GNSS TEC-Based Earthquake Ionospheric Perturbation Detection Using a Novel Deep Learning Framework

Abstract

In this article, a new method for seismic ionospheric Global Navigation Satellite System (GNSS) total electron content (TEC) based anomaly detection using a deep learning framework is proposed. The performance of the proposed encoder–decoder long short-term memory extended model is compared with those of five other benchmarking predictors. The proposed model achieves the best performance (R2 = 0.9105 and root-mean-square error (RMSE) = 2.6759) in predicting TEC time series data, demonstrating a 20% improvement in R2 and 39.1% improvement in the RMSE over the autoregressive integrated moving average model. To detect the pre-earthquake ionospheric disturbances more accurately, a reasonable strategy for determining anomaly limits is also proposed. Finally, the method is applied to analyze the pre-earthquake ionospheric TEC disturbance of the 2016 Xinjiang Ms 6.2 earthquake. By excluding the effects of solar activity and geomagnetic activity, obvious ionospheric anomalies could be observed, occurring during 4–8 days prior to, and on 1 day before, the earthquake. Negative anomalies tended to occur in the earlier period, whereas positive anomalies occurred closer to the earthquake time, with increasing anomaly intensity with temporal proximity. Furthermore, confusion analysis is used in this article to verify the reliability of the proposed model. The proposed model achieves significant improvements in predicting GNSS TEC time series and is shown to advance the performance of earthquake anomaly detection technology.

Keywords

• Anomaly detection
• deep learning
• earthquake
• ionospheric perturbation
• time series prediction