Институт физики Земли им. О.Ю. Шмидта РАН
Москва, Россия
Институт физики Земли им. О.Ю. Шмидта Российской академии наук
Москва, Россия
Институт физики Земли им. О.Ю. Шмидта РАН
Москва, Россия
Москва, Россия
Москва, Россия
Москва, Россия
We solve the problem of recognizing geomagnetic storms from matrix time series of observations with the URAGAN muon hodoscope, using deep learning neural networks. A variant of the neural network software module is selected and its parameters are determined. Geomagnetic storms are recognized using binary classification procedures; a decision-making rule is formed. We estimate probabilities of correct and false recognitions. The recognition of geomagnetic storms is experimentally studied; for the assigned Dst threshold Yᴅ₀=–45 nT we obtain acceptable probabilities of correct and false recognitions, which amount to β=0.8212 and α=0.0047. We confirm the effectiveness and prospects of the proposed neural network approach.
geomagnetic storms, recognition, neural networks, probabilities of correct and false recognitions, matrix observations, muon hodoscope
1. Astapov I.I., Barbashina N.S., Borog V.V., Dmitrieva A.N, Shulzhenko I.A., Shutenko V.V., et al. Muon Diagnostics of Earth’s Magnetosphere and Atmosphere. Moscow, MEPhI, 2014. 132 p. (In Russian).
2. Ba J.L., Kingma D.P. Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. 2015, pp. 1-15.
3. Barkhatov N.A. Artificial Neural Networks in Solar-Terrestrial Physics. Nizhny Novgorod, Povolzh’e, 2010. 707 p. (In Russian).
4. Belov A.V., Gvishiani A.D., Getmanov V.G., Kovylyaeva A.A., Solovyev A.A., Chinkin V.E., et al. Identification of geomagnetic storms based on neural model estimates of Dst indices. J. Computer and Systems Sciences International. 2022, no. 1, pp. 56-66. (In Russian).
5. Berngardt O.N. The first comparative analysis of meteor echo and sporadic scattering identified by a self-learning neural network in EKB and MAGW ISTP SB RAS radar data Solar-Terr. Phys. 2022, vol. 8, iss. 4, pp. 63-72.
6. Chen X., Yu R., Ullah S., Wu D., Zhiqiang Li Zh., Li Q., et al. A novel loss function of deep learning in wind speed forecasting. Energy. 2022, vol. 238, p. 121808.
7. Chinkin V.E., Astapov I.I., Gvishiani A.D., Getmanov V.G., Dmitrieva A.N., Dobrovolsky M.N., et al. Method for the identification of heliospheric anomalies based on the functions of the characteristic deviations for the observation matrices of the muon hodoscope. Physics of Atomic Nuclei. 2019, vol. 82, no. 6, pp. 924-928.
8. Dolenko S.A., Orlov Yu.V., Persianinov I.G., Shugai Ju.S. Neural network algorithm for events forecasting and its application to space physics data. Lecture Notes in Computer Science. 2005, vol. 3697, pp. 527-532.
9. Getmanov V.G., Chinkin V.E., Gvishiani A.D., Dobrovolsky M.N., Sidorov R.G., Soloviev A.A., et al. Application of indicator matrices for the recognition of local anisotropies of muon fluxes in time series of matrix observations of the URAGAN hodoscope. Pattern Recognition and Image Analysis: Adv. in Mathematical Theory and Applications. 2022a, vol. 32, no. 3, pp. 717-728.
10. Getmanov V.G., Chinkin V.E., Gvishiani A.D., Sidorov R.V., Gvishiani A.D., Dobrovolskii M.N., Solov’ev A.A., et al. Geomagnetic storm prediction based on the neural network digital processing of joint observations of the URAGAN muon hodoscope and neutron monitor stations. Geomangetism and Aeronomy. 2022b, vol. 62, iss. 4, pp. 388-398.
11. Gruet M.A., Chandorkar M., Sicard A., Camporeale E. Multiplehour-ahead forecast of the Dst-index using a combination of long short-term memory neural network and Gaussian process. Space Weather. 2018, vol. 16, iss. 11, pp. 1882-1896. DOI:https://doi.org/10.1029/2018SW001898.
12. Efitorov A.O., Myagkova I.N., Shirokii V.P., Dolenko S.A. The prediction of the Dst-index based on machine learning methods. Cosmic Res. 2018, vol. 56, iss. 6, pp. 434-441.
13. Lundstredt H. Geomagnetic storm predictions from solar wind data with the use of dynamic neural networks. J. Geophys. Res. 1997, vol. 102, no. A7, pp.14,255-14,268.
14. Murzin V.S. Astrophysics of Cosmic Rays. Moscow, 2007. 488 p.
15. Myagkova I.N., Shirokii V.R., Vladimirov R.D., Barinov O.G., Dolenko S.A. Prediction of the Dst-index using adaptive мethods. Russian Meteorology and Hydrology. Allerton Press Inc. 2021, vol. 46, no. 3, pp. 157-162.
16. Pallochia G., Amota E., Consolini G., Marcucci M.F., Bertello I. Geomagnetics Dst-index forecast based on IMF data only. Ann. Geophys. 2006, vol. 24, pp. 989-999.
17. Stepanova M.V., Perez P. Autoprediction of Dst-index using neural network techniques and relationship to the auroral geomagnetics indices. Geofisica International. 2000, vol. 39, no. 1, pp. 143-146.
18. Suigiura M. Hourly values of equatorial Dst for the IGY. Ann. Int. Geophys. Year. Pergamon Press, Oxford. 1964, vol. 35, pp. 9-45.
19. Yashin I.I., Astapov I.I., Barbashina N.S., Bogod V.V., Chernov D.V., Dmitieva A.N., et al. Real-time data of muon hodoscope URAGAN. Adv. Space Res. 2015, vol. 56, iss. 12, pp. 2693-2705.
20. URL: https://wdc.kugi.kyoto-u.ac.jp (accessed December 23, 2023).
21. URL: http://www.nevod.mephi.ru/ (accessed March 13, 2024).
22. URL: https://it.mephi.ru/hpc/perfomance (accessed December 23, 2023).
23. URL: https://arxiv.org/abs/1905.11946/ (accessed December 23, 2023).
24. URL: https://arxiv.org/abs/1409.1556v6 (accessed December 23, 2023).
25. URL: https://arxiv.org/abs/1608.06993v5 (accessed December 23, 2023).
26. URL: https://arxiv.org/abs/1512.00567v3 (accessed March 13, 2024).
27. URL: https://arxiv.org/pdf/2107.07699.pdf (accessed December 23, 2023).
28. URL: https://arxiv.org/abs/1512.03385 (accessed December 23, 2023).
29. URL: https://arxiv.org/pdf/1412.6980.pdf (accessed December 23, 2023).
30. URL: https://pytorch.org/docs/stable/generated/torch.optim. Adam.html (accessed December 23, 2023).
31. URL: https://learndatasci/glossary/binary-classification (accessed October 23, 2023).
32. URL: https://www.learndatasci.com/glossary/binary-classification/ (accessed March 13, 2024).
33. URL: https://helenkapatsa.ru/blogpost/otsienka-f1 (accessed March 13, 2024).
34. URL: https://www.izmiran.ru/ionosphere/weather/storm/ (accessed March 13, 2024).
