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Ionospheric anomalies related to the Mw 6.5 Samar, Philippines earthquake

Eroglu Emre

Acta Geophysica

2023

Vol. 71, no. 2

601--611

Models belonging to the ionosphere that is directly affected by factors such as solar activity, geomagnetic storm, earthquake, seasonal changes, and geographical location need to be considered altogether. In this sense, the cause of the ionospheric anomalies should be meticulously distinguished from each other. Ionospheric anomalies that occur before or (and) after an earthquake have a serious place in earthquake prediction studies. Total electron content (TEC) is one of the significant parameters to be able to discuss the anomalies of the ionosphere. This essay investigates ionospheric anomalies before and after the Mw 6.5 Samar, Philippines (12.025° N, 125.416° E and November 18, 2003, at 17:14 UT) earthquake. The paper analyzes anomalies with the aid of the TEC (TECU) map. In the paper, the time-domain TEC variables are transferred to the frequency-domain for observing some clues-peaks by short-term Fourier transformation spectral analysis. The discussion handles the effect of the solar activity with the F10.7 (sfu) index and the effect of geomagnetic storms with Bz (nT), v (km/s), P (nPa), E (mV/m), Kp (nT), and Dst (nT) parameters (index). The lower and upper boundaries of the TEC map obtained from the International Reference Ionosphere (IRI-2016) are calculated with the help of median and standard deviation. The boundary-setting process is named statistical analysis. TEC data exceeding the boundaries are marked as anomaly data. According to the paper, 11-day anomalies (9-day of which belong to pre-earthquake) are detected. Probably, the anomalies observed on November 6, 7, and 12 belong to the Samar earthquake.

Support Vector Regression model to predict TEC for GNSS signals

Sivakrishna Kondaveeti, Ratnam Devanaboyina Venkata, Sivavaraprasad Gampala

Acta Geophysica

2022

Vol. 70, no 6

2827--2836

Ionospheric Total Electron Content (TEC) predominantly affects the radio wave communication and navigation links of Global Navigation Satellite Systems (GNSS). The ionospheric TEC exhibits a complex spatial–temporal pattern over equatorial and low latitude regions, which are difficult to predict for providing early warning alerts to GNSS users. Machine Learning (ML) techniques are proven better for ionospheric space weather predictions due to their ability of processing and learning from the available datasets of solar-geophysical data. Hence, a supervised ML algorithm such as the Support Vector Regression (SVR) model is proposed to predict TEC over northern equatorial and low latitudinal GNSS stations. The vertical TEC data estimated from GPS measurements for the entire 24th solar cycle period, 11 years (2009–2019), is considered over Bengaluru and Hyderabad International GNSS Service (IGS) stations. The performance of the proposed SVR model with kernel Gaussian or Radial Basis Function (RBF) is evaluated over the two selected testing periods during the High Solar Activity (HSA) year, 2014 and the Low Solar Activity (LSA) year, 2019. The proposed model performance is compared with Neural Networks (NN) model, and International Reference Ionosphere (IRI-2016) model during both LSA and HSA periods. It is noticed that the proposed SVR model has well predicted the VTEC values better than NN and IRI-2016 models. The experimental results of the SVR model evidenced that it could be an effective tool for predicting TEC over low-latitude and equatorial regions.