Muography. Группа авторов

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СКАЧАТЬ L., Baccani, G., Noli, P., Amato, L., Ambrosio, F., Bonechi, L., et al. (2019). 3D muography for the search of hidden cavities. Scientific Reports, 9, 2974. https://doi.org/10.1038/s41598‐019‐39682‐5

      8 Corradino, C., Ganci, G., Cappello, A., Bilotta, G., Hérault, A., & Del Negro, C. (2019). Mapping recent lava flows at Mount Etna using multispectral Sentinel‐2 images and machine learning techniques. Remote Sensing, 11, 1916. https://doi.org/10.3390/rs11161916

      9 D’Alessandro, R., Ambrosino, F., Baccani, G., Bonechi, L., Bongi, M., Caputo, A, et al. (2019). Volcanoes in Italy and the role of muon radiography. Philosophical Transactions of the Royal Society A, 377, 20180050. https://doi.org/10.1098/rsta.2018.0050

      10 Davis, K., & Oldenburg, D. W. (2012). Joint 3D of muon tomography and gravity data to recover density. ASEG Extended Abstracts, 1, 1–4. https://doi.org/10.1071/ASEG2012ab172

      11 Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist‐level classification of skin cancer with deep neural networks. Nature, 542, 115–118. https://doi.org/10.1038/nature21056

      12 Falsaperla, S., Graziani, S., Nunnari, G., & Spampinato, S. (1996). Automatic classification of volcanic earthquakes by using Multi‐Layered neural networks. Natural Hazards, 13, 205–228. https://doi.org/10.1007/BF00215816

      13 Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27, 861–874. https://doi.org/10.1016/j.patrec.2005.10.010

      14 Gaddes, M. E., Hooper, A., & Bagnardi, M. (2019). Using machine learning to automatically detect volcanic unrest in a time series of interferograms. Journal of Geophysical Research: Solid Earth, 124, 12304–12322. https://doi.org/10.1029/2019JB017519

      15 Geller, R. J. (1997). Earthquake prediction: a critical review. Geophysical Journal International, 131, 425–450. https://doi.org/10.1111/j.1365‐246X.1997.tb06588.x

      16 Géron, A. (2019). Hands‐on Machine Learning with Scikit‐Learn, Keras & TensorFlow. O’Reilly Media, Inc., Sebastopol, CA.

      17 Gluyas, J., Thompson, L., Allen, D., Benton, C., Chadwick, P., Clark, S., et al. (2019). Passive, continuous monitoring of carbon dioxide geostorage using muon tomography. Philosophical Transactions of the Royal Society A, 377, 20180059. https://doi.org/10.1098/rsta.2018.0059

      18 Goh, G. B., Hodas, N. O., & Vishnu, A. (2017). Deep learning for computational chemistry. Journal of Computational Chemistry, 38, 1291–1307. https://doi.org/10.1002/jcc.24764

      19 Guardincerri, E., Rowe, C., Schultz‐Fellenz, E., Roy, M., George, N., Morris, C., et al. (2017). 3D cosmic ray muon tomography from an underground tunnel. Pure and Applied Geophysics, 174, 2133–2141. https://doi.org/10.1007/s00024-017-1526-x

      20 Guest, D., Cranmer, K., & Whiteson, D. (2018). Deep learning and its application to LHC Physics. Annual Review of Nuclear and Particle Science, 68, 161–181. https://doi.org/10.1146/annurev‐nucl‐101917‐021019

      21 Hickey, J., Gottsmann, J., Nakamichi, H., & Iguchi, M. (2016). Thermomechanical controls on magma supply and volcanic deformation: application to Aira caldera, Japan. Scientific Reports, 6, 32691. https://doi.org/10.1038/srep32691

      22 Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A., Jaitly, N., et al. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 29, 82–97. https://doi.org/10.1109/MSP.2012.2205597

      23 Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313, 504–507. https://doi.org/10.1126/science.1127647

      24 Hochreiter, S., & Schmidhuber, J. (1997). Long Short‐Term Memory. Neural Computation, 9, 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

      25 Iguchi, M., Yakiwara, H., Tameguri, T., Hendrasto, M., & Hirabayashi, J. (2008). Mechanism of explosive eruption revealed by geophysical observations at the Sakurajima, Suwanosejima and Semeru volcanoes. Journal of Volcanology and Geothermal Research, 178, 1–9. https://doi.org/10.1016/j.jvolgeores.2007.10.010

      26 Ioffe, S. & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. ICML’15: Proceedings of the 32nd International Conference on International Conference on Machine Learning, 37, 448–456.

      27 Japan Meteorological Agency (2020). Sakurajima Euption Observation Tables. https://www.jma-net.go.jp/kagoshima/vol/kazan_top.html

      28 Kazahaya, R., Shinohara, H., Mori, T., Iguchi, M., & Yokoo, A. (2016). Pre‐eruptive inflation caused by gas accumulation: Insight from detailed gas flux variation at Sakurajima volcano, Japan. Geophysical Research Letters, 43, 11219–11225. https://doi.org/10.1002/2016GL070727

      29 Keras. (2020). Retrieved from https://keras.io/

      30 Kingma, D. P., & Ba, L. J. (2015). Adam: A Method for Stochastic Optimization. International Conference on Learning Representations. Retrieved from https://arxiv.org/abs/1412.6980v5

      31 Korup, O., & Stolle, A. (2014). Landslide prediction from machine learning. Geology Today, 30, 26–33. https://doi.org/10.1111/gto.12034

      32 Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet classification with deep convolutional neural networks. Communications of ACM, 60, 84–90. https://doi.org/10.1145/3065386

      33 Langer, H., Falsaperla, S., & Thompson, G. (2003). Application of artificial neural networks for the classification of the seismic transients at Soufrière Hills volcano, Montserrat. Geophysical Research Letters, 30, 2090. https://doi.org/10.1029/2003GL018082

      34 Lázaro Roche, I., Bitri, A., Bouteille, S., Decitre, J.‐B., Jourde, K., Gance, J., et al. (2019). Design, construction and in situ testing of a muon camera for Earth science and civil engineering applications. E3S Web Conference, 88, 01003. https://doi.org/10.1051/e3sconf/20198801003

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