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

• 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

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.