Ground Roll Attenuation Using Generative Adversarial Network

References

1. Brown, M., and R. G.Clapp
   , 2001, Seismic pattern recognition via predictive signal/noise separation: STANFORD EXPLORATION PROJECT, 177.
   [Google Scholar]
2. Creswell, A., T.White, V.Dumoulin, K.Arulkumaran, B.Sengupta, and A. A.Bharath
   , 2018, Generative adversarial networks: An overview: IEEE Signal Processing Magazine, 35, 53–65.
   [Google Scholar]
3. Fomel, S.
   , 2000, Applications of plane-wave destructor filters: Sep–105.
   [Google Scholar]
4. Fomel, S.
   , 2008, Adaptive multiple subtraction using regularized nonstationary regression: Geophysics, 74, V25–V33.
   [Google Scholar]
5. Fomel, S., P.Sava, I.Vlad, Y.Liu, and V.Bashkardin
   , 2013, Madagascar: Open-source software project for multidimensional data analysis and reproducible computational experiments: Journal of Open Research Software, 1.
6. Guitton, A., M.Brown, J.Rickett, and R.Clapp
   , 2001, A pattern-based technique for ground-roll and multiple attenuation: Sep–108.
   [Google Scholar]
7. Jiao, S., Y.Chen, M.Bai, W.Yang, E.Wang, and S.Gan
   , 2015, Ground roll attenuation using non-stationary matching filtering: Journal of Geophysics and Engineering, 12, 922.
   [Google Scholar]
8. Li, H., W.Yang, and X.Yong
   , 2018, Deep learning for ground-roll noise attenuation, in SEG Technical Program Expanded Abstracts 2018: Society of Exploration Geophysicists, 1981–1985.
   [Google Scholar]
9. Paszke, A., S.Gross, S.Chintala, G.Chanan, E.Yang, Z.DeVito, Z.Lin, A.Desmaison, L.Antiga, and A.Lerer
   , 2017, Automatic differentiation in pytorch: Presented at the NIPS-W.
   [Google Scholar]
10. Zhu, J.-Y., T.Park, P.Isola, and A. A.Efros
    , 2017, Unpaired image-to-image translation using cycle-consistent adversarial networks: arXiv preprint.
    [Google Scholar]