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convolutional sparse coding

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Book Chapter

Convolutional Sparse Coding Available to Purchase

Author(s)
Gerard Schuster
Book: Machine Learning Methods in Geoscience
Series: Geophysical Developments Series
Publisher: Society of Exploration Geophysicists
Published: 18 December 2024
DOI: 10.1190/1.9781560804048.ch15
EISBN: 9781560804048
... with surface waves, (b) denoised data, and (c) the residual. Figure 15.16: Projection of b on the convex set ‖ y ‖ 2 2 ≤ 1 . This chapter discusses how the convolutional sparse coding method can be used to eliminate both coherent and random noise in seismic data. 15.5...
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Abstract
View PDF for content titled, <span class="search-highlight">Convolutional</span> <span class="search-highlight">Sparse</span> <span class="search-highlight">Coding</span>
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This chapter discusses how the convolutional sparse coding method can be used to eliminate both coherent and random noise in seismic data.

Journal Article

Convolutional sparse coding for noise attenuation in seismic data Available to Purchase

Zhaolun Liu, Kai Lu
Journal: Geophysics
Published: 04 January 2021
Geophysics (2021) 86 (1): V23–V30.
DOI: https://doi.org/10.1190/geo2019-0746.1
...Zhaolun Liu; Kai Lu ABSTRACT We have developed convolutional sparse coding (CSC) to attenuate noise in seismic data. CSC gives a data-driven set of basis functions whose coefficients form a sparse distribution. The noise attenuation method by CSC can be divided into the training and denoising...
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View PDF for article title, <span class="search-highlight">Convolutional</span> <span class="search-highlight">sparse</span> <span class="search-highlight">coding</span> for noise attenuation in seismic data
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Journal Article

Dictionary learning with convolutional structure for seismic data denoising and interpolation Available to Purchase

Murad Almadani, Umair bin Waheed, Mudassir Masood, Yangkang Chen
Journal: Geophysics
Published: 27 July 2021
Geophysics (2021) 86 (5): V361–V374.
DOI: https://doi.org/10.1190/geo2019-0689.1
...-based learning algorithms work on overlapping patches of data and do not take the full data into account during reconstruction. In contrast, the data patches (convolutional sparse coding [CSC]) model treats signals globally and, therefore, has shown superior performance over patch-based methods...
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View PDF for article title, Dictionary learning with <span class="search-highlight">convolutional</span> structure for seismic data denoising and interpolation
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Journal Article

IncepTCN: A new deep temporal convolutional network combined with dictionary learning for strong cultural noise elimination of controlled-source electromagnetic data Available to Purchase

Guang Li, Shouli Wu, Hongzhu Cai, Zhushi He, Xiaoqiong Liu, Cong Zhou, Jingtian Tang
Journal: Geophysics
Published: 07 June 2023
Geophysics (2023) 88 (4): E107–E122.
DOI: https://doi.org/10.1190/geo2022-0317.1
... good results. To overcome the problem, a new strong-noise elimination method called inception-temporal convolutional network-shift-invariant sparse coding (IncepTCN-SISC) is developed based on deep learning and dictionary learning. First, a novel deep neural network model called IncepTCN is created...
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View PDF for article title, IncepTCN: A new deep temporal <span class="search-highlight">convolutional</span> network combined with dictionary learning for strong cultural noise elimination of controlled-source electromagnetic data
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Journal Article

Revisiting two notable methods for improving the deblending performance of marine towed-streamer acquisition Available to Purchase

Yangkang Chen, Min Zhou, Ray Abma
Journal: Geophysics
Published: 27 March 2024
Geophysics (2024) 89 (3): P33–P45.
DOI: https://doi.org/10.1190/geo2022-0621.1
... air-gun sources to obtain source-coded blended data. Deblending of the popcorn-source blended data might be done by combining popcorn reconstruction and source separation in sparse inversion. Simulated synthetic examples show that the incorporation of popcorn reconstruction into the original source...
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View PDF for article title, Revisiting two notable methods for improving the deblending performance of marine towed-streamer acquisition
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Journal Article

Deep convolutional neural network and sparse least-squares migration Open Access

Zhaolun Liu, Yuqing Chen, Gerard Schuster
Journal: Geophysics
Published: 13 June 2020
Geophysics (2020) 85 (4): WA241–WA253.
DOI: https://doi.org/10.1190/geo2019-0412.1
... version of SLSM that finds the Γ * and m * that minimize equation  5 , which is equivalent to the convolutional sparse coding problem. We denote the optimal solution m * as the NNLSM image. Here, we assume that the migration image m mig can be decomposed...
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View PDF for article title, Deep <span class="search-highlight">convolutional</span> neural network and <span class="search-highlight">sparse</span> least-squares migration
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Journal Article

Data and model dual-driven seismic deconvolution via error-constrained joint sparse representation Available to Purchase

Yaojun Wang, Guiqian Zhang, Ting Chen, Yu Liu, Bingxin Shen, Jiandong Liang, Guangmin Hu
Journal: Geophysics
Published: 21 June 2023
Geophysics (2023) 88 (4): V345–V359.
DOI: https://doi.org/10.1190/geo2022-0561.1
... D. Cohen I. Vassiliou A. A. , 2021 , Convolutional sparse coding fast approximation with application to seismic reflectivity estimation : IEEE Transactions on Geoscience and Remote Sensing , 60 , 1 – 19 , doi: http://dx.doi.org/10.1109/TGRS.2021.3105300 . IGRSD2 0196-2892...
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View PDF for article title, Data and model dual-driven seismic deconvolution via error-constrained joint <span class="search-highlight">sparse</span> representation
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Journal Article

Quadratic Unet for seismic random noise attenuation Available to Purchase

Xiaojing Wang, Yuhan Sui, Jianwei Ma
Journal: Geophysics
Published: 06 January 2025
Geophysics (2025) 90 (2): V53–V65.
DOI: https://doi.org/10.1190/geo2024-0019.1
... into a novel supervised deep-learning framework, demonstrating improved denoising performance. Liu and Lu (2021) propose a convolutional sparse coding method to denoise seismic signals with a set of learned translationally invariant dictionaries. Iqbal (2023) proposes a deep segmental denoising neural...
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View PDF for article title, Quadratic Unet for seismic random noise attenuation
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Journal Article

This issue of Geophysics Available to Purchase

Journal: Geophysics
Published: 17 September 2021
Geophysics (2021) 86 (5): 1SO–4SO.
DOI: https://doi.org/10.1190/geo2021-0819-tiogeo.1
... of both noise-free and noisy seismic data. Almadani et al. propose using a new convolutional sparse coding-based approach to denoise and interpolate seismic data. This approach leverages global data features during the reconstruction that results in a more simplified and accurate implementation...
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Journal Article

Adapting the residual dense network for seismic data denoising and upscaling Available to Purchase

Rongqian Wang, Ruixuan Zhang, Chenglong Bao, Lingyun Qiu, Dinghui Yang
Journal: Geophysics
Published: 16 June 2022
Geophysics (2022) 87 (4): V321–V340.
DOI: https://doi.org/10.1190/geo2021-0294.1
... : Geophysics , 48 , 854 – 886 , doi: http://dx.doi.org/10.1190/1.1441516 . GPYSA7 0016-8033 Liu Z. Lu K. , 2021 , Convolutional sparse coding for noise attenuation in seismic data : Geophysics , 86 , no.  1 , V23 – V30 , doi: http://dx.doi.org/10.1190/geo2019-0746.1 . GPYSA7 0016-8033...
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View PDF for article title, Adapting the residual dense network for seismic data denoising and upscaling
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Journal Article

Machine-learning-based data recovery and its contribution to seismic acquisition: Simultaneous application of deblending, trace reconstruction, and low-frequency extrapolation Available to Purchase

Shotaro Nakayama, Gerrit Blacquière
Journal: Geophysics
Published: 23 February 2021
Geophysics (2021) 86 (2): P13–P24.
DOI: https://doi.org/10.1190/geo2020-0303.1
.... Hinton G. E. , 2012 , Imagenet classification with deep convolutional neural networks : NIPS’12 Proceedings of the 25th International Conference on Neural Information Processing Systems , 1 , 1097 – 1105 . Kutscha H. Verschuur D. J. , 2012 , Data reconstruction via sparse double...
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View PDF for article title, Machine-learning-based data recovery and its contribution to seismic acquisition: Simultaneous application of deblending, trace reconstruction, and low-frequency extrapolation
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Journal Article

Stochastic representation and conditioning of process-based geological model by deep generative and recognition networks Available to Purchase

Siu Wun Cheung, Amit Kushwaha, Huafei Sun, Xiao-Hui Wu
Published: 05 June 2024
Petroleum Geoscience (2024) 30 (3): petgeo2022-032.
DOI: https://doi.org/10.1144/petgeo2022-032
... in Goodfellow et al. (2014) , DCGAN sets the classes G and D to be two subfamilies of sparsely connected deep convolutional neural networks in the two-player minimax problem. Taking the advantages of convolutional architectures, DCGAN is useful in extracting important features from...
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View PDF for article title, Stochastic representation and conditioning of process-based geological model by deep generative and recognition networks
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Journal Article

Unsupervised deep learning with higher-order total-variation regularization for multidimensional seismic data reconstruction Available to Purchase

Thomas André Larsen Greiner, Jan Erik Lie, Odd Kolbjørnsen, Andreas Kjelsrud Evensen, Espen Harris Nilsen, Hao Zhao, Vasily Demyanov, Leiv-J. Gelius
Journal: Geophysics
Published: 31 December 2021
Geophysics (2022) 87 (2): V59–V73.
DOI: https://doi.org/10.1190/geo2021-0099.1
... via convolutional sparse coding : The Journal of Machine Learning Research , 18 , 2887 – 2938 . Porsani M. J. , 1999 , Seismic trace interpolation using half-step prediction filters : Geophysics , 64 , 1461 – 1467 , doi: http://dx.doi.org/10.1190/1.1444650 . GPYSA7 0016-8033...
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View PDF for article title, Unsupervised deep learning with higher-order total-variation regularization for multidimensional seismic data reconstruction
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Book Chapter

Convolutional quelling in seismic tomography Available to Purchase

Author(s)
Keith A. Meyerholtz, Gary L. Pavlis, Sally A. Szpakowski
Book: Prestack Depth Migration and Velocity Model Building
Series: Society of Exploration Geophysicists Geophysics Reprint Series
Publisher: Society of Exploration Geophysicists
Published: 01 January 2008
EISBN: 9781560801917
... Abstract This paper introduces convolutional quelling as a technique to improve imaging of seismic tomography data. We show the result amounts to a special type of damped, weighted, least-squares solution. This insight allows us to implement the technique in a practical manner using a sparse...
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View PDF for content titled, <span class="search-highlight">Convolutional</span> quelling in seismic tomography
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Abstract This paper introduces convolutional quelling as a technique to improve imaging of seismic tomography data. We show the result amounts to a special type of damped, weighted, least-squares solution. This insight allows us to implement the technique in a practical manner using a sparse matrix, conjugate gradient equation solver. We applied the algorithm to synthetic data using an eight nearest-neighbor smoothing filter for the quelling The results were found to be superior to a simple least-squares solution because convolutional quelling suppresses side bands in the resolving function that lead to imaging artifacts.

Journal Article

TransInver: 3D data-driven seismic inversion based on self-attention Available to Purchase

Kewen Li, Yimin Dou, Yuan Xiao, Ruilin Jing, Jianbing Zhu, Chengjie Ma
Journal: Geophysics
Published: 06 November 2023
Geophysics (2024) 89 (1): WA127–WA141.
DOI: https://doi.org/10.1190/geo2023-0144.1
... from sparse 1D well-log labels. Fully convolutional networks rely on their parameter sharing mechanism and receptive fields to achieve this, but their perceptual range is limited, making it difficult to capture long-term correlations in seismic data. The transformer is a type of network...
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View PDF for article title, TransInver: 3D data-driven seismic inversion based on self-attention
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Journal Article

Controlled-source electromagnetic noise attenuation via a deep convolutional neural network and high-quality sounding curve screening mechanism Available to Purchase

Yecheng Liu, Diquan Li, Jin Li, Xian Zhang
Journal: Geophysics
Published: 06 January 2025
Geophysics (2025) WA125–WA140.
DOI: https://doi.org/10.1190/geo2024-0255.1
... convolutional network-shift-invariant sparse coding method. This method enables the identification and removal of strong and weak noise. The S/N of the CSEM data is improved by almost 8 dB following denoising with this method. In addition, deep learning has been extensively used in seismic data processing...
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View PDF for article title, Controlled-source electromagnetic noise attenuation via a deep <span class="search-highlight">convolutional</span> neural network and high-quality sounding curve screening mechanism
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Early Publication
Journal Article

High-resolution seismic acoustic impedance inversion with the sparsity-based statistical model Available to Purchase

Lingqian Wang, Hui Zhou, Wenling Liu, Bo Yu, Sheng Zhang
Journal: Geophysics
Published: 01 July 2021
Geophysics (2021) 86 (4): R509–R527.
DOI: https://doi.org/10.1190/geo2020-0345.1
... al., 2006 ). Given the dictionaries, the corresponding sparse coefficients and patterns can be obtained. The sparse coding for high-resolution details m h l is conducted with the orthogonal matching pursuit (OMP) ( Elad, 2010 ) for the high-resolution coefficient α h...
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View PDF for article title, High-resolution seismic acoustic impedance inversion with the sparsity-based statistical model
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Unsupervised 3D seismic data reconstruction using a weighted-attentive deep-learning framework Available to Purchase

Gui Chen, Yang Liu, Yuhang Sun
Journal: Geophysics
Published: 08 November 2024
Geophysics (2024) 89 (6): V635–V652.
DOI: https://doi.org/10.1190/geo2023-0553.1
... the dictionary update and sparse-coding processes during reconstruction, replacing the SVD operation for updating the dictionary with a straightforward summation across the patches. Furthermore, Almadani et al. (2021) use a dictionary learning method featuring a convolutional structure called the local block...
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View PDF for article title, Unsupervised 3D seismic data reconstruction using a weighted-attentive deep-learning framework
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Journal Article

Integrating self-supervised denoising in inversion-based seismic deblending Available to Purchase

Nick Luiken, Matteo Ravasi, Claire Birnie
Journal: Geophysics
Published: 11 October 2023
Geophysics (2024) 89 (1): WA39–WA51.
DOI: https://doi.org/10.1190/geo2023-0131.1
... together using an appropriate dithering code such that the input and output traces are related via a system of linear equations. In this work, as we deal with marine data, we choose a dithering code that is a predefined sequence of time delays. For a general discussion on blending codes for different...
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View PDF for article title, Integrating self-supervised denoising in inversion-based seismic deblending
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Using a dynamic representation of sedimentary structural features in prestack inversion: A case study in a tight sandstone in the Sichuan Basin Available to Purchase

Ting Chen, Yaojun Wang, Hanpeng Cai, Bangli Zou, Gang Yu, Yalin Li, Guangmin Hu
Journal: Geophysics
Published: 13 December 2023
Geophysics (2024) 89 (1): B1–B15.
DOI: https://doi.org/10.1190/geo2022-0746.1
...-1 . Mairal J. Bach F. Ponce J. Sapiro G. , 2010 , Online learning for matrix factorization and sparse coding : Journal of Machine Learning Research , 11 , 19 – 60 , doi: http://dx.doi.org/10.48550/arXiv.0908.0050 . Marfurt K. J. Sudhaker V. Gersztenkorn...
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View PDF for article title, Using a dynamic representation of sedimentary structural features in prestack inversion: A case study in a tight sandstone in the Sichuan Basin
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