Including Physics in Deep Learning – An Example from 4D Seismic Pressure Saturation Inversion

References

1. Chollet, F.
   [2015] Keras. https://github.com/fchollet/keras.
   [Google Scholar]
2. Dramsch, J.S., Corte, G., Amini, H., Lüthje, M. and MacBeth, C.
   [2019] Deep Learning Application for 4D Pressure Saturation Inversion Compared to Bayesian Inversion on North Sea Data.
   [Google Scholar]
3. Dramsch, J.S. and Lüthje, M.
   [2018] Deep-learning seismic facies on state-of-the-art CNN architectures. In: SEG Technical Program Expanded Abstracts2018, Society of Exploration Geophysicists.
   [Google Scholar]
4. Grün, F., Rupprecht, C., Navab, N. and Tombari, F.
   [2016] A taxonomy and library for visualizing learned features in convolutional neural networks. arXiv preprint arXiv:1606.07757.
   [Google Scholar]
5. He, K., Zhang, X., Ren, S. and Sun, J.
   [2015] Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision. 1026–1034.
   [Google Scholar]
6. Ioffe, S. and Szegedy, C.
   [2015] Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Arxiv Preprint Arxiv:1502.03167.
   [Google Scholar]
7. Karpatne, A., Watkins, W., Read, J. and Kumar, V.
   [2017] Physics-guided neural networks (pgnn): An application in lake temperature modeling. arXiv preprint arXiv:1710.11431.
   [Google Scholar]
8. Kingma, D.P. and Welling, M.
   [2013] Auto-Encoding Variational Bayes. Arxiv Preprint Arxiv:1312.6114.
   [Google Scholar]
9. de Oliveira, L., Paganini, M. and Nachman, B.
   [2017] Learning particle physics by example: location-aware generative adversarial networks for physics synthesis. Computing and Software for Big Science.
   [Google Scholar]
10. Roeth, G. and Tarantola, A.
    [1994] Neural networks and inversion of seismic data. Journal of Geophysical Research: Solid Earth, 99(B4), 6753–6768.
    [Google Scholar]
11. Schütt, K.T., Arbabzadah, F., Chmiela, S., Müller, K.R. and Tkatchenko, A.
    [2017] Quantum-chemical insights from deep tensor neural networks. Nature communications, 8, 13890.
    [Google Scholar]
12. Ulyanov, D., Vedaldi, A. and Lempitsky, V.
    [2018] Deep image prior. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 9446–9454.
    [Google Scholar]