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Structured Graph Dictionary Learning and Application on the Seismic Denoising
IEEE Transactions on Geoscience and Remote Sensing, 2019Sparse coding method has been used for seismic denoising, as the data can be sparsely represented by the sparse transform and dictionary learning (DL) methods. DL methods have attracted wide attention because the learned dictionary is adaptive. However, for seismic denoising, the dictionary learned from the noise data is a mix of atoms representing ...
Lina Liu 0004, Jianwei Ma
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Deep Learning for Simultaneous Seismic Image Super-Resolution and Denoising
IEEE Transactions on Geoscience and Remote Sensing, 2020Seismic interpretation is often limited by low resolution and strong noise data. To deal with this issue, we propose to leverage deep convolutional neural network (CNN) to achieve seismic image super-resolution and denoising simultaneously. To train the CNN, we simulate a lot of synthetic seismic images with different resolutions and noise levels to ...
Jintao Li, Xinming Wu, Zhanxuan Hu
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Seismic denoising based on modified BP neural network
2010 Sixth International Conference on Natural Computation, 2010A new method for seismic random noise reduction based on robust function and back propagation (BP) neural network is proposed in this paper. This method introduces BP neural network utilizing least mean log squares (LMLS) error function or least trimmed squares (LTS) estimator instead of least mean squares (LMS) error function as its error function ...
Yinxue Zhang +3 more
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Operational Seismic Denoising Workflow to Enhance Seismic Catalogues
Recent studies have demonstrated the potential of deep learning (DL) techniques for denoising seismic signals and improving signal analysis, but they are not yet widely adopted in seismic monitoring. Denoising models are typically applied to short segments of triggered data.Nikolaj Dahmen +2 more
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Seismic signal denoising method based on curvelet transform
2010 Sixth International Conference on Natural Computation, 2010Considering the characteristic of curvelet coefficients in difference levels, a adaptive threshold denoising method is proposed by using fast discrete curvelet transform. Using total variation minimization reduces the noise while edges are preserved.
Aidi Wu, Xiuling Zhao
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Seismic Denoise Using Side Window Filter
EAGE 2020 Annual Conference & Exhibition Online, 2020Summary Local windows are routinely used in seismic data and almost without exception the center of the window is aligned with the points being processed. When a point is on an edge, placing the center of the window on the point is one of the fundamental reasons that cause many filtering algorithms to blur the edges. Based on this insight, we use a new
D. Chang, G. Zhang, Y. Wang, J. Zhang
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Widely linear denoising of multicomponent seismic data
Geophysical Prospecting, 2019ABSTRACTSeismic data processing is a challenging task, especially when dealing with vector‐valued datasets. These data are characterized by correlated components, where different levels of uncorrelated random noise corrupt each one of the components.
Breno Bahia, Mauricio D. Sacchi
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Mask-Guided Model for Seismic Data Denoising
IEEE Geoscience and Remote Sensing Letters, 2022Ziyi Fang, Hongbo Lin, Xinyu Xu
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NS2NS: Self-Learning for Seismic Image Denoising
IEEE Transactions on Geoscience and Remote Sensing, 2022Naihao Liu +6 more
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Nonlocal Total Variation Denoising of Seismic Data
Proceedings, 2013Seismic denoising can be considered to be a total variation minimization problem. Nonlocal total variation (NLTV) denoising is one of the best denoising models and is widely used in image processing. Combined with Split-Bregman algorithm, the computational efficiency of NLTV regularization can be improved, making it able to handle large data set.
S. Shang, L.G. Han
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