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., Zheng, X.: TensorFlow: Large-scale machine learning on heterogeneous systems. https://www.tensorflow.org/. Software available from tensorflow.org (2015)
2. Bai, L., Lu, H., Liu, Y.: High-efficiency observations: compressive sensing and recovery of seismic waveform data. Pure Appl. Geophys. 177(1), 469–485 (2020)
3. Ballesio, M., Beck, J., Pandey, A., Parisi, L., von Schwerin, E., Tempone, R.: Multilevel monte carlo acceleration of seismic wave propagation under uncertainty. GEM-Int. J. Geomathe. 10(1), 22 (2019)
4. Barbosa, C.H., Kunstmann, L.N., Silva, R.M., Alves, C.D., Silva, B.S., Mattoso, M., Rochinha, F.A., Coutinho, A.L., et al.: A workflow for seismic imaging with quantified uncertainty. Comput. Geosci. 145, 104615 (2020)
5. Belhadj, J., Romary, T., Gesret, A., Noble, M., Figliuzzi, B.: New parametrizations for bayesian seismic tomography. Inverse Problems 34, 33 (2018)