Recognition of geomagnetic storms from time series of matrix observations with the muon hodoscope URAGAN using neural networks of deep learning-Reference-Cited by-同舟云学术

Recognition of geomagnetic storms from time series of matrix observations with the muon hodoscope URAGAN using neural networks of deep learning

Published:2024-03-26 Issue:1 Volume:10 Page:76-83
ISSN:2500-0535
Container-title:Solar-Terrestrial Physics
language:en
Short-container-title:

Author:

Getmanov Viktor¹²,Gvishiani Alexei³⁴^ORCID,Soloviev Anatoly¹²^ORCID,Zajtsev Konstantin⁵,Dunaev Maksim⁵,Ehlakov Eduard⁵

Affiliation:

1. Geophysical Center of RAS

2. Schmidt Institute of Physics of the Earth, RAS

3. Geophysical Center of the Russian Academy of Sciences

4. Schmidt Institute of the Physics of the Earth Russian Academy of Sciencies

5. National Reasearch Nuclear University MEPHI

Abstract

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.

Publisher

Infra-M Academic Publishing House

Reference34 articles.

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)., 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., 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)., 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)., 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., 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.