Numerous research (Draz, et al., 2023; Cheng & Fu, 2022; De Oliveira-Júnior, et al., 2022) showed unusual disturbances in atmosphere and ionosphere related to seismic activities in EQ preparation time as measured by atmospheric parameters and GNSS TEC. There are various parameters to detect perturbations in ionosphere over the epicenter of the earthquake such as atmospheric and ionospheric indices, the electron content in ionosphere and critical frequency (\({f}_{o }{F}_{2}\)) in F2 layer (Abbasi, 2021; Chen, 2021). A variety of development models and statistical techniques are used in this research field for the detection of anomalies in ionosphere over the EQ epicenter (Shah, 2020; Ahmed, 2018).
During the seismic preparation era, there were numerous reports on the application of various methodologies to find variations in ionosphere for an EQ from space and ground measurements. The GNSS TEC data extracted from RINEX file showed substantial variation in ionosphere in a time frame of 5 to 10 days prior and after the earthquake (Huang, 2021; Jin, 2014). Furthermore, the study of EQ in Taiwan (Chi-Chi 1999) stressed the significance to use a statistical technique for the assessment of strong anomalies in ionosphere(Zhang, 2021; Thomas, 2017). In addition, (Calabia & Jin, 2020; Ahmed, 2018; Adebesin, et al., 2013) investigated the augmentation, reduction, and appearance rate of anomalies in ionosphere, among other morphological aspects of seismic ionospheric anomalies. DEMETER satellites are helpful to find the fluctuations in the observations of density of electron, (Chen, 2021; Shah, 2020)noted a strong augmentation that coincided with the seismic ionospheric anomaly. (De Santis, 2019) also discovered ionospheric abnormalities from Swarm satellite data of the EQs all around the world in a time span of two months.
According to (Freund, 2002; De Santis, 2019), there are various theories explaining the phenomenon of ionosphere and lithosphere connection over the epicenter during 1 to 7 days’ time window prior the EQ. For instance, (Zhang, 2019; Yu, 2021) indicate that the ionization is caused by the Radon gas which is discharged by the EQ sick zone few days prior the EQ. Similar to this, (Adebesin, et al., 2013; Tian, 2020) provided an explanation of how tectonic forces caused stressed rocks inside the Earth to be lifted to the lithosphere, where p-hole events occurred. Numerous events took place, including ion production and molecular reactions, and also in the vicinity of the future EQ variations in electric field are observed. P-holes spread through the atmosphere to the ionosphere at various altitudes as more and more of them reach the surface (Timoçin, 2020; Thomas, 2017).
The perturbations in ionosphere are closely related to the variation in electrons over the epicenter of the EQ (Tian, 2019; Jin, 2014). The findings of this numerical technique indicate that the positive charge carriers produced an electric field in the EQ preparation area. These carriers act as conduits for the flow of charge vertically to the atmosphere, which in turn propels plasma bubbles into the ionosphere. To demonstrate the precursory behavior of the radon gas emission in the atmosphere prior to a major EQ, (Tariq, 2019; Stankov, et al., 2010) devised Lithosphere-Atmosphere-Ionosphere Coupling model. The Radon gas is the main cause for the thermal anomalies appeared in the upper atmosphere. In (De Santis, 2019; Kuo, 2011) and (Li, 2022; Le, 2011), emission of this gas has many effects on atmosphere such as change in land surface temperature, ionization and formation of charged particles and variations in electric fields intensity. The EQ induced anomalies in ionosphere appeared within 1 to 10 days of time window prior and after the earthquake (Yu, 2021; Thomas, 2017).
All the previous research work by using numerous numerical or statistical techniques and development models to illustrate a plausible link between anomalies and seismic activities. To achieve accurate assessment of this phenomenon the advanced techniques can be correlated with each other. In this research paper the GNSS temporal TEC data is processed to deeply investigate the variation in ionosphere. The diurnal TEC analysis is done to monitor hourly manner changes in ionosphere. The day and night data of swarm satellites are used to detect the variations in ionosphere for an earthquake. Furthermore, the findings of Swarm’s satellites are correlated with GNSS stations to find better assessment of our results. The TEC observations of the four IGS stations near the origin of this EQ are retrieved to examine perturbations in ionosphere. The 30th November 2018 earthquake is unique in its geographical location and time aspects. The three GNSS CORS sites near the EQ origin are situated within the Dobrovolsky region. Only one site located outside. This gives us a chance to validate our results for the GNSS station lies inside and outside of the Dobrovolsky region. Additionally, a weak geo magnetic storm occurred 25 days prior this EQ, by processing data for both geo magnetic storm and EQ we can closely examined and differentiate between the ionospheric anomalies induced by an EQ and geomagnetic storm. The primary goal of this research is to inspect ionosphere TEC anomalies using GNSS stations and Swarm satellite data. Finding the distinction between seismic and geomagnetic storm anomalies is another goal. We arranged our paper in this way: the data and methodology are included in Section 2; in section 3 we have results and discussions, and Section 4 has the conclusions.