Neural network approach to the prediction of seismic events based on low-frequency signal monitoring of the Kuril-Kamchatka and Japanese regions
Very-low-frequency/ low-frequency (VLF/LF) sub-ionospheric radiowave monitoring has been widely used in recent years to analyze earthquake preparatory processes. The connection between earthquakes with M ≥5.5 and nighttime disturbances of signal amplitude and phase has been established. Thus, it is...
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ftopenaccessrep:oai:zenodo.org:130247 2023-10-29T02:37:34+01:00 Neural network approach to the prediction of seismic events based on low-frequency signal monitoring of the Kuril-Kamchatka and Japanese regions Masashi Hayakawa Boris Levin Pier Francesco Biagi Alexander Rozhnoi Konrad Schwingenschuh Maria Solovieva Yasuhide Hobara I. V. Popova 2013-08-30 https://www.openaccessrepository.it/record/130247 https://doi.org/10.4401/ag-6224 und unknown url:https://www.openaccessrepository.it/communities/itmirror https://www.openaccessrepository.it/record/130247 doi:10.4401/ag-6224 info:eu-repo/semantics/openAccess NEANIAS Atmospheric Research Community Geophysics info:eu-repo/semantics/article publication-article 2013 ftopenaccessrep https://doi.org/10.4401/ag-6224 2023-10-03T22:17:22Z Very-low-frequency/ low-frequency (VLF/LF) sub-ionospheric radiowave monitoring has been widely used in recent years to analyze earthquake preparatory processes. The connection between earthquakes with M ≥5.5 and nighttime disturbances of signal amplitude and phase has been established. Thus, it is possible to use nighttime anomalies of VLF/LF signals as earthquake precursors. Here, we propose a method for estimation of the VLF/LF signal sensitivity to seismic processes using a neural network approach. We apply the error back-propagation technique based on a three-level perceptron to predict a seismic event. The back-propagation technique involves two main stages to solve the problem; namely, network training, and recognition (the prediction itself). To train a neural network, we first create a so-called 'training set'. The 'teacher' specifies the correspondence between the chosen input and the output data. In the present case, a representative database includes both the LF data received over three years of monitoring at the station in Petropavlovsk-Kamchatsky (2005-2007), and the seismicity parameters of the Kuril-Kamchatka and Japanese regions. At the first stage, the neural network established the relationship between the characteristic features of the LF signal (the mean and dispersion of a phase and an amplitude at nighttime for a few days before a seismic event) and the corresponding level of correlation with a seismic event, or the absence of a seismic event. For the second stage, the trained neural network was applied to predict seismic events from the LF data using twelve time intervals in 2004, 2005, 2006 and 2007. The results of the prediction are discussed. Article in Journal/Newspaper Kamchatka Istituto Nazionale di Fisica Nucleare (INFN): Open Access Repository Annals of Geophysics 56 3 |
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Istituto Nazionale di Fisica Nucleare (INFN): Open Access Repository |
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NEANIAS Atmospheric Research Community Geophysics |
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NEANIAS Atmospheric Research Community Geophysics Masashi Hayakawa Boris Levin Pier Francesco Biagi Alexander Rozhnoi Konrad Schwingenschuh Maria Solovieva Yasuhide Hobara I. V. Popova Neural network approach to the prediction of seismic events based on low-frequency signal monitoring of the Kuril-Kamchatka and Japanese regions |
topic_facet |
NEANIAS Atmospheric Research Community Geophysics |
description |
Very-low-frequency/ low-frequency (VLF/LF) sub-ionospheric radiowave monitoring has been widely used in recent years to analyze earthquake preparatory processes. The connection between earthquakes with M ≥5.5 and nighttime disturbances of signal amplitude and phase has been established. Thus, it is possible to use nighttime anomalies of VLF/LF signals as earthquake precursors. Here, we propose a method for estimation of the VLF/LF signal sensitivity to seismic processes using a neural network approach. We apply the error back-propagation technique based on a three-level perceptron to predict a seismic event. The back-propagation technique involves two main stages to solve the problem; namely, network training, and recognition (the prediction itself). To train a neural network, we first create a so-called 'training set'. The 'teacher' specifies the correspondence between the chosen input and the output data. In the present case, a representative database includes both the LF data received over three years of monitoring at the station in Petropavlovsk-Kamchatsky (2005-2007), and the seismicity parameters of the Kuril-Kamchatka and Japanese regions. At the first stage, the neural network established the relationship between the characteristic features of the LF signal (the mean and dispersion of a phase and an amplitude at nighttime for a few days before a seismic event) and the corresponding level of correlation with a seismic event, or the absence of a seismic event. For the second stage, the trained neural network was applied to predict seismic events from the LF data using twelve time intervals in 2004, 2005, 2006 and 2007. The results of the prediction are discussed. |
format |
Article in Journal/Newspaper |
author |
Masashi Hayakawa Boris Levin Pier Francesco Biagi Alexander Rozhnoi Konrad Schwingenschuh Maria Solovieva Yasuhide Hobara I. V. Popova |
author_facet |
Masashi Hayakawa Boris Levin Pier Francesco Biagi Alexander Rozhnoi Konrad Schwingenschuh Maria Solovieva Yasuhide Hobara I. V. Popova |
author_sort |
Masashi Hayakawa |
title |
Neural network approach to the prediction of seismic events based on low-frequency signal monitoring of the Kuril-Kamchatka and Japanese regions |
title_short |
Neural network approach to the prediction of seismic events based on low-frequency signal monitoring of the Kuril-Kamchatka and Japanese regions |
title_full |
Neural network approach to the prediction of seismic events based on low-frequency signal monitoring of the Kuril-Kamchatka and Japanese regions |
title_fullStr |
Neural network approach to the prediction of seismic events based on low-frequency signal monitoring of the Kuril-Kamchatka and Japanese regions |
title_full_unstemmed |
Neural network approach to the prediction of seismic events based on low-frequency signal monitoring of the Kuril-Kamchatka and Japanese regions |
title_sort |
neural network approach to the prediction of seismic events based on low-frequency signal monitoring of the kuril-kamchatka and japanese regions |
publishDate |
2013 |
url |
https://www.openaccessrepository.it/record/130247 https://doi.org/10.4401/ag-6224 |
genre |
Kamchatka |
genre_facet |
Kamchatka |
op_relation |
url:https://www.openaccessrepository.it/communities/itmirror https://www.openaccessrepository.it/record/130247 doi:10.4401/ag-6224 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.4401/ag-6224 |
container_title |
Annals of Geophysics |
container_volume |
56 |
container_issue |
3 |
_version_ |
1781062473544105984 |