The Comparison of Convolution Neural Networkы for Localized Capturing Detection of Faults on Seismic Images

References

1. Hale, D.
   [2013] Methods to compute fault images, extract fault surfaces, and estimate fault throws from 3D seismic images. Geophysics, 78(2), O33–O43.
   [Google Scholar]
2. Hale, D. and Emanuel, J.
   [2003] Seismic interpretation using global image segmentation. In: SEG Technical Program Expanded Abstracts 2003, Society of Exploration Geophysicists, 2410–2413.
   [Google Scholar]
3. Herron, D.A.
   [2011] First steps in seismic interpretation. Society of Exploration Geophysicists.
   [Google Scholar]
4. Meldahl, P., Heggland, R., Bril, B. and de Groot, P.
   [2001] Identifying faults and gas chimneys using multiattributes and neural networks. The Leading Edge, 20(5), 474–482.
   [Google Scholar]
5. Ronneberger, O., Fischer, P. and Brox, T.
   [2015] U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer, 234–241.
   [Google Scholar]
6. Xiong, W., Ji, X., Ma, Y., Wang, Y., BenHassan, N.M., Ali, M.N. and Luo, Y.
   [2018] Seismic fault detection with convolutional neural network. Geophysics, 83(5), 1–28.
   [Google Scholar]