The Behavior of VLF/LF Variations Associated with Geomagnetic Activity, Earthquakes, and the Quiet Condition Using a Neural Network Approach

The neural network approach is proposed for studying very-low- and low-frequency (VLF and LF) subionospheric radio wave variations in the time vicinities of magnetic storms and earthquakes, with the purpose of recognizing anomalies of different types. We also examined the days with quiet geomagnetic...

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Bibliographic Details
Published in:Entropy
Main Authors: Irina Popova, Alexandr Rozhnoi, Maria Solovieva, Danila Chebrov, Masashi Hayakawa
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2018
Subjects:
earthquake precursors
magnetic storm
neural network
low frequency electromagnetic signals
Science
Q
Astrophysics
QB460-466
Physics
QC1-999
Petropavlovsk
Petropavlovsk-Kamchatsky
Kamchatka
Online Access:https://doi.org/10.3390/e20090691
https://doaj.org/article/194bb0c8d34f462fb6626b6d32ba7052
Description
Summary:The neural network approach is proposed for studying very-low- and low-frequency (VLF and LF) subionospheric radio wave variations in the time vicinities of magnetic storms and earthquakes, with the purpose of recognizing anomalies of different types. We also examined the days with quiet geomagnetic conditions in the absence of seismic activity, in order to distinguish between the disturbed signals and the quiet ones. To this end, we trained the neural network (NN) on the examples of the representative database. The database included both the VLF/LF data that was measured during four-year monitoring at the station in Petropavlovsk-Kamchatsky, and the parameters of seismicity in the Kuril-Kamchatka and Japan regions. It was shown that the neural network can distinguish between the disturbed and undisturbed signals. Furthermore, the prognostic behavior of the VLF/LF variations indicative of magnetic and seismic activity has a different appearance in the time vicinity of the earthquakes and magnetic storms.

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