Muography. Группа авторов

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      36 Le Gonidec, Y., Rosas‐Carbajal, M., de Bremond d’Ars, J., Carlus, B., Ianigro, J.‐C., Kergosien, B., et al. (2019). Abrupt changes of hydrothermal activity in a lava dome detected by combined seismic and muon monitoring. Scientific Reports, 9, 3079. https://doi.org/10.1038/s41598‐019‐39606‐3

      37 Lesparre, N., Cabrera, J., & Marteu, J. (2017). 3‐D density imaging with muon flux measurements from underground galleries. Geophysical Journal International, 208, 1579–1591. https://doi.org/10.1093/gji/ggw482

      38 Lesparre, N., Gibert, D., Marteau, J., Komorowski, J.‐C., Nicollin, F., & Coutant, O. (2012). Density muon radiography of La Soufrière of Guadeloupe volcano: comparison with geological, electrical resistivity and gravity data. Geophysical Journal International, 190, 1008–1019. https://doi.org/10.1111/j.1365‐246X.2012.05546.x

      39 Litjens, G., Kooi, T., Bejnordi, B. A., Setio, A. A. A., Ciompi, F., Ghafoorian, M., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005

      40 Lo Presti, D., Riggi, F., Ferlito, C., Bonanno, D. L., Bonanno, G., Gallo, G., et al. (2020). Muographic monitoring of the volcano‐tectonic evolution of Mount Etna. Scientific Reports, 10, 11351. https://doi.org/10.1038/s41598‐020‐68435‐y

      41 McCulloch, W., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133. https://doi.org/10.1007/BF02478259

      42 Nagahara, S., & Miyamoto, S. (2018). Feasibility of three‐dimensional density tomography using dozens of muon radiographies and filtered back projection for volcanos. Geoscientific Instrumentation, Methods and Data Systems, 7, 307–316. https://doi.org/10.5194/gi‐7‐307‐2018

      43 Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted Boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML‐10), 807–814.

      44 Newhall, C. G., & Hoblitt, R. P. (2002). Constructing event trees for volcanic crises. Bulletin of Volcanology, 64, 3–20. https://doi.org/10.1007/s004450100173

      45 Nishiyama, R., Tanaka, Y., Okubo, S., Oshima, H., Tanaka, H. K. M., & Maekawa, T. (2014). Integrated processing of muon radiography and gravity anomaly data toward the realization of high‐resolution 3‐D density structural analysis of volcanoes: Case study of Showa‐Shinzan lava dome, Usu, Japan. Journal of Geophysical Research: Solid Earth, 119, 699–710. https://doi.org/10.1002/2013JB010234

      46 Nomura, Y., Nemoto, M., Hayashi, N., Hanaoka, S., Murata, M., Yoshikawa, T., et al. (2020). Pilot study of eruption forecasting with muography using convolutional neural network. Scientific Reports, 10, 5272. https://doi.org/10.1038/s41598‐020‐62342‐y

      47 Okubo, S., & Tanaka, H. K. M. (2012). Imaging the density profile of a volcano interior with cosmic‐ray muon radiography combined with classical gravimetry. Measurement Science and Technology, 23, 042001. https://doi.org/10.1088/0957‐0233/23/4/042001

      48 Oláh, L., Barnaföldi, G. G., Hamar, G., Melegh, H. G., Surányi, G., & Varga, D. (2012). CCC‐based muon telescope for examination of natural caves. Geoscientific Instrumentation, Methods and Data Systems, 1, 229–234. https://doi.org/10.5194/gi‐1‐229‐2012

      49 Oláh, L., Hamar, G., Miyamoto, S., Tanaka, H. K. M., & Varga, D. (2018a). The first prototype of an MWPC‐based borehole‐detector and its application for muography of an underground pillar. Geophysical Exploration, 71, 161–168. https://doi.org/10.3124/segj.71.161

      50 Oláh, L. & Tanaka, H. K. M. (2021). Muography of magma intrusion beneath the active craters of Sakurajima Volcano. In: L. Oláh, H. K. M. Tanaka, D. Varga (Eds.), Muography: Exploring Earth’s Subsurface With Elementary Particles, Geophysical Monograph Series 270, Washington, DC: American Geophysical Union. (this volume)

      51 Oláh, L., Tanaka, H. K. M., Hamar, G., & Varga, D. (2019a). Muographic observation of density variations in the vicinity of Minami‐dake crater of Sakurajima volcano. Journal of Disaster Research, 14, 701–712. https://doi.org/10.20965/jdr.2019.p0701

      52 Oláh, L., Tanaka, H. K. M., Hamar, G., & Varga, D. (2019b). Plug formation imaged beneath the active craters of Sakurajima Volcano with muography. Geophysical Research Letters, 46, 10417–10424. https://doi.org/10.1029/2019GL084784

      53 Oláh, L., Tanaka, H. K. M., Ohminato, T., & Varga, D. (2018b). High‐definition and low‐noise muography of the Sakurajima volcano with gaseous tracking detectors. Scientific Reports, 8, 3207. https://doi.org/10.1038/s41598‐018‐21423‐9

      54 Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit‐learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.

      55 Radovic, A., Williams, M., Rousseau, D., Kagan, M., Bonacorsi, D., Himmer, A., et al. (2018). Machine learning at the energy and intensity frontiers of particle physics. Nature, 560, 41–48. https://doi.org/10.1038/s41586‐018‐0361‐2

      56 Reath, K., Ramsey, M., Dehn, J., & Webley, P. (2016). Predicting eruptions from precursory activity using remote sensing data hybridization. Journal of Volcanology and Geothermal Research, 321, 18–30. https://doi.org/10.1016/j.jvolgeores.2016.04.027

      57 Ren, C. X., Peltier, A., Ferrazzini, V., Rouet‐Leduc, B., Johnson, P. A., & Brenguier, F. (2020). Machine learning reveals the seismic signature of eruptive behavior at Piton de la Fournaise Volcano. Geophysical Research Letters, 47, e2019GL085523. https://doi.org/10.1029/2019GL085523

      58 Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65, 386–408. https://doi.org/10.1037/h0042519

      59 Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1, 206–215. https://doi.org/10.1038/s42256‐019‐0048‐x

      60 Rumelhart, СКАЧАТЬ