A Comparison of Poststack Seismic Denoising: Deep Learning Vs. Structure-oriented Filtering

References

1. Fehmers, G., F. W.Höecker, 2003, Fast structural interpretation with structure-oriented filtering: Geophysics, 68, 1286–1293.
   [Google Scholar]
2. Hale, D., 2009, Structure-oriented smoothing and semblance: Colorado School of Mines, CWP Report 635.
   [Google Scholar]
3. Kuwahara, M., K.Hachimura, S.Eiho, and M.Kinoshita, 1976, Digital processing of biomedical images: Plenum Press, 187–203.
   [Google Scholar]
4. Luo, Y., S.Al-Dossary, and M.Marhoon, 2002, Edge-preserving smoothing and applications: The Leading Edge, 21, 136–158.
   [Google Scholar]
5. Jain, V., and S.Seung, 2009, Natural image denoising with convolutional networks, Proceedings of the Advances in Neural Information Processing Systems, 769–776.
   [Google Scholar]
6. Xie, J., L.Xu, and E.Chen, 2012, Image denoising and inpainting with deep neural networks, Proceedings of the Advances in Neural Information Processing Systems, 350–358.
   [Google Scholar]
7. Zhang, K., W.Zuo, Y.Chen, D.Meng, and L.Zhang, 2017, Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising, IEEE Transaction on Image Processing, 26, 7, 3142–3155.
   [Google Scholar]
8. Yu, S., J.Ma, and W.Wang, 2019, Deep learning for denoising, 84, V333–V350.
   [Google Scholar]
9. Wu, H., B.Zhang, T.Lin, F.Li, and N.Liu, 2019c, White noise attenuation of seismic trace by integrating variational mode decomposition with convolutional neural network, 84, V307–V317.
   [Google Scholar]
10. Wang, E., and J.Nealon, 2019, Applying machine learning to 3D seismic image denoising and enhancement: Interpretation, 7, 3, SE131–SE139.
    [Google Scholar]
11. WuH., B.Zhang and N.Liu, 2022, Self-adaptive denoising net: Self-supervised learning for seismic migration artifacts and random noise attenuation: IEEE Transactions on Geoscience and Remote Sensing, 60.
    [Google Scholar]