1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K.,
Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O.,
Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.:
TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, https://www.tensorflow.org, last access: 9 August 2020, 2015. a
2. Ahmed, E., Saint, A., Shabayek, A. E. R., Cherenkova, K., Das, R., Gusev, G.,
Aouada, D., and Ottersten, B.: A survey on Deep Learning Advances on
Different 3D Data Representations, arXiv [preprint], https://arxiv.org/abs/1808.01462, 2018. a
3. Aki, K. and Richards, P. G.: Quantitative seismology, W. H. Freeman and Co., New York, New York, 1980. a, b
4. Araya-Polo, M., Jennings, J., Adler, A., and Dahlke, T.: Deep-learning
tomography, The Leading Edge, 37, 58–66, 2018. a
5. Bergen, K. J., Johnson, P. A., De Hoop, M. V., and Beroza, G. C.: Machine
learning for data-driven discovery in solid Earth geoscience, Science, 363, eaau0323, https://doi.org/10.1126/science.aau0323, 2019. a