Monitoring of Ionospheric Anomalies Using GNSS Observations to Detect Earthquake Precursors
The study of the Earth’s ionosphere is a topic that has increased in relevance over the past few decades. The ability to predict the ionosphere’s behavior, as well as to mitigate the effects of its rapid changes, is a matter of primary importance in satellite communications, positioning, and navigat...
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/338 |
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| author | Nicola Perfetti Yuri Taddia Alberto Pellegrinelli |
| author_facet | Nicola Perfetti Yuri Taddia Alberto Pellegrinelli |
| author_sort | Nicola Perfetti |
| collection | DOAJ |
| description | The study of the Earth’s ionosphere is a topic that has increased in relevance over the past few decades. The ability to predict the ionosphere’s behavior, as well as to mitigate the effects of its rapid changes, is a matter of primary importance in satellite communications, positioning, and navigation applications at present. Ionosphere perturbations can be produced by geomagnetic storms correlated with the solar activity or by earthquakes, volcanic activities, and so on. The monitoring of space weather is achieved through analyzing the Vertical Total Electron Content (VTEC) and its anomalies by means of time series, maps, and other derived parameters. In this study, we outline a strategy to estimate the VTEC in real-time, its rate of change, and the corresponding Signal-to-Noise Ratio (SNR) based on dual-frequency GNSS Doppler observations. We describe how to compute these parameters from GNSS data for a regional network using Adjusted Spherical Harmonic Analysis (ASHA) applied to a local model. The proposed method was tested to study ionospheric anomalies for two seismic events: the 2015 Nepal and 2023 Turkey earthquakes. In both cases, anomalies were detected in the maps of the differential VTEC (DTEC), differential VTEC rate, and SNR of the VTEC produced close to the earthquake zone. The robustness of the results is strongly related to the availability of a dense Ionosphere Pierce Point (IPP) cloud on the ionospheric layer and surrounding the studied area. At present, the distribution of Continuously Operating Reference Stations (CORSs) around the world is insufficiently dense and homogeneous in certain regions (e.g., the oceans). Robustness can be improved by increasing the number of CORSs and developing new models involving measurements over ocean surfaces. |
| format | Article |
| id | doaj-art-47d5bd52c7e849018121e40986a7b3c3 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-47d5bd52c7e849018121e40986a7b3c32025-01-24T13:48:11ZengMDPI AGRemote Sensing2072-42922025-01-0117233810.3390/rs17020338Monitoring of Ionospheric Anomalies Using GNSS Observations to Detect Earthquake PrecursorsNicola Perfetti0Yuri Taddia1Alberto Pellegrinelli2Engineering Department, University of Ferrara, 44122 Ferrara, ItalyEngineering Department, University of Ferrara, 44122 Ferrara, ItalyEngineering Department, University of Ferrara, 44122 Ferrara, ItalyThe study of the Earth’s ionosphere is a topic that has increased in relevance over the past few decades. The ability to predict the ionosphere’s behavior, as well as to mitigate the effects of its rapid changes, is a matter of primary importance in satellite communications, positioning, and navigation applications at present. Ionosphere perturbations can be produced by geomagnetic storms correlated with the solar activity or by earthquakes, volcanic activities, and so on. The monitoring of space weather is achieved through analyzing the Vertical Total Electron Content (VTEC) and its anomalies by means of time series, maps, and other derived parameters. In this study, we outline a strategy to estimate the VTEC in real-time, its rate of change, and the corresponding Signal-to-Noise Ratio (SNR) based on dual-frequency GNSS Doppler observations. We describe how to compute these parameters from GNSS data for a regional network using Adjusted Spherical Harmonic Analysis (ASHA) applied to a local model. The proposed method was tested to study ionospheric anomalies for two seismic events: the 2015 Nepal and 2023 Turkey earthquakes. In both cases, anomalies were detected in the maps of the differential VTEC (DTEC), differential VTEC rate, and SNR of the VTEC produced close to the earthquake zone. The robustness of the results is strongly related to the availability of a dense Ionosphere Pierce Point (IPP) cloud on the ionospheric layer and surrounding the studied area. At present, the distribution of Continuously Operating Reference Stations (CORSs) around the world is insufficiently dense and homogeneous in certain regions (e.g., the oceans). Robustness can be improved by increasing the number of CORSs and developing new models involving measurements over ocean surfaces.https://www.mdpi.com/2072-4292/17/2/338GNSSionosphereionospheric anomaliesearthquakeTECVTEC |
| spellingShingle | Nicola Perfetti Yuri Taddia Alberto Pellegrinelli Monitoring of Ionospheric Anomalies Using GNSS Observations to Detect Earthquake Precursors Remote Sensing GNSS ionosphere ionospheric anomalies earthquake TEC VTEC |
| title | Monitoring of Ionospheric Anomalies Using GNSS Observations to Detect Earthquake Precursors |
| title_full | Monitoring of Ionospheric Anomalies Using GNSS Observations to Detect Earthquake Precursors |
| title_fullStr | Monitoring of Ionospheric Anomalies Using GNSS Observations to Detect Earthquake Precursors |
| title_full_unstemmed | Monitoring of Ionospheric Anomalies Using GNSS Observations to Detect Earthquake Precursors |
| title_short | Monitoring of Ionospheric Anomalies Using GNSS Observations to Detect Earthquake Precursors |
| title_sort | monitoring of ionospheric anomalies using gnss observations to detect earthquake precursors |
| topic | GNSS ionosphere ionospheric anomalies earthquake TEC VTEC |
| url | https://www.mdpi.com/2072-4292/17/2/338 |
| work_keys_str_mv | AT nicolaperfetti monitoringofionosphericanomaliesusinggnssobservationstodetectearthquakeprecursors AT yuritaddia monitoringofionosphericanomaliesusinggnssobservationstodetectearthquakeprecursors AT albertopellegrinelli monitoringofionosphericanomaliesusinggnssobservationstodetectearthquakeprecursors |