Using Convolutional Neural Networks for Denoising and Deblending of Marine Seismic Data

References

1. Baardman, R
   [2018] Classification And Suppression Of Blending Noise Using CNN. First EAGE/PESGB Workshop Machine Learning 2018
   [Google Scholar]
2. Barbier, M.
   [1971] Seismic Exploration. US patent 3956730A
   [Google Scholar]
3. Goodfellow, I., Bengio, Y. and Courville, A.
   [2016] Deep Learning. MIT: MIT Press. http://www.deeplearningbook.org
   [Google Scholar]
4. Jin, Y., Wu, X., Chen, J., Han, Z., and Hu, W.
   [2018] Seismic data denoising by deep-residual networks. SEG Technical Program Expanded Abstracts 2018: pp. 4593–4597.
   [Google Scholar]
5. Li, H., Yang, W. and Yong, X.
   [2018] Deep learning for ground-roll noise attenuation. SEG Technical Program Expanded Abstracts 2018: pp. 1981–1985.
   [Google Scholar]
6. Liu, D., Wang, W., Chen, W., Wang, X., Zhou, Y. and Shi, Z.
   [2018] Random-noise suppression in seismic data: What can deep learning do?. SEG Technical Program Expanded Abstracts 2018: pp. 2016–2020.
   [Google Scholar]
7. Mikhailiuk, A. and Faul, A.
   [2018] Deep Learning Applied to Seismic Data Interpolation. 80th EAGE Conference and Exhibitions 2018.
   [Google Scholar]
8. Si, X. and Yuan, Y.
   [2018] Random noise attenuation based on residual learning of deep convolutional neural network. SEG Technical Program Expanded Abstracts 2018: pp. 1986–1990.
   [Google Scholar]
9. Xie, P., Boelle, J. and Puntous, H.
   [2018] Generative-adversarial network-based fast-noise removal on land-seismic data. SEG Technical Program Expanded Abstracts 2018: pp. 2171–2175.
   [Google Scholar]
10. Ma, J.
    [2018] Deep learning for attenuating random and coherence noise simultaneously. 80th Annual International Conference and Exhibition, EAGE, Expanded abstracts
    [Google Scholar]
11. Zhang, Y., Lin, H., and Li, Y.
    [2018] Noise attenuation for seismic image using a deep-residual learning. SEG Technical Program Expanded Abstracts 2018: pp. 2176–2180.
    [Google Scholar]