Ionospheric parameter modelling and anomaly discovery by combining the wavelet transform with autoregressive models
The paper is devoted to new mathematical tools for ionospheric parameter analysis and anomaly discovery during ionospheric perturbations. The complex structure of processes under study, their a-priori uncertainty and therefore the complex structure of registered data require a set of techniques and...
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Istituto Nazionale di Geofisica e Vulcanologia, INGV
2015
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Online Access: | https://www.annalsofgeophysics.eu/index.php/annals/article/view/6729 https://doi.org/10.4401/ag-6729 |
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ftjaog:oai:ojs.annalsofgeophysics.eu:article/6729 2023-05-15T16:58:56+02:00 Ionospheric parameter modelling and anomaly discovery by combining the wavelet transform with autoregressive models Mandrikova, Oksana V. Fetisova (Glushkova), Nadezda V. Al-Kasasbeh, Riad Taha Klionskiy, Dmitry M. Geppener, Vladimir V. Ilyash, Maksim Y. 2015-11-11 application/pdf https://www.annalsofgeophysics.eu/index.php/annals/article/view/6729 https://doi.org/10.4401/ag-6729 eng eng Istituto Nazionale di Geofisica e Vulcanologia, INGV https://www.annalsofgeophysics.eu/index.php/annals/article/view/6729/6545 https://www.annalsofgeophysics.eu/index.php/annals/article/view/6729 doi:10.4401/ag-6729 Annals of Geophysics; V. 58 N. 5 (2015); A0550 Annals of Geophysics; Vol. 58 No. 5 (2015); A0550 2037-416X 1593-5213 Ionospheric parameters Perturbation Earthquake Anomaly Prediction Wavelet transform Autoregressive integrated moving average model Critical frequency data Total electron content data info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2015 ftjaog https://doi.org/10.4401/ag-6729 2022-03-27T06:38:26Z The paper is devoted to new mathematical tools for ionospheric parameter analysis and anomaly discovery during ionospheric perturbations. The complex structure of processes under study, their a-priori uncertainty and therefore the complex structure of registered data require a set of techniques and technologies to perform mathematical modelling, data analysis, and to make final interpretations. We suggest a technique of ionospheric parameter modelling and analysis based on combining the wavelet transform with autoregressive integrated moving average models (ARIMA models). This technique makes it possible to study ionospheric parameter changes in the time domain, make predictions about variations, and discover anomalies caused by high solar activity and lithospheric processes prior to and during strong earthquakes. The technique was tested on critical frequency foF2 and total electron content (TEC) datasets from Kamchatka (a region in the Russian Far East) and Magadan (a town in the Russian Far East). The mathematical models introduced in the paper facilitated ionospheric dynamic mode analysis and proved to be efficient for making predictions with time advance equal to 5 hours. Ionospheric anomalies were found using model error estimates, those anomalies arising during increased solar activity and strong earthquakes in Kamchatka. Article in Journal/Newspaper Kamchatka Annals of Geophysics (INGV, Istituto Nazionale di Geofisica e Vulcanologia) Magadan ENVELOPE(150.803,150.803,59.564,59.564) Annals of Geophysics 58 5 |
institution |
Open Polar |
collection |
Annals of Geophysics (INGV, Istituto Nazionale di Geofisica e Vulcanologia) |
op_collection_id |
ftjaog |
language |
English |
topic |
Ionospheric parameters Perturbation Earthquake Anomaly Prediction Wavelet transform Autoregressive integrated moving average model Critical frequency data Total electron content data |
spellingShingle |
Ionospheric parameters Perturbation Earthquake Anomaly Prediction Wavelet transform Autoregressive integrated moving average model Critical frequency data Total electron content data Mandrikova, Oksana V. Fetisova (Glushkova), Nadezda V. Al-Kasasbeh, Riad Taha Klionskiy, Dmitry M. Geppener, Vladimir V. Ilyash, Maksim Y. Ionospheric parameter modelling and anomaly discovery by combining the wavelet transform with autoregressive models |
topic_facet |
Ionospheric parameters Perturbation Earthquake Anomaly Prediction Wavelet transform Autoregressive integrated moving average model Critical frequency data Total electron content data |
description |
The paper is devoted to new mathematical tools for ionospheric parameter analysis and anomaly discovery during ionospheric perturbations. The complex structure of processes under study, their a-priori uncertainty and therefore the complex structure of registered data require a set of techniques and technologies to perform mathematical modelling, data analysis, and to make final interpretations. We suggest a technique of ionospheric parameter modelling and analysis based on combining the wavelet transform with autoregressive integrated moving average models (ARIMA models). This technique makes it possible to study ionospheric parameter changes in the time domain, make predictions about variations, and discover anomalies caused by high solar activity and lithospheric processes prior to and during strong earthquakes. The technique was tested on critical frequency foF2 and total electron content (TEC) datasets from Kamchatka (a region in the Russian Far East) and Magadan (a town in the Russian Far East). The mathematical models introduced in the paper facilitated ionospheric dynamic mode analysis and proved to be efficient for making predictions with time advance equal to 5 hours. Ionospheric anomalies were found using model error estimates, those anomalies arising during increased solar activity and strong earthquakes in Kamchatka. |
format |
Article in Journal/Newspaper |
author |
Mandrikova, Oksana V. Fetisova (Glushkova), Nadezda V. Al-Kasasbeh, Riad Taha Klionskiy, Dmitry M. Geppener, Vladimir V. Ilyash, Maksim Y. |
author_facet |
Mandrikova, Oksana V. Fetisova (Glushkova), Nadezda V. Al-Kasasbeh, Riad Taha Klionskiy, Dmitry M. Geppener, Vladimir V. Ilyash, Maksim Y. |
author_sort |
Mandrikova, Oksana V. |
title |
Ionospheric parameter modelling and anomaly discovery by combining the wavelet transform with autoregressive models |
title_short |
Ionospheric parameter modelling and anomaly discovery by combining the wavelet transform with autoregressive models |
title_full |
Ionospheric parameter modelling and anomaly discovery by combining the wavelet transform with autoregressive models |
title_fullStr |
Ionospheric parameter modelling and anomaly discovery by combining the wavelet transform with autoregressive models |
title_full_unstemmed |
Ionospheric parameter modelling and anomaly discovery by combining the wavelet transform with autoregressive models |
title_sort |
ionospheric parameter modelling and anomaly discovery by combining the wavelet transform with autoregressive models |
publisher |
Istituto Nazionale di Geofisica e Vulcanologia, INGV |
publishDate |
2015 |
url |
https://www.annalsofgeophysics.eu/index.php/annals/article/view/6729 https://doi.org/10.4401/ag-6729 |
long_lat |
ENVELOPE(150.803,150.803,59.564,59.564) |
geographic |
Magadan |
geographic_facet |
Magadan |
genre |
Kamchatka |
genre_facet |
Kamchatka |
op_source |
Annals of Geophysics; V. 58 N. 5 (2015); A0550 Annals of Geophysics; Vol. 58 No. 5 (2015); A0550 2037-416X 1593-5213 |
op_relation |
https://www.annalsofgeophysics.eu/index.php/annals/article/view/6729/6545 https://www.annalsofgeophysics.eu/index.php/annals/article/view/6729 doi:10.4401/ag-6729 |
op_doi |
https://doi.org/10.4401/ag-6729 |
container_title |
Annals of Geophysics |
container_volume |
58 |
container_issue |
5 |
_version_ |
1766051076421713920 |