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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MDPI AG
2018-09-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/20/9/691 |
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| author | Irina Popova Alexandr Rozhnoi Maria Solovieva Danila Chebrov Masashi Hayakawa |
| author_facet | Irina Popova Alexandr Rozhnoi Maria Solovieva Danila Chebrov Masashi Hayakawa |
| author_sort | Irina Popova |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T03:20:33Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-14T03:20:33Z |
| publishDate | 2018-09-01 |
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| series | Entropy |
| spelling | doaj.art-194bb0c8d34f462fb6626b6d32ba70522022-12-22T02:15:19ZengMDPI AGEntropy1099-43002018-09-0120969110.3390/e20090691e20090691The Behavior of VLF/LF Variations Associated with Geomagnetic Activity, Earthquakes, and the Quiet Condition Using a Neural Network ApproachIrina Popova0Alexandr Rozhnoi1Maria Solovieva2Danila Chebrov3Masashi Hayakawa4Institute of Physics of the Earth RAS, Bolshaya Gruzinskay, 10-1, Moscow 123242, RussiaInstitute of Physics of the Earth RAS, Bolshaya Gruzinskay, 10-1, Moscow 123242, RussiaInstitute of Physics of the Earth RAS, Bolshaya Gruzinskay, 10-1, Moscow 123242, RussiaKamchatska Branch of Geophysical Survey of RAS, Boulevard Piypa, Petropavlovsk-Kamchatsky 683006, RussiaAdvanced Wireless Communications Research Center, University of Electro-Communications, Chofu, Tokyo 182-8585, JapanThe 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.http://www.mdpi.com/1099-4300/20/9/691earthquake precursorsmagnetic stormneural networklow frequency electromagnetic signals |
| spellingShingle | Irina Popova Alexandr Rozhnoi Maria Solovieva Danila Chebrov Masashi Hayakawa The Behavior of VLF/LF Variations Associated with Geomagnetic Activity, Earthquakes, and the Quiet Condition Using a Neural Network Approach Entropy earthquake precursors magnetic storm neural network low frequency electromagnetic signals |
| title | The Behavior of VLF/LF Variations Associated with Geomagnetic Activity, Earthquakes, and the Quiet Condition Using a Neural Network Approach |
| title_full | The Behavior of VLF/LF Variations Associated with Geomagnetic Activity, Earthquakes, and the Quiet Condition Using a Neural Network Approach |
| title_fullStr | The Behavior of VLF/LF Variations Associated with Geomagnetic Activity, Earthquakes, and the Quiet Condition Using a Neural Network Approach |
| title_full_unstemmed | The Behavior of VLF/LF Variations Associated with Geomagnetic Activity, Earthquakes, and the Quiet Condition Using a Neural Network Approach |
| title_short | The Behavior of VLF/LF Variations Associated with Geomagnetic Activity, Earthquakes, and the Quiet Condition Using a Neural Network Approach |
| title_sort | behavior of vlf lf variations associated with geomagnetic activity earthquakes and the quiet condition using a neural network approach |
| topic | earthquake precursors magnetic storm neural network low frequency electromagnetic signals |
| url | http://www.mdpi.com/1099-4300/20/9/691 |
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