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NTIS 바로가기IEEE access : practical research, open solutions, v.9, 2021년, pp.11585 - 11597
Li, Wenda (Institute of Geology and Geophysics, Chinese Academy of Sciences, Key Laboratory of Petroleum Resources Research, Beijing, China) , Wang, Jian (Institute of Geology and Geophysics, Chinese Academy of Sciences, Key Laboratory of Petroleum Resources Research, Beijing, China)
Random noise attenuation has always been an indispensable step in the seismic exploration workflow. The quality of the results directly affects the results of subsequent inversion and migration imaging. This paper proposes a cycle-GAN denoising framework based on the data augmentation strategy. We i...
Zhang, Mi, Liu, Yang, Bai, Min, Chen, Yangkang. Seismic Noise Attenuation Using Unsupervised Sparse Feature Learning. IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society, vol.57, no.12, 9709-9723.
Das, Vishal, Pollack, Ahinoam, Wollner, Uri, Mukerji, Tapan. Convolutional neural network for seismic impedance inversion. Geophysics, vol.84, no.6, R869-R880.
Neural Netw Tricks Trade Practical recommendations for gradient-based training of deep architectures bengio 2015 10.1007/978-3-642-35289-8_26 7700 437
Liu, Yang, Liu, Ning, Liu, Cai. Adaptive prediction filtering in t-x-y domain for random noise attenuation using regularized nonstationary autoregression. Geophysics, vol.80, no.1, V13-V21.
arXiv 1611 04076 Least squares generative adversarial networks mao 2016
Bonar, David, Sacchi, Mauricio. Denoising seismic data using the nonlocal means algorithm. Geophysics, vol.77, no.1, A5-A8.
Geophysics Spatial prediction fifiltering in the t-x and f-x domains hornbostel 1991 10.1190/1.1443014 56 2019
Zhu, Lingchen, Liu, Entao, McClellan, James H. Joint seismic data denoising and interpolation with double-sparsity dictionary learning. Journal of Geophysics and Engineering, vol.14, no.4, 802-810.
Jia, Yongna, Ma, Jianwei. What can machine learning do for seismic data processing? An interpolation application. Geophysics, vol.82, no.3, V163-V177.
Jun Young Cheong, In Kyu Park. Deep CNN-Based Super-Resolution Using External and Internal Examples. IEEE signal processing letters, vol.24, no.8, 1252-1256.
Xiong, Wei, Ji, Xu, Ma, Yue, Wang, Yuxiang, AlBinHassan, Nasher M., Ali, Mustafa N., Luo, Yi. Seismic fault detection with convolutional neural network. Geophysics, vol.83, no.5, O97-O103.
Proc ECCV Perceptual losses for real-time style transfer and super-resolution johnson 2016 694
Oil Geophys Prospecting Micro-seismic data denoising based on sparse representations over learned dictionary in the wavelet domain jie 2016 51 254
Zhang, Kai, Zuo, Wangmeng, Chen, Yunjin, Meng, Deyu, Zhang, Lei. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol.26, no.7, 3142-3155.
Zhang, Kai, Zuo, Wangmeng, Zhang, Lei. FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol.27, no.9, 4608-4622.
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.. Gradient-based learning applied to document recognition. Proceedings of the IEEE, vol.86, no.11, 2278-2324.
Zhu, Lingchen, Liu, Entao, McClellan, James H.. Seismic data denoising through multiscale and sparsity-promoting dictionary learning. Geophysics, vol.80, no.6, WD45-WD57.
Rudin, Leonid I., Osher, Stanley, Fatemi, Emad. Nonlinear total variation based noise removal algorithms. Physica. D, Nonlinear phenomena, vol.60, no.1, 259-268.
Zhang, Mi, Liu, Yang, Chen, Yangkang. Unsupervised Seismic Random Noise Attenuation Based on Deep Convolutional Neural Network. IEEE access : practical research, open solutions, vol.7, 179810-179822.
Weisheng Dong, Guangming Shi, Xin Li. Nonlocal Image Restoration With Bilateral Variance Estimation: A Low-Rank Approach. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol.22, no.2, 700-711.
Ross, Christopher P., Cole, David M.. A comparison of popular neural network facies-classification schemes. The leading edge, vol.36, no.4, 340-349.
Hornbostel, Scott. Spatial prediction filtering in thet-xandf-xdomains. Geophysics, vol.56, no.12, 2019-2026.
Russell, Brian, Hampson, Dan, Chun, Joong. Noise elimination and the Radon transform, Part 1. Geophysics, the leading edge of exploration, vol.9, no.10, 18-23.
Hinton, Geoffrey E., Osindero, Simon, Teh, Yee-Whye. A Fast Learning Algorithm for Deep Belief Nets. Neural computation, vol.18, no.7, 1527-1554.
Hennenfent, G., Herrmann, F.J.. Seismic denoising with nonuniformly sampled curvelets. Computing in science & engineering, vol.8, no.3, 16-25.
arXiv 1612 07828 Learning from simulated and unsupervised images through adversarial training shrivastava 2016
Tang, Gang, Ma, Jian-Wei, Yang, Hui-Zhu. Seismic data denoising based on learning-type overcomplete dictionaries. Applied geophysics = 應用地球物理. 英文版, vol.9, no.1, 27-32.
Krizhevsky, Alex, Sutskever, Ilya, Hinton, Geoffrey E.. ImageNet classification with deep convolutional neural networks. Communications of the ACM, vol.60, no.6, 84-90.
Tassano, Matias, Delon, Julie, Veit, Thomas. An Analysis and Implementation of the FFDNet Image Denoising Method. Image processing on line, vol.9, 1-25.
Zhang, Rongfeng, Ulrych, Tadeusz J.. Physical Wavelet Frame Denoising. Geophysics, vol.68, no.1, 225-231.
Basic Earth Imaging Stanford Exploration Project claerbout 2009
J Mach Learn Res Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion vincent 2010 11 3371
Zhang, Hao, Yang, Xiuyan, Ma, Jianwei. Can learning from natural image denoising be used for seismic data interpolation?. Geophysics, vol.85, no.4, WA115-WA136.
Yu, Siwei, Ma, Jianwei, Osher, Stanley. Monte Carlo data-driven tight frame for seismic data recovery. Geophysics, vol.81, no.4, V327-V340.
Geophysic Deep-learning-based seismic data interpolation: A preliminary resultDeep learning for interpolation benfeng 2018
Yu, Siwei, Ma, Jianwei, Wang, Wenlong. Deep learning for denoising. Geophysics, vol.84, no.6, V333-V350.
Proc Int Conf Med Image Comput Comput -Assist Intervent U-net: Convolutional networks for biomedical image segmentation ronneberger 2015
Freire, Sergio L. M., Ulrych, Tad J.. Application of singular value decomposition to vertical seismic profiling. Geophysics, vol.53, no.6, 778-785.
Comput Sci Very deep convolutional networks for large-scale image recognition simonyan 2014
Fomel, Sergey, Liu, Yang. Seislet transform and seislet frame. Geophysics, vol.75, no.3, V25-V38.
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