Prediction of GPS-TEC on Mw>5 Earthquake Days Using Bayesian Regularization Backpropagation Algorithm

Detection of earthquake precursor signals a few days before the earthquake day is one of the most studied subjects today. In recent years, a strong correlation is observed between earthquakes and ionospheric parameters. In this study, a Feed Forward Backpropagation Artificial Neural Network Bayesian...

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Bibliographic Details
Published inIEEE geoscience and remote sensing letters Vol. 20; p. 1
Main Authors Karatay, Secil, Gul, Saide Eda
Format Journal Article
LanguageEnglish
Published IEEE 01.01.2023
Subjects
Artificial neural networks
Backpropagation
Bayes methods
Earthquake
Earthquakes
Ionosphere
Neurons
Precursor
Prediction algorithms
Total Electron Content
Training
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Summary:Detection of earthquake precursor signals a few days before the earthquake day is one of the most studied subjects today. In recent years, a strong correlation is observed between earthquakes and ionospheric parameters. In this study, a Feed Forward Backpropagation Artificial Neural Network Bayesian Regularization algorithm is applied to detect the seismic disturbances and anomalies by predicting GPS-TEC on earthquake days with magnitude greater than 5. It is observed that TEC is predicted with greater error margins for the stations at a maximum distance of 50 km from the epicenters. The errors for earthquakes less than Mw 7 are smaller than those for greater than 7.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2023.3262028