Deep learning of detecting ionospheric precursors associated with M ≥ 6.0 earthquakes in Taiwan

A short-term (30 days before an earthquake) prediction of an earthquake is a big challenge in seismology. We apply deep learning to the ionospheric total electron content (TEC) data between 2003 and 2014 to detect the seismo-ionospheric precursors (SIP) of M ≥ 6.0 earthquakes in Taiwan. The bidirectional Long Short-Term Memory (Bi-LSTM) network is employed to use observed input data (features) to obtain the sequential TEC variations. The five input features are sequential vectors of TEC, the geomagnetic index Dst, the solar activity index F10.7, sunspot number (SSN), and solar emission index Lyman-α. The seismo-ionopheric precursors can be identified by the normalized difference between the predicted TEC and the observed TEC data. The results show that all 22 positive cases with M ≥ 6.0 earthquakes are successfully predicted, while 10 of 19 negative cases without M ≥ 5.3 earthquakes are correctly predicted "No". A high accuracy of 78.05% is obtained.