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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Veröffentlicht in:IEEE geoscience and remote sensing letters Jg. 20; S. 1
Hauptverfasser: Karatay, Secil, Gul, Saide Eda
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.01.2023
Schlagworte:
Artificial neural networks
Backpropagation
Bayes methods
Earthquake
Earthquakes
Ionosphere
Neurons
Precursor
Prediction algorithms
Total Electron Content
Training
ISSN:1545-598X, 1558-0571
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Zusammenfassung: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