1st Intercontinental Geoinformation Days (IGD), Mersin, Turkey, 25 - 26 November 2020, pp.1-4
Since
ionospheric variability changes dramatically before the major earthquakes (EQs),
the detection of ionospheric anomalies to EQ forecasts has become a new trend
in the current era. Therefore, there is a call to identify highly accurate,
advance, and intelligent models to identify these anomalies. In this study, we
have proposed a deep learning-based method, long short-term memory (LSTM)
network, to detect ionospheric anomalies using the Total Electron Content (TEC)
time series of Awaran, Pakistan (Mw=7.7) EQ on September 24, 2013. We have
taken 45 days TEC data with a 2-h temporal resolution and train the models with
an accuracy of 0.07 TECU. After fitted models with optimal hyperparameters, we
have applied both to forecast TEC values for one week before the EQ. The
anomalies, high differences (crossing the threshold value) between forecasted
and observed TEC, are an indication of abnormal activities, e.g. earthquake,
space weather etc. In this study, we detected anomalies for the Awaran EQ. We
conclude our results with the identification of ionospheric anomalies that
occurred before the EQ results showed that strong positive anomalies are
recorded 3 days before (on Sep 21) the EQ. These anomalies are thought to be
related to Awaran EQ due to the quiet space weather conditions on the anomalies
days. This study brings new insights into the AI techniques in the
seismoionospheric EQ forecasting.