A semi-supervised total electron content anomaly detection method using LSTM-auto-encoder. (December 2022) - Muhammad, Ahmad; Külahcı, Fatih
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 GNSS-TEC receivers (ankr, ista, and tubi) in Türkiye. 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. Graphical abstract: The LSTM Auto-encoder architecture for the detection of TEC. Image 1 Highlights: Seismo-Ionospheric studies, variations of the vertical total electron content over a seismic event. Artificial intelligence for TEC anomaly detection. The long-short term memory (LSTM) based auto-encoder network is proposed for the detection of TEC anomalies.