Application of Total Electron Content Derived from the Global Navigation Satellite System for Detecting Earthquake Precursors

2018 ◽  
pp. 305-317
Author(s):  
Jann-Yenq Tiger Liu ◽  
Katsumi Hattori ◽  
Yuh-Ing Chen
Keyword(s):  
Total Electron Content ◽  
Global Navigation Satellite System ◽  
Satellite System ◽  
Earthquake Precursors ◽  
Electron Content ◽  
Navigation Satellite ◽  
Total Electron ◽  
Global Navigation ◽  
Global Navigation Satellite

scholarly journals Ionosonde total electron content evaluation using International Global Navigation Satellite System Service data

2020 ◽  
Vol 38 (2) ◽  
pp. 347-357 ◽  
Author(s):  
Telmo dos Santos Klipp ◽  
Adriano Petry ◽  
Jonas Rodrigues de Souza ◽  
Eurico Rodrigues de Paula ◽  
Gabriel Sandim Falcão ◽  
...  
Keyword(s):  
Electron Density ◽  
Total Electron Content ◽  
Global Navigation Satellite System ◽  
Satellite System ◽  
Scale Height ◽  
Electron Content ◽  
Navigation Satellite ◽  
Total Electron ◽  
System Service ◽  
Global Navigation Satellite

Abstract. In this work, a period of 2 years (2016–2017) of ionospheric total electron content (ITEC) from ionosondes operating in Brazil is compared to the International GNSS (Global Navigation Satellite System) Service (IGS) vertical total electron content (vTEC) data. Sounding instruments from the National Institute for Space Research (INPE) provided the ionograms used, which were filtered based on confidence score (CS) and C-Level flag evaluation. Differences between vTEC from IGS maps and ionosonde TEC were accumulated in terms of root mean squared error (RMSE). As expected, we noticed that the ITEC values provided by ionosondes are systematically underestimated, which is attributed to a limitation in the electron density modeling for the ionogram topside that considers a fixed scale height, which makes density values decay too rapidly above ∼800 km, while IGS takes in account electron density from GNSS stations up to the satellite network orbits. The topside density profiles covering the plasmasphere were re-modeled using two different approaches: an optimization of the adapted α-Chapman exponential decay that includes a transition function between the F2 layer and plasmasphere and a corrected version of the NeQuick topside formulation. The electron density integration height was extended to 20 000 km to compute TEC. Chapman parameters for the F2 layer were extracted from each ionogram, and the plasmaspheric scale height was set to 10 000 km. A criterion to optimize the proportionality coefficient used to calculate the plasmaspheric basis density was introduced in this work. The NeQuick variable scale height was calculated using empirical parameters determined with data from Swarm satellites. The mean RMSE for the whole period using adapted α-Chapman optimization reached a minimum of 5.32 TECU, that is, 23 % lower than initial ITEC errors, while for the NeQuick topside formulation the error was reduced by 27 %.

Recurrent Neural Networks for Ionospheric Time Delays Prediction Using Global Navigation Satellite System Observables

2021 ◽  
Author(s):  
Maria Kaselimi ◽  
Nikolaos Doulamis ◽  
Demitris Delikaraoglou
Keyword(s):  
Global Navigation Satellite System ◽  
Satellite System ◽  
Electron Content ◽  
Navigation Satellite ◽  
Total Electron ◽  
Proposed Model ◽  
Model Complex ◽  
Global Navigation ◽  
Global Navigation Satellite

<p>Total Electron Content (TEC) is the integral of the location-dependent electron density along the signal path and is a crucial parameter that is often used to describe ionospheric variability, as it is strongly affected by solar activity. TEC is highly depended on local time, latitude, longitude, season, solar and geomagnetic conditions. The propagation of the signals from GNSS (Global Navigation Satellite System) throughout the ionosphere is strongly influenced by short- and long-term changes and ionospheric regular or irregular variations. <br>Long short-term memory network (LSTM) is a specific recurrent neural network architecture and is capable of learning time dependence in sequential problems and can successfully model ionosphere variability. As LSTM networks “memorize” long term correlations in a sequence, they can model complex sequences with various features, where solar radio flux at 10.7 cm and magnetic activity indices are taken into consideration to provide more accurate results. <br>Here, we propose a deep learning architecture to create regional TEC models around a station. The proposed model allows different solar and geomagnetic parameters to be inserted into the model as features. Our model has been evaluated under different solar and geomagnetic conditions. Also, the proposed model is tested for different time periods and seasonal variations and for varying geographic latitudes. </p>

scholarly journals Estimativa e análise de índices de irregularidades da ionosfera utilizando dados GPS de redes ativas

2013 ◽  
Vol 19 (3) ◽  
pp. 374-390 ◽  
Author(s):  
Vinícius Amadeu Stuani Pereira ◽  
Paulo de Oliveira Camargo
Keyword(s):  
Total Electron Content ◽  
Global Positioning System ◽  
Global Navigation Satellite System ◽  
Satellite System ◽  
Electron Content ◽  
Positioning System ◽  
Navigation Satellite ◽  
Total Electron ◽  
Global Positioning ◽  
Global Navigation Satellite

As observáveis GNSS (Global Navigation Satellite System) são afetadas por erros sistemáticos devido aos elétrons livres presentes na ionosfera. O erro associado à ionosfera depende do Conteúdo Total de Elétrons (TEC - Total Electron Content), que é influenciado por diversas variáveis: ciclo solar, época do ano, hora local, localização geográfica e atividade geomagnética. Os receptores GPS (Global Positioning System), GLONASS (Global Orbiting Navigation Satellite System) e Galileo de dupla frequência permitem calcular o erro que afeta as observáveis GNSS e o TEC. Com a taxa de variação do TEC (ROT - Rate of TEC) pode-se determinar índices que indicam irregularidades da ionosfera, permitindo assim fazer inferências sobre o comportamento da mesma. Atualmente é possível realizar estudos dessa natureza no Brasil, devido às diversas Redes Ativas disponíveis, tais como a RBMC/RIBaC (Rede Brasileira de Monitoramento Contínuo/Rede INCRA de Bases Comunitárias) e a Rede GNSS Ativa do Estado de São Paulo. A pesquisa proposta visou à estimativa e análise de índices de irregularidades da ionosfera, além de suprir as geociências de informações sobre o comportamento da ionosfera.

scholarly journals Atmospheric Modes Excited by the 2021 August Eruption of the Fukutoku-okanoba Volcano, Izu-bonin Arc, Observed as Harmonic Oscillations of QZSS Total Electron Conte

2021 ◽  
Author(s):  
Kosuke Heki ◽  
Tatsuya Fujimoto
Keyword(s):  
Global Navigation Satellite System ◽  
Large Amplitude ◽  
Satellite System ◽  
Electron Content ◽  
Navigation Satellite ◽  
Submarine Volcano ◽  
Harmonic Oscillations ◽  
Total Electron ◽  
Ionospheric Total Electron Content ◽  
Global Navigation Satellite

Abstract Continuous Plinian eruptions of volcanoes often excite atmospheric resonant oscillations with several distinct periods of a few minutes. We detected such harmonic oscillations excited by the 2021 August eruption of the Fukutoku-Okanoba volcano, a submarine volcano in the Izu-Bonin arc, in ionospheric total electron content (TEC) observed from global navigation satellite system (GNSS) stations deployed on three nearby islands, Chichijima, Hahajima, and Iwojima. Continuous records with the geostationary satellite of Quasi-Zenith Satellite System (QZSS) presented four frequency peaks of such atmospheric modes. The harmonic TEC oscillations, started at ~5:16 UT, exhibited an unprecedented large amplitude but decayed in a few hours.

scholarly journals Formal estimation of the random component in global maps of total electron content

2016 ◽  
Vol 6 (1) ◽  
pp. 56-60 ◽  
Author(s):  
V. Choliy
Keyword(s):  
Total Electron Content ◽  
Singular Spectrum Analysis ◽  
Satellite System ◽  
Electron Content ◽  
Navigation Satellite ◽  
Random Component ◽  
Optimal Parameters ◽  
Total Electron ◽  
Global Navigation Satellite

Random component of the total electron content (TEC) maps, produced by global navigation satellite system processing centres, was analysed. Helmert transform (HT) and two-dimension singular spectrum analysis (2dSSA) were used. Optimal parameters (in the sense calculation speed versus quality) of the 2dSSA windows were determined along with precision estimations.

scholarly journals OutFin, a multi-device and multi-modal dataset for outdoor localization based on the fingerprinting approach

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Fahad Alhomayani ◽  
Mohammad H. Mahoor
Keyword(s):  
Global Navigation Satellite System ◽  
Urban Areas ◽  
Satellite System ◽  
Reference Points ◽  
Navigation Satellite ◽  
Data Types ◽  
Entry Barrier ◽  
Promising Alternative ◽  
Global Navigation ◽  
Global Navigation Satellite

AbstractIn recent years, fingerprint-based positioning has gained researchers’ attention since it is a promising alternative to the Global Navigation Satellite System and cellular network-based localization in urban areas. Despite this, the lack of publicly available datasets that researchers can use to develop, evaluate, and compare fingerprint-based positioning solutions constitutes a high entry barrier for studies. As an effort to overcome this barrier and foster new research efforts, this paper presents OutFin, a novel dataset of outdoor location fingerprints that were collected using two different smartphones. OutFin is comprised of diverse data types such as WiFi, Bluetooth, and cellular signal strengths, in addition to measurements from various sensors including the magnetometer, accelerometer, gyroscope, barometer, and ambient light sensor. The collection area spanned four dispersed sites with a total of 122 reference points. Each site is different in terms of its visibility to the Global Navigation Satellite System and reference points’ number, arrangement, and spacing. Before OutFin was made available to the public, several experiments were conducted to validate its technical quality.

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