A semi-supervised total electron content anomaly detection method using LSTM-auto-encoder

Abstract

Total electron content (TEC) is one of the important features used in studying of ionospheric properties. In seismo-ionospheric studies, variations/anomalies of the vertical total electron content over a seismic event are used to study the manifestation of lithospheric in the ionosphere. It is significant to design a robust TEC anomaly detection algorithm, which is capable of detecting non-spurious Seismo-induced TEC variations. In this paper, the long short term memory (LSTM) based auto-encoder network is presented for the detection of TEC anomalies recorded by GNSS receivers. The LSTM-auto-encoder is applied as a semi-supervised scheme to learn TEC responses in quiet solar and geomagnetic conditions. It then uses the learned TEC features to identify anomalies in a given TEC input data. The method is implemented to detect TEC anomalies recorded by three different GNSSTEC receivers (ankr, ista, and tubi) in Turkiye. The model is also used to verify results from a recently published study on Mexico earthquake (magnitude 7.4). In each case, plausible results are obtained, and the relationship between detected anomalies with some lithosphere-atmosphere processes are discussed. The method highlights the significance/applications of AI in studying ionospheric variations.