DeepSeismic: a Deep Learning Library for Seismic Interpretation

References

1. Alaudah, Y., Michalowicz, P., Alfarraj, M. and AlRegib, G.
   [2019] A machine learning benchmark for facies classification. Interpretation, 7(3), 1–51.
   [Google Scholar]
2. Baroni, L., Silva, R.M., Ferreira, R.S., Civitarese, D., Szwarcman, D. and Brazil, E.V.
   [2019] Penobscot Dataset: Fostering Machine Learning Development for Seismic Interpretation. arXiv preprint arXiv:1903.12060.
   [Google Scholar]
3. Hu, J., Shen, L. and Sun, G.
   [2018] Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 7132–7141.
   [Google Scholar]
4. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L. et al.
   [2019] PyTorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems. 8024–8035.
   [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. Sun, K., Zhao, Y., Jiang, B., Cheng, T., Xiao, B., Liu, D., Mu, Y., Wang, X., Liu, W. and Wang, J.
   [2019] High-Resolution Representations for Labeling Pixels and Regions. arXiv preprint arXiv:1904.04514.
   [Google Scholar]
7. Waldeland, A. and Solberg, A.
   [2017] Salt classification using deep learning. In: 79th EAGE Conference and Exhibition 2017.
   [Google Scholar]
8. Waldeland, A.U., Jensen, A.C., Gelius, L.J. and Solberg, A.H.S.
   [2018] Convolutional neural networks for automated seismic interpretation. The Leading Edge, 37(7), 529–537.
   [Google Scholar]