A New Method for Detection of Pre‐Earthquake Ionospheric Anomalies

Abstract: This paper proposed a new method for detection of pre‐earthquake ionospheric anomalies using time series analysis based on the Autoregressive Integrated Moving Average (ARIMA) model. Firstly, we compared the precision of this new method with the traditional ones, namely the Inter Quartile Range (IQR) method and the sliding window method, in predicting the TEC reference background values. The results show that the precision of the former is obviously better than the latter, while the average prediction residual errors of the former are twice smaller than the latter. To detect pre‐earthquake ionospheric anomalies more accurately, besides precise reference background values, its reasonable error range is also needed. Therefore, this paper put forward a new method to calculate the reference background value's upper and lower bounds. Finally, the earthquake happened in Sumatra on January 10, 2012 was taken as example. We analyzed its pre‐earthquake ionospheric anomalies and proved the effectiveness of the new method. The results show that obvious ionospheric anomalies appeared on the 13th, 8th to 9th and 1st to 2nd days before the earthquake as well as several hours during the day when the earthquake happened. Furthermore, positive anomalies (observational values higher than normal ones) generally appeared to the north of the epicenter and were much earlier before the earthquake occurrence, while the negative ones (observation values lower than the normal) occurred in any direction to the epicenter, and close to the moment the earthquake occurrence. Through statistics for the frequency of ionospheric anomalies occurred at different times of the day, we have also found a valuable law that the closer the time of the day before the earthquake moment, the higher the occurrence frequency of the ionospheric anomalies, which is likely to be an important reference to the more accurate earthquake prediction in the future.