Abstract
Abstract. The analysis of the unexpected ionospheric phases before large earthquakes is one of the cutting-edge issues in earthquake prediction studies. In this study, the total electron content (TEC) data from seven International GNSS Service (IGS) stations and the global ionosphere maps (GIMs) were used. Short-time Fourier transform (STFT) and a running median process were applied to the TEC time series to detect abnormalities before the Mw 7.3 Iran–Iraq border earthquake on 12 November 2017. The analyses showed positive anomalies 8–9 d before the earthquake and some positive and negative anomalies 1–6 d before the earthquake. These anomalies were cross-checked using the Kp, Dst, F10.7, Bz component of the interplanetary magnetic field (IMF Bz), electric field (Ey), and plasma speed (VSW) space weather indices. The results showed that the anomalies 1–6 d before the earthquake were caused by a moderate magnetic storm. Moreover, the positive anomalies 8–9 d before the earthquake were likely related to the Iran–Iraq border earthquake due to quiet space weather, local dispersion, and the proximity to the epicenter.
Highlights
The ionosphere is a three-dimensional dispersive atmospheric layer for electromagnetic signals traveling from space to the Earth
The paper is organized as follows: information on the Iran–Iraq border earthquake is given in Sect. 2.1; Sect. 2.2 presents data observations; Sect. 2.3 describes GPS-total electron content (TEC) and global ionosphere maps (GIMs)-TEC data calculations; Sect. 2.4 capaciously explains the methods used in the study; and the results and conclusions are given in Sects. 3 and 4, respectively
During the storm-induced time interval, the highest positive divergence of TEC values (DTEC) was detected on 7 November (+115 %) and the lowest DTEC was detected on 9 November (−60 %) at the LROC station, which is located at the outside of the earthquake preparation area (EPA)
Summary
The method was compared with sliding quartile and Kalman filter methods They found that the linear model was more effective at detecting the TEC anomalies caused by the Nepal earthquake in temporal and spatial analyses. Freund et al (2006), in contrast, detected the ionization of the side surfaces of a granite block in the laboratory and proposed that the air was ionized due to an increase in the mechanical pressure applied to the upper surface Under this assumption, strains occurring in the huge rocks in the lithosphere before earthquakes could cause electron emission towards the atmosphere and, may cause changes in the ionosphere (Freund et al, 2009). Temporal, spatial, and spectral analyses were applied to the GNSS-based TEC data to detect ionospheric anomalies before the Mw 7.3 Iran–Iraq border earthquake on 12 November 2017. The paper is organized as follows: information on the Iran–Iraq border earthquake is given in Sect. 2.1; Sect. 2.2 presents data observations; Sect. 2.3 describes GPS-TEC and GIM-TEC data calculations; Sect. 2.4 capaciously explains the methods used in the study; and the results and conclusions are given in Sects. 3 and 4, respectively
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