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

Abstract

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 <italic>Mw</italic> 7 are smaller than those for greater than 7.