Tsunami detection by GPS-derived ionospheric total electron content

Shrivastava, Mahesh N.; Maurya, Amit Kumar; González, Gabriel; Sunil, P. S.; González, J. A.; Salazar, Pablo; Aránguiz, Rafael

doi:10.1038/s41598-021-92479-3

Cited by 11 publications

(20 citation statements)

References 79 publications

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“…(2015) and Shrivastava et al. (2021) for the 1 April 2014 earthquake (Figure 4c); by Reddy et al. (2016) and Shrivastava et al.…”

Section: Discussionmentioning

confidence: 82%

“…(2016) and Shrivastava et al. (2021) for the 16 September 2015 earthquake (Figures 3, 7a, and 7b); and by Shults et al. (2016) and X. Liu et al.…”

Section: Discussionmentioning

confidence: 84%

“…These observations provided information on TIDs with high temporal but low spatial resolution. Only after the substantial coverage increase of the networks of dual and multifrequency Global Navigation Satellite System (GNSS) receivers, the spatial propagation of TIDs could be determined (e.g., Afraimovich et al., 2001; Astafyeva & Heki, 2009; Astafyeva et al., 2009; Calais & Minster, 1995; Ducic et al., 2003; Galvan et al., 2011, 2012; Grawe & Makela, 2015, 2017; Heki, 2011; Heki & Ping, 2005; Kakinami et al., 2012; Kamogawa et al., 2016; X. Liu et al., 2017; Perevalova et al., 2014; Ravanelli et al., 2021; Reddy, 2016; Reddy & Seemala, 2015; Reddy et al., 2015, 2016; Rolland et al., 2010, 2013; Shrivastava et al., 2021; Shults et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

“…,Astafyeva et al (2013),Perevalova et al (2014), and for the 27 February 2010 earthquake mainshock (Figure4a); byPerevalova et al (2014) and for an aftershock of the same earthquake; by andShrivastava et al (2021) for the 1 April 2014 earthquake (Figure4c); byReddy et al (2016) andShrivastava et al (2021) for the 16 September 2015 earthquake(Figures 3, 7a, and 7b); and byShults et al (2016) and X Liu et al (2017). after the volcanic eruption of 22-23 April 2015 (Figure4b).…”

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confidence: 97%

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Traveling Ionospheric Disturbances Observed Over South America After Lithospheric Events: 2010–2020

et al. 2022

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The ionosphere behavior is mainly dependent on solar radiation and the geomagnetic configuration, but it is also affected by tides, neutral winds, and atmosphere waves. There are many kinds of waves in the atmosphere, with periods ranging from 2 years (quasi-biennial oscillations) to less than a second (infrasonic waves). These include planetary waves, acoustic waves, and internal gravity waves (Rishbeth, 1988) and are associated with auroral activity, orography, seismic activity, tsunamis, volcanic eruptions, explosions, large-scale tropospheric weather phenomena, and so on. This association indicates a strong coupling between the ionosphere and the lower layers of the atmosphere and the lithosphere (Hines, 1972;Rishbeth, 1988).In the case of lithospheric phenomena such as earthquakes, earthquake-associated tsunamis, and volcanic eruptions, ionospheric responses are observed from a few minutes after the events. Two kinds of waves have been reported: acoustic waves and gravity waves (Hines, 1960;Rishbeth & Garriott, 1969). Although they widely differ by frequencies, the main difference is that for acoustic waves, the restoring force arises from the compressibility of the atmosphere, while for gravity waves, it comes from the gravitational acceleration (Meng et al., 2019). Both wave types can be detected in the ionosphere because their amplitude increases exponentially as they propagate upward due to the decrease in the atmospheric density with height (Astafyeva, 2019;Hines, 1972). Acoustic waves are longitudinal waves and propagate vertically when generated, for example, by a sudden crustal motion during an earthquake. These waves propagate at a speed close to the speed of sound near the surface but slow down with height, reaching the ionosphere (∼300 km) in ∼8-9 min.On the other hand, internal gravity waves are transverse waves, such as those generated by tsunamis. They can only propagate obliquely in the atmosphere (Hines, 1972), with speeds close to the tsunami, thus reaching the ionosphere in 45-60 min (Astafyeva, 2019;Meng et al., 2019). When reaching the ionosphere, the disturbances generated by these acoustic and gravity waves propagate spatially to other regions and are called Traveling Ionospheric Disturbances (TIDs). Observed TIDs with horizontal propagation speeds between 200 and 300 m/s are

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“…(2015) and Shrivastava et al. (2021) for the 1 April 2014 earthquake (Figure 4c); by Reddy et al. (2016) and Shrivastava et al.…”

Section: Discussionmentioning

confidence: 82%

“…(2016) and Shrivastava et al. (2021) for the 16 September 2015 earthquake (Figures 3, 7a, and 7b); and by Shults et al. (2016) and X. Liu et al.…”

Section: Discussionmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 97%

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Traveling Ionospheric Disturbances Observed Over South America After Lithospheric Events: 2010–2020

et al. 2022

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“…Their technique has been implemented at several GNSS-receivers around the Pacific Ocean (https://iono2la.gdgps.net), and is aiming -in the future -to detect traveling ionospheric disturbances (TIDs) associated with tsunamis. Shrivastava et al [23] demonstrated the possibility of tsunami detection by GPS-derived TEC, however, no discussion on the real-time use was provided.…”

Section: Introductionmentioning

confidence: 99%

Determining spatio-temporal characteristics of Coseismic Travelling Ionospheric Disturbances (CTID) in near real-time

Maletckii¹,

Astafyeva²

2021

Preprint

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Earthquakes are known to generate ionospheric disturbances that are commonly referred to as co-seismic travelling ionospheric disturbances (CTID). In this work, for the first time, we present a novel method that enables to automatically detect CTID in ionospheric GNSS-data, and to determine their spatio-temporal characteristics (velocity and azimuth of propagation) in near-real time (NRT), i.e., less than 15 minutes after an earthquake. The obtained instantaneous velocities allow us to understand the evolution of CTID and to estimate the location of the CTID source in NRT. Furthermore, also for the first time, we developed a concept of real-time traveltime diagrams that aid to verify the correlation with the source and to estimate additionally the propagation speed of the observed CTID. We apply our methods to the Mw7.4 Sanriku earthquake of 09/03/2011 and the Mw9.0 Tohoku earthquake of 11/03/2011, and we make a NRT analysis of the dynamics of CTID driven by these seismic events. We show that the best results are achieved with high-rate 1Hz data. While the first tests are made on CTID, our method is also applicable for detection and determining of spatio-temporal characteristics of other travelling ionospheric disturbances that often occur in the ionosphere driven by many geophysical phenomena.

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Machine learning-based detection of TEC signatures related to earthquakes and tsunamis: the 2015 Illapel case study

Fuso,

Crocetti,

Ravanelli

et al. 2024

GPS Solut

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Earthquakes and tsunamis can trigger acoustic and gravity waves that could reach the ionosphere, generating electron density disturbances, known as traveling ionospheric disturbances. These perturbations can be investigated as variations in ionospheric total electron content (TEC) estimated through global navigation satellite systems (GNSS) receivers. The VARION (Variometric Approach for Real-Time Ionosphere Observation) algorithm is a well-known real-time tool for estimating TEC variations. In this context, the high amount of data allows the exploration of a VARION-based machine learning classification approach for TEC perturbation detection. For this purpose, we analyzed the 2015 Illapel earthquake and tsunami for its strength and high impact. We use the VARION-generated observations (i.e., dsTEC/dt) provided by 115 GNSS stations as input features for the machine learning algorithms, namely, Random Forest and XGBoost. We manually label time frames of TEC perturbations as the target variable. We consider two elevation cut-off time series, namely, 15° and 25°, to which we apply the classifier. XGBoost with a 15° elevation cut-off dsTEC/dt time series reaches the best performance, achieving an F1 score of 0.77, recall of 0.74, and precision of 0.80 on the test data. Furthermore, XGBoost presents an average difference between the labeled and predicted middle epochs of TEC perturbation of 75 s. Finally, the model could be seamlessly integrated into a real-time early warning system, due to its low computational time. This work demonstrates high-probability TEC signature detection by machine learning for earthquakes and tsunamis, that can be used to enhance tsunami early warning systems.

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Tsunami detection by GPS-derived ionospheric total electron content

Cited by 11 publications

References 79 publications

Traveling Ionospheric Disturbances Observed Over South America After Lithospheric Events: 2010–2020

Traveling Ionospheric Disturbances Observed Over South America After Lithospheric Events: 2010–2020

Determining spatio-temporal characteristics of Coseismic Travelling Ionospheric Disturbances (CTID) in near real-time

Machine learning-based detection of TEC signatures related to earthquakes and tsunamis: the 2015 Illapel case study

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