Schmidt Institute of Physics of the Earth, RAS
Moskva, Russian Federation
Schmidt Institute of the Physics of the Earth Russian Academy of Sciencies
Moscow, Russian Federation
Schmidt Institute of Physics of the Earth, RAS
Moscow, Russian Federation
Moscow, Russian Federation
Moscow, Russian Federation
Moscow, Russian Federation
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
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