Optimal Setting of Earthquake-Related Ionospheric TEC (Total Electron Content) Anomalies Detection Methods: Long-Term Validation over the Italian Region
Over the last decade, thanks to the availability of historical satellite observations that have begun to be significantly large and thanks to the exponential growth of artificial intelligence techniques, many advances have been made in the detection of geophysical parameters such as seismic-related...
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MDPI AG
2023-05-01
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| Series: | Geosciences |
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| Online Access: | https://www.mdpi.com/2076-3263/13/5/150 |
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| author | Roberto Colonna Carolina Filizzola Nicola Genzano Mariano Lisi Valerio Tramutoli |
| author_facet | Roberto Colonna Carolina Filizzola Nicola Genzano Mariano Lisi Valerio Tramutoli |
| author_sort | Roberto Colonna |
| collection | DOAJ |
| description | Over the last decade, thanks to the availability of historical satellite observations that have begun to be significantly large and thanks to the exponential growth of artificial intelligence techniques, many advances have been made in the detection of geophysical parameters such as seismic-related anomalies. In this study, the variations of the ionospheric Total Electron Content (TEC), one of the main parameters historically proposed as a seismic-connected indicator, are analyzed. To make a statistically robust analysis of the complex phenomena involved, we propose a completely innovative machine-learning approach developed in the R programming language. Through this approach, an optimal setting of the multitude of methodological inputs currently proposed for the detection of ionospheric anomalies is performed. The setting is optimized by analyzing, for the first time, multi-year—mostly twenty-year—time series of TEC satellite data measured by global navigation satellite systems (GNSS) over the Italian region, matched with the corresponding multi-year time series of seismic events. Seismic events including all the countries of the Mediterranean area, up to Turkey, are involved in the analysis. Tens of thousands of possible combinations of input methodological parameters are simulated and classified according to pre-established criteria. Several inputs examined return clear results. These results combined with each other highlight the presence of anomalous seismic-related sequences that have an extremely low probability of having been detected randomly (up to 2 out of 1 million). The anomalies identified represent the most anomalous behaviors of the TEC recorded during the entire period under investigation (e.g., 20 years). Some of the main conclusions are that, at mid-latitudes, ① the detection of seismic-TEC anomalies can be more efficient looking for punctual rather than persistent phenomena; ② the optimal thresholds for the identification of co-seismic anomalies can assume different values depending on type of anomaly (positive or negative) and type of observation; ③ single GNSS receiver data can be useful for capturing local earthquake-ionospheric effects and Global Ionospheric Maps (GIM) data can be functional in detecting large-scale earthquake-ionospheric effects; ④ earthquakes deeper than 50 km are less likely to affect the ionosphere. |
| first_indexed | 2024-03-11T03:41:41Z |
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| issn | 2076-3263 |
| language | English |
| last_indexed | 2024-03-11T03:41:41Z |
| publishDate | 2023-05-01 |
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| record_format | Article |
| series | Geosciences |
| spelling | doaj.art-89250fdcc2794e8f9fec79332b0fe3b92023-11-18T01:31:09ZengMDPI AGGeosciences2076-32632023-05-0113515010.3390/geosciences13050150Optimal Setting of Earthquake-Related Ionospheric TEC (Total Electron Content) Anomalies Detection Methods: Long-Term Validation over the Italian RegionRoberto Colonna0Carolina Filizzola1Nicola Genzano2Mariano Lisi3Valerio Tramutoli4School of Engineering, University of Basilicata, 85100 Potenza, ItalySatellite Application Centre (SAC), Space Technologies and Applications Centre (STAC), 85100 Potenza, ItalySchool of Engineering, University of Basilicata, 85100 Potenza, ItalyInstitute of Methodologies for Environmental Analysis, National Research Council, 85050 Potenza, ItalySchool of Engineering, University of Basilicata, 85100 Potenza, ItalyOver the last decade, thanks to the availability of historical satellite observations that have begun to be significantly large and thanks to the exponential growth of artificial intelligence techniques, many advances have been made in the detection of geophysical parameters such as seismic-related anomalies. In this study, the variations of the ionospheric Total Electron Content (TEC), one of the main parameters historically proposed as a seismic-connected indicator, are analyzed. To make a statistically robust analysis of the complex phenomena involved, we propose a completely innovative machine-learning approach developed in the R programming language. Through this approach, an optimal setting of the multitude of methodological inputs currently proposed for the detection of ionospheric anomalies is performed. The setting is optimized by analyzing, for the first time, multi-year—mostly twenty-year—time series of TEC satellite data measured by global navigation satellite systems (GNSS) over the Italian region, matched with the corresponding multi-year time series of seismic events. Seismic events including all the countries of the Mediterranean area, up to Turkey, are involved in the analysis. Tens of thousands of possible combinations of input methodological parameters are simulated and classified according to pre-established criteria. Several inputs examined return clear results. These results combined with each other highlight the presence of anomalous seismic-related sequences that have an extremely low probability of having been detected randomly (up to 2 out of 1 million). The anomalies identified represent the most anomalous behaviors of the TEC recorded during the entire period under investigation (e.g., 20 years). Some of the main conclusions are that, at mid-latitudes, ① the detection of seismic-TEC anomalies can be more efficient looking for punctual rather than persistent phenomena; ② the optimal thresholds for the identification of co-seismic anomalies can assume different values depending on type of anomaly (positive or negative) and type of observation; ③ single GNSS receiver data can be useful for capturing local earthquake-ionospheric effects and Global Ionospheric Maps (GIM) data can be functional in detecting large-scale earthquake-ionospheric effects; ④ earthquakes deeper than 50 km are less likely to affect the ionosphere.https://www.mdpi.com/2076-3263/13/5/150anomalies detection methodsoptimal settingR programming languagestatistical data analysismachine learning techniquesTotal Electron Content (TEC) |
| spellingShingle | Roberto Colonna Carolina Filizzola Nicola Genzano Mariano Lisi Valerio Tramutoli Optimal Setting of Earthquake-Related Ionospheric TEC (Total Electron Content) Anomalies Detection Methods: Long-Term Validation over the Italian Region Geosciences anomalies detection methods optimal setting R programming language statistical data analysis machine learning techniques Total Electron Content (TEC) |
| title | Optimal Setting of Earthquake-Related Ionospheric TEC (Total Electron Content) Anomalies Detection Methods: Long-Term Validation over the Italian Region |
| title_full | Optimal Setting of Earthquake-Related Ionospheric TEC (Total Electron Content) Anomalies Detection Methods: Long-Term Validation over the Italian Region |
| title_fullStr | Optimal Setting of Earthquake-Related Ionospheric TEC (Total Electron Content) Anomalies Detection Methods: Long-Term Validation over the Italian Region |
| title_full_unstemmed | Optimal Setting of Earthquake-Related Ionospheric TEC (Total Electron Content) Anomalies Detection Methods: Long-Term Validation over the Italian Region |
| title_short | Optimal Setting of Earthquake-Related Ionospheric TEC (Total Electron Content) Anomalies Detection Methods: Long-Term Validation over the Italian Region |
| title_sort | optimal setting of earthquake related ionospheric tec total electron content anomalies detection methods long term validation over the italian region |
| topic | anomalies detection methods optimal setting R programming language statistical data analysis machine learning techniques Total Electron Content (TEC) |
| url | https://www.mdpi.com/2076-3263/13/5/150 |
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