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

被引：8
作者：
Karatay, Secil [1 ]
Gul, Saide Eda [1 ]
机构：
[1] Kastamonu Univ, Dept Elect & Elect Engn, Kastamonu 37150, Turkiye
来源：
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2023年 / 20卷
关键词：
Earthquakes; Artificial neural networks; Training; Neurons; Bayes methods; Backpropagation; Prediction algorithms; Artificial neural network (ANN); Bayesian regularization backpropagation (BRB); earthquake; ionosphere; total electron content (TEC);
D O I：
10.1109/LGRS.2023.3262028
中图分类号：
P3 [地球物理学]; P59 [地球化学];
学科分类号：
0708 ; 070902 ;
摘要：
The 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 back propagation artificial neural network (ANN) Bayesian regularization (BR) algorithm is applied to detect the seismic disturbances and anomalies by predicting global positioning system (GPS)-total electron content (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.
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页数：5
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