The study delves into the relationship between ionospheric total electron content (TEC) anomalies and seismic activity, with a focus on Morocco’s 6.8 Mw earthquake on 8 September 2023, lying within a tectonically active region at the convergence of the African and Eurasian Plates. To enhance the reliability of our findings, we incorporate space weather conditions, utilizing indices (Dst, Kp, and F10.7) to pinpoint periods of stable space weather. This minimizes the possibility of erroneously attributing natural ionospheric fluctuations to seismic events. Notably, our TEC analysis unveils positive and negative anomalies, with some occurring up to a week before the earthquake. These anomalies, exceeding predefined thresholds, provide compelling evidence of significant deviations from typical ionospheric conditions. Spatial mapping techniques employing both station-specific vTEC data and pseudorandom noise codes (PRNs) from multiple global navigation satellite system (GNSS) stations highlight a strong correlation between ionospheric anomalies and the earthquake’s epicenter. The integration of PRNs enhances coverage and sensitivity to subtle anomalies. Additionally, the analysis of satellite imagery and ground displacement data using Sentinel-1 confirms significant ground uplift of approximately 15 cm following the earthquake, shedding light on surface responses to seismic events. These findings underscore the potential of ionospheric science in advancing earthquake early warning systems and deepening our understanding of earthquake precursors, thus contributing to the mitigation of seismic event impacts and the protection of lives and infrastructure.

The influence of solar activity on the ionosphere, a critical area of investigation due to its relevance to the Sun–Earth relationship, has been extensively examined through various methodologies. The ability of solar events to induce disturbances in both the ionosphere and the geomagnetic field is widely acknowledged. This specific study focused on sporadic incidents resulting from X-class solar flares that occurred between 2021 and 2022. Utilizing a methodology that involved analyzing data at 5Hz intervals using wavelet algorithms, the data from the GNSS stations of the National Autonomous University of Mexico (UNAM) were investigated. The primary emphasis was on deducing the Total Electron Content (TEC) within the ionosphere. Subsequently, this parameter for each satellite during instances of solar flares was analyzed. The approach uncovered disruptions in the ionosphere triggered by solar flares, even in cases where events transpired at the periphery of the solar disk and were of magnitudes smaller than X2.

This study provides a comprehensive investigation into ionospheric perturbations associated with the Mw 7.5 earthquake on the Noto Peninsula in January 2024, utilizing data from the International GNSS Service (IGS) network. Focusing on Total Electron Content (TEC), the analysis incorporates spatial mapping and temporal pattern assessments over a 30-day period before the earthquake. The time series for TEC at the closest station to the epicenter, USUD, reveals a localized decline, with a significant negative anomaly exceeding 5 TECU observed 22 and 23 days before the earthquake, highlighting the potential of TEC variations as seismic precursors. Similar patterns were observed at a nearby station, MIZU, strengthening the case for a seismogenic origin. Positive anomalies were linked to intense space weather episodes, while the most notable negative anomalies occurred under geomagnetically calm conditions, further supporting their seismic association. Using Kriging interpolation, the anomaly zone was shown to closely align with the earthquake’s epicenter. To assess the consistency of TEC anomalies in different seismic events, the study also examines the Mw 7.1 Nichinan earthquake in August 2024. The results reveal a prominent negative anomaly, reinforcing the reliability of TEC depletions in seismic precursor detection. Additionally, spatial correlation analysis of Pearson correlation across both events demonstrates that TEC coherence diminishes with increasing distance, with pronounced correlation decay beyond 1000–1600 km. This spatial decay, consistent with Dobrovolsky’s earthquake preparation area, strengthens the association between TEC anomalies and seismic activity. This research highlights the complex relationship between ionospheric anomalies and seismic events, underscoring the value of TEC analysis as tool for earthquake precursor detection. The findings significantly enhance our understanding of ionospheric dynamics related to seismic events, advocating for a comprehensive, multi-station approach in future earthquake prediction efforts.

The occurrence of earthquakes, which can strike suddenly without any warning, has always posed a potential threat to humanity. However, researchers worldwide have been diligently studying the mechanisms and patterns of these events in order to develop warning systems and improve detection methods. One of the most reliable indicators for predicting large earthquakes has been the examination of electron availability in the ionosphere. This study focuses on analyzing the behavior of the Total Electron Content (TEC) in the ionosphere during the 30-day period leading up to the three most devastating earthquakes of the past decade. Specifically, the data were examined from the cGPS stations closest to the epicenters: MERS for the Turkey earthquake with 7.8 Mw on 6 February 2023, CHLM for the Nepal earthquake with 7.8 Mw on 25 April 2015, and MIZU for the Japan earthquake with 9.1 Mw on 11 March 2011. Notable positive and negative anomalies were observed for each earthquake, and the vertical Total Electron Content (vTEC) for each PRN (pseudo-random number) was plotted to determine the specific time of the TEC anomaly. The spatial distribution of vTEC for the anomalous specific time revealed that the anomalies were in close proximity to the earthquake epicenters, particularly within denser fault zones.

The layer of the Earth’s atmosphere known as the ionosphere presents a significant obstacle to global satellite navigation systems (GNSS) due to its ability to introduce errors. To address this challenge, various navigation systems have introduced new signals designed to minimize the errors caused by the ionosphere. These signals not only aid in error reduction but also facilitate the examination of electron content behavior. This study focuses on the analysis of vTEC plots obtained from RINEX data collected at the INEG station in Aguascalientes, Mexico, from 2011 to 2018, with a particular emphasis on highly intense geomagnetic storms characterized by values below −100 nT. Our analysis of these plots employed the Probability Density Function (PDF), which allows for the graphical representation of data distribution. This distribution is then examined in conjunction with the station’s Total Electron Content (TEC) values and the Dst index during the corresponding geomagnetic storm events. The findings establish the correlation between each of these parameters during such events.