Читать книгу Muography - Группа авторов - Страница 70
REFERENCES
Оглавление1 A Living Review of Machine Learning for Particle Physics (2020). Retrieved from https://iml‐wg.github.io/HEPML‐LivingReview/
2 Anantrasirichai, N., Biggs, J., Albino, F., Hill, P., & Bull, D. R. (2018). Application of machine learning to classification of volcanic deformation in routinely generated InSAR data. Journal of Geophysical Research: Solid Earth, 123, 6592–6606. https://doi.org/10.1029/2018JB015911
3 Barnoud, A., Cayol, V., Leliévre, P. G., Portal, A., Labazuy, P., Boivin, P., & Gailler, L. (2021). Robust Bayesian joint inversion of gravimetric and muographic data for the density imaging of the Puy de Dôme Volcano (France). Frontiers in Earth Science, 8, 575842. https://doi.org/10.3389/feart.2020.575842
4 Brancato, A., Buscema, P. M., Massini, G., & Gresta, S. (2016). Pattern recognition for flank eruption forecasting: An application at Mount Etna Volcano (Sicily, Italy). Open Journal of Geology, 6, 583–597. https://doi.org/10.4236/ojg.2016.67046
5 Brancato, A., Buscema, P. M., Massini, G., Gresta, S., Salerno, G., & Della Torre, F. (2019). K‐CM application for supervised pattern recognition at Mt. Etna: an innovative tool to forecast flank eruptive activity. Bulletin of Volcanology, 81, 40. https://doi.org/10.1007/s00445‐019‐1299‐4
6 Ching, T., Himmelstein, D. S., Beaulieu‐Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., et al. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of the Royal Society Interface, 15, 20170387. https://doi.org/10.1098/rsif.2017.0387
7 Cimmino, 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
35 LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444. https://doi.org/10.1038/nature14539
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, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back‐propagating errors. Nature, 323, 533–536. https://doi.org/10.1038/323533a0
61 Sahiner, B., Pezeshk, A., Hadjiiski, L. M., Wang, X., Drukker, K., Cha, K. H., et al. (2019). Deep learning in medical imaging and radiation therapy. Medical Physics, 46, 1–36. https://doi.org/10.1002/mp.13264
62 Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3, 210–229. https://doi.org/10.1147/rd.33.0210
63 Scarpetta, S., Giudicepietro, F., Ezin, E. C., Petrosino, S., Del Pezzo, E., Martini, M., & Marinaro, M. (2005). Automatic classification of seismic signals at Mt. Vesuvius Volcano, Italy, using neural networks. Bulletin of the Seismological Society of America, 95, 185–196. https://doi.org/10.1785/0120030075
64 Schouten, D., & Lendru, P. (2018). Muon tomography applied to a dense uranium deposit at the McArthur River Mine. Journal of Geophysical Research: Solid Earth, 123, 8637–8652. https://doi.org/10.1029/2018JB015626
65 Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical Bayesian optimization of machine learning algorithms. Advances in Neural Information Processing Systems, 25, 2951–2959.
66 Sparks, R. (2003). Forecasting volcanic eruptions. Earth and Planetary Science Letters, 210, 1–15. https://doi.org/10.1016/S0012‐821X(03)00124‐9
67 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929–1958.
68 Tanaka, H. K. M. (2015). Muographic mapping of the subsurface density structures in Miura, Boso and Izu peninsulas, Japan. Scientific Reports, 5, 8305. https://doi.org/10.1038/srep08305
69 Tanaka, H. K. M., Kusagaya, T., & Shinohara, H. (2014). Radiographic visualization of magma dynamics in an erupting volcano. Nature Communications, 5, 3381. https://doi.org/10.1038/ncomms4381
70 Tanaka, H. K. M., Nakano, T., Takahashi, S., Yoshida, J., Takeo, M., Oikawa, J., et al. (2007). High resolution imaging in the inhomogeneous crust with cosmic‐ray muon radiography: The density structure below the volcanic crater floor of Mt. Asama, Japan. Earth and Planetary Science Letters, 263, 104–113. https://doi.org/10.1016/j.epsl.2007.09.001
71 Tanaka, H. K. M., Taira, H., Uchida, T., Tanaka, M., Takeo, M., Ohminato, T., et al. (2010). Three‐dimensional computational axial tomography scan of a volcano with cosmic ray muon radiography. Journal of Geophysical Research, 115, B12332. https://doi.org/10.1029/2010JB007677
72 Tensorflow (2020). Retrieved from https://tensorflow.org/
73 Titov, V. V., Gonzalez, F. I., Bernard, E. N., Eble, M. C., Mofjeld, H. O., Newman, J. C., & Venturato, A. J. (2005). Real‐time tsunami forecasting: Challenges and solutions. Natural Hazards, 35, 35–41. https://doi.org/10.1007/s11069‐004‐2403‐3
74 Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. Springer‐Verlag, New York.
75 VanderPlas, J., A., Connolly, J., Ivezić, Z., & Gray, A. (2012). Introduction to astroML: Machine learning for astrophysics. Conference on Intelligent Data Understanding, 2012, 47–54.
76 Varga, D., Gál, Z., Hamar, G., Molnár, J. S., Oláh, É., & Pázmándi, P. (2015). Cosmic muon detector using proportional chambers. European Journal of Physics, 36, 065006. https://doi.org/10.1088/0143‐0807/36/6/065006
77 Varga, D., Hamar, G., & Oláh, L. (2021). Development of multi‐wire proportional chamber‐based trackers for muography. 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)
78 Varga, D., Nyitrai, G., Hamar, G., Galgóczi, G., Oláh, L., Tanaka, H. K. M., & Ohminato, T. (2020). Detector developments for high performance muography applications. Nuclear Instruments and Methods in Physics Research Section A, 958, 162236. https://doi.org/10.1016/j.nima.2019.05.077
79 Varga, D., Nyitrai, G., Hamar, G., & Oláh, L. (2016). High efficiency gaseous tracking detector for cosmic muon radiography. Advances in High Energy Physics, 2016, 1962317. https://doi.org/10.1155/2016/1962317
80 Witsil, A. J. C. & Johnson, J. B. (2020). Volcano video data characterized and classified using computer vision and machine learning algorithms. Geoscience Frontiers, 11, 1789–1803. https://doi.org/10.1016/j.gsf.2020.01.016
81 Yamaoka, K., Geshi, N., Hashimoto, T., Ingrebitsen, S. E., & Oikawa, T. (2016). Special issue “The phreatic eruption of Mt. Ontake volcano in 2014”. Earth, Planets and Space, 68, 175. https://doi.org/10.1186/s40623‐016‐0548‐4
82 Youden, W. J. (1950). Index for rating diagnostics tests. Cancer, 3, 32–35. https://doi.org/10.1002/1097‐0142(1950)3:1<32::aid‐cncr2820030106>3.0.co;2‐3