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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ftmdpi:oai:mdpi.com:/1099-4300/20/9/691/ 2023-08-20T04:07:40+02:00 The Behavior of VLF/LF Variations Associated with Geomagnetic Activity, Earthquakes, and the Quiet Condition Using a Neural Network Approach Irina Popova Alexandr Rozhnoi Maria Solovieva Danila Chebrov Masashi Hayakawa 2018-09-11 application/pdf https://doi.org/10.3390/e20090691 EN eng Multidisciplinary Digital Publishing Institute Complexity https://dx.doi.org/10.3390/e20090691 https://creativecommons.org/licenses/by/4.0/ Entropy; Volume 20; Issue 9; Pages: 691 earthquake precursors magnetic storm neural network low frequency electromagnetic signals Text 2018 ftmdpi https://doi.org/10.3390/e20090691 2023-07-31T21:43:22Z 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. Text Kamchatka MDPI Open Access Publishing Petropavlovsk ENVELOPE(158.626,158.626,53.067,53.067) Petropavlovsk-Kamchatsky ENVELOPE(158.651,158.651,53.044,53.044) Entropy 20 9 691 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
earthquake precursors magnetic storm neural network low frequency electromagnetic signals |
spellingShingle |
earthquake precursors magnetic storm neural network low frequency electromagnetic signals 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 |
topic_facet |
earthquake precursors magnetic storm neural network low frequency electromagnetic signals |
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. |
format |
Text |
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 |
title |
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_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_sort |
behavior of vlf/lf variations associated with geomagnetic activity, earthquakes, and the quiet condition using a neural network approach |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2018 |
url |
https://doi.org/10.3390/e20090691 |
long_lat |
ENVELOPE(158.626,158.626,53.067,53.067) ENVELOPE(158.651,158.651,53.044,53.044) |
geographic |
Petropavlovsk Petropavlovsk-Kamchatsky |
geographic_facet |
Petropavlovsk Petropavlovsk-Kamchatsky |
genre |
Kamchatka |
genre_facet |
Kamchatka |
op_source |
Entropy; Volume 20; Issue 9; Pages: 691 |
op_relation |
Complexity https://dx.doi.org/10.3390/e20090691 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/e20090691 |
container_title |
Entropy |
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20 |
container_issue |
9 |
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691 |
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1774719474889392128 |