• Laser & Optoelectronics Progress
  • Vol. 57, Issue 6, 061501 (2020)
Shaohua Cui*, Suwen Li, and Xude Wang
Author Affiliations
  • College of Physics and Electronic Information, Huaibei Normal University, Huaibei, Anhui 235000, China
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    DOI: 10.3788/LOP57.061501 Cite this Article Set citation alerts
    Shaohua Cui, Suwen Li, Xude Wang. De-Noising Method for Seismic Data via Improved Convolution Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061501 Copy Citation Text show less
    References

    [1] Liu F Q, Zhang G Q. Application of wavelet transform and F-K algorithm in filtering[J]. Oil Geophysical Prospecting, 31, 782-791, 906(1996).

    [2] Kang Y, Yu C Y, Jia W et al. A study on noise-suppression method in f-x domain[J]. Oil Geophysical Prospecting, 38, 136-138(2003).

    [3] Lu W K, Ding W L, Zhang S W et al. A high-resolution processing technique for 3-D seismic data based on signal sub-space decomposition[J]. Chinese Journal of Geophysics, 48, 896-901(2005).

    [4] Cheng H, Yuan Y, Wang E D et al. Study of hierarchical adaptive threshold micro-seismic signal denoising based on wavelet transform[J]. Journal of Northeastern University (Natural Science), 39, 1332-1336(2018).

    [5] Zhao Y, Yue Y X, Huang J L et al. CEEMD and wavelet transform jointed de-noising method[J]. Progress in Geophysics, 30, 2870-2877(2015).

    [6] Zhang Y X. 47(1): 56-62[J]. Tian X M. Seismic denoising based on the modified particle swarm optimization-independent component analysis. Oil Geophysical Prospecting, 188, 194(2012).

    [7] Bu Y B, Liang N S, Shao D et al. Desert seismic noise attenuation via compound sparse denoising[J]. Journal of Jilin University (Information Science Edition), 36, 240-245(2018).

    [8] Zhang Y G, Yi B S, Wu C Y et al. Low-dose CT image denoising method based on convolutional neural network[J]. Acta Optica Sinica, 38, 0410003(2018).

    [9] Han W X, Zhou Y T, Chi Y. Deep learning convolutional neural networks for random noise attenuation in seismic data[J]. Geophysical Prospecting for Petroleum, 57, 862-869, 877(2018).

    [10] Zhang Y S, Yang G W, Wang Q Q et al. Weld feature extraction based on fully convolutional networks[J]. Chinese Journal of Lasers, 46, 0302002(2019).

    [11] Peng Y F, Song X N, Zi L L et al. Remote sensing image retrieval based on convolutional neural network and modified fuzzy C-means[J]. Laser & Optoelectronics Progress, 55, 091008(2018).

    [12] LeCun Y, Bottou L, Bengio Y et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 86, 2278-2324(1998).

    [13] Qiao J F, Wang G M, Li W J et al. An adaptive deep Q-learning strategy for handwritten digit recognition[J]. Neural Networks, 107, 61-71(2018).

    [14] Zhang H, Lu S F, Li W H et al. Application of ΔLogR technology and BP neural network in organic evaluation in the complex lithology tight stratum[J]. Progress in Geophysics, 32, 1308-1313(2017).

    [15] Li Y, Lin X Z, Jiang M Y. Facial expression recognition with cross-connect LeNet-5 network[J]. Acta Automatica Sinica, 44, 176-182(2018).

    [16] Li C P, Qin P L, Zhang J J. Research on image denoising based on deep convolutional neural network[J]. Computer Engineering, 43, 253-260(2017).

    [17] Shi Z T, Wang Z R, Wang R et al. Single image super-resolution based on convolutional neural network[J]. Laser & Optoelectronics Progress, 55, 121001(2018).

    [18] Cui S H, Shan W, Fang Z G. Research on denoising of SVD algorithm based on Hankel matrix[J]. Research and Exploration in Laboratory, 37, 32-34(2018).

    Shaohua Cui, Suwen Li, Xude Wang. De-Noising Method for Seismic Data via Improved Convolution Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061501
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