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:83-91
ISSN:2712-9640
Container-title:Solnechno-Zemnaya Fizika
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

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