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

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.

Keywords

• geomagnetic storms
• recognition
• neural networks
• probabilities of correct and false recognitions
• matrix observations
• muon hodoscope