are used to demonstrate the successful performance of the proposed method in attenuating blending noise and correcting the distorted events.

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Section: A Inversion-based Deblendingmentioning

confidence: 99%

Iterative Deblending of off-the-Grid Simultaneous Source Data

et al. 2021

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“…A joint sparse and low rank approximation, which is a robust implementation of rank reduction method, has been proven to be an effective denoising methodology, which can be based either on alternating direction method of multipliers or accelerated proximal gradient methods (Candès et al ., 2011; Sternfels et al ., 2015; Jeong et al ., 2020a). In addition, there have been machine learning‐based methods that are erratic noise removal applications of hybrid‐sparsity constrained dictionary learning and convolutional neural networks (Zu et al ., 2019; Baardman and Hegge, 2020).…”

Section: Introductionmentioning

confidence: 99%

Seismic erratic noise attenuation using unsupervised anomaly detection

Jeong

Almubarak

Tsingas

2021

Geophysical Prospecting

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This study introduces a new attribute to identify seismic erratic noise, i.e. outlier, in the context of unsupervised anomaly detection and is defined as local outlier probabilities. The local outlier probabilities calculate scores of degrees of isolation, i.e. outlier‐ness, for each object in a data set, which represents how far an object is deviated from its surrounding objects. Since the local outlier probabilities combines a density‐based outlier detection method with a statistically oriented scheme, its scoring system provides regularized outlier‐ness, which is an outlier probability, to be used for making a binary decision to do inclusion or exclusion of an object; such a decision only requires a simple and straightforward threshold on a probability. Based on the binary decision that flags outliers versus non‐outliers, local outlier probabilities‐denoising workflows are developed by combining multiple steps to complete an application of the local outlier probabilities to attenuate seismic erratic noise. Higher stability and improved robustness in the detection and rejection of seismic erratic noise have been achieved by implementing moving windows and decision tree‐based processes. To avoid loss of useful signal energy, signal enhancement applications are additionally suggested. Numerical experiments on synthetic data investigate the applicability of the proposed algorithms to seismic erratic noise attenuation. Field data examples demonstrate the feasibility of a local outlier probabilities‐denoising application as an effective tool in seismic denoising portfolio.

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“…It is mainly used in the smooth noise reduction and recovery of coding effect of digital video [9]. The most significant feature of spatiotemporal noise reduction filter is that it is itself a nonlinear three-dimensional signal processing process, which can reduce the spatial noise of each frame while taking advantage of the temporal information of the video sequence and effectively preserving the edge and texture information of the image [10]. The video processing based on spatiotemporal filtering will be targeted to repair the noise according to its specific characteristics, thus helping to solve some key problems in the field of digital TV and film picture quality improvement and effectively improve the performance of the signal system.…”

Section: Introductionmentioning

confidence: 99%

Partial Differential Equations-Based Iterative Denoising Algorithm for Movie Images

Sun

Wang

et al. 2021

Advances in Mathematical Physics

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Film video noise can usually be defined as the error information visible on the video image, caused by the digital signal system. This distortion is inevitably present in the video obtained by various camera equipment. Noise reduction techniques are important preprocessing processes in many video processing applications, and its main goal is to reduce the noise contained in a video image while preserving as much of its edge and texture information as possible. In this paper, we describe in detail the principles of the space-time noise reduction filter, propose a 3D-filter algorithm for Gaussian noise, an improved 3D-filter algorithm based on the 3D-BDP (bloom-deep-split) filter for mixed noise, and a filter algorithm for luminance and color noise in low-brightness scenes. By dissecting the partial differential equation (PDE) denoising process, we establish a new iterative denoising algorithm. The partial differential equation method can be considered as the iterative denoising of the filter, and the first stage of the new algorithm uses wavelet-domain adaptive Wiener filter as the filtering base and achieves good results by adjusting the parameters. The proposed model in this paper is compared with the existing denoising model, and the analysis results show that the model proposed in this section can effectively remove multiplicative noise. The experimental report shows that the parameters set by the algorithm have some stability and can achieve good processing results for multiple images, which is an advantage over the partial differential equation method for denoising. The second stage of the algorithm uses the appropriate partial differential equation method to remove the pseudo-Gibbs in the first stage, which further improves the performance of the algorithm. After the image containing Gaussian noise is processed by the new algorithm, the pseudo-Gibbs effect, which often occurs in wavelet denoising, is eliminated, and the step effect, which occurs in partial differential equation denoising, is avoided; the details are better preserved, and the peak signal-to-noise ratio is improved, and a large number of experiments show that it is an effective denoising method.

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Hybrid-Sparsity Constrained Dictionary Learning for Iterative Deblending of Extremely Noisy Simultaneous-Source Data

Cited by 63 publications

References 59 publications

Iterative Deblending of off-the-Grid Simultaneous Source Data

Iterative Deblending of off-the-Grid Simultaneous Source Data

Seismic erratic noise attenuation using unsupervised anomaly detection

Partial Differential Equations-Based Iterative Denoising Algorithm for Movie Images

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