To understand why this matters, it helps to start with the quantity being measured. The ionosphere forms where solar and cosmic radiation strip electrons from atoms in the mesosphere, thermosphere and exosphere, creating a plasma of free electrons and ions. The Total Electron Content, or TEC, counts the total number of electrons along a signal path, typically between a Global Navigation Satellite System satellite and a ground receiver. Because GPS and other positioning signals must traverse this charged region, TEC is both a nuisance for navigation accuracy and a remarkably sensitive diagnostic of what is happening overhead. Variations in TEC are driven primarily by space weather, including solar wind, solar flux and geomagnetic storms, but a growing body of research on Lithosphere-Atmosphere-Ionosphere Coupling, or LAIC, suggests that large earthquakes may perturb the ionosphere as well, through mechanisms that propagate energy and electric charge upward from the fault zone before and during rupture.

The idea that earthquakes leave traces in the sky is not new, but it remains contentious. Preparation zones around large faults can span hundreds of kilometers, and proposed coupling mechanisms range from acoustic-gravity waves launched by ground motion to electromagnetic effects associated with stress accumulation and radon release. What has been missing is a rigorous way to separate the seismic component of ionospheric variability from the much larger space-weather background. This is precisely the gap the new study addresses. Rather than simply hunting for anomalies, the researchers built predictive models of TEC using only solar and geomagnetic inputs, then evaluated how well those models captured the TEC behavior observed around the three Japanese earthquakes. Any systematic shortfall or distinctive pattern in the residuals becomes evidence of ionospheric disturbance tied to the seismic events themselves.

The data backbone of the study comes from three Japanese GNSS stations: USUD, AIRA and ISHI. True TEC values for these stations were obtained from IONOLAB, a well-established service for automatic near-real-time estimation of GPS-derived TEC. Solar and geomagnetic drivers were drawn from NASA’s OMNIWeb database and included the solar wind speed, the F10.7 solar flux index, the Disturbance Storm Time index known as Dst, and the Ap index of geomagnetic activity. Earthquake details were compiled from the United States Geological Survey. Together these inputs form a multivariate prediction problem: given the current state of the Sun and the geomagnetic field, what should the TEC be at each station? Deviations between prediction and observation during the earthquake windows then become the object of analysis.

Three machine learning architectures formed the core of the comparison. The first was a Gated Recurrent Unit network, a recurrent neural network designed to retain temporal dependencies in sequential data, trained with the Root Mean Square Propagation optimizer, or RMSProp, which adapts learning rates per parameter and is well suited to the noisy gradients typical of geophysical time series. The second was a Random Forest, an ensemble of decision trees introduced by Leo Breiman in 2001, whose hyperparameters were optimized using a Particle Swarm Optimizer, a bio-inspired search method that mimics the social behavior of bird flocks to explore the parameter space efficiently. The third was XGBoost, the extreme gradient boosting algorithm of Chen and Guestrin, optimized here with a Genetic Algorithm, an evolutionary search technique that iteratively selects, crosses and mutates candidate hyperparameter configurations. These three were benchmarked against NeQuick, a physics-based empirical model of the ionosphere widely used in the GNSS community.

Evaluation rested on four complementary statistical metrics: Root Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error and the Symmetric Mean Absolute Percentage Error. RMSE penalizes large errors heavily, MAE treats all errors equally, and the percentage-based measures express accuracy relative to the magnitude of the true values, which matters when TEC varies strongly with latitude, season and solar activity. The authors also performed cross-validation on Global Ionospheric Map data for the two best-performing machine learning models, a step that guards against the possibility that strong results reflect overfitting to a single dataset rather than genuine predictive skill. Cross-validation confirmed what the event-based comparisons suggested: XGBoost held its advantage when tested on data it had not been tuned against.

The headline result is that across all three earthquakes, the genetically optimized XGBoost model achieved the lowest RMSE and MAE values of any approach tested, consistently outperforming the GRU network, the swarm-optimized Random Forest and the NeQuick model. Among the remaining methods, Random Forest beat the GRU, an interesting outcome given that the recurrent network is explicitly designed for time series while the tree ensemble is not. The authors suggest that all models were able to capture TEC variations associated with the seismic events, which implies that the ionospheric response to these earthquakes is structured enough to be learned from solar and geomagnetic context, and that deviations from that learned baseline carry information about the lithosphere’s influence on the upper atmosphere.

There are several reasons why a gradient-boosted tree model might outperform a recurrent network in this setting. XGBoost builds an additive ensemble of shallow trees, each correcting the errors of its predecessors, and this architecture handles heterogeneous input features, such as the mix of solar wind speed, flux measurements and geomagnetic indices used here, without requiring the inputs to be sequenced or normalized to the same temporal scale. Boosting also tends to be robust with modest training sets, which is a real constraint in earthquake studies where the number of usable large events is inherently limited. The genetic algorithm’s global search over hyperparameters, including tree depth, learning rate and regularization strength, may have found configurations that gradient-based tuning would miss. The GRU, by contrast, must learn its temporal filters from data, and with only a handful of earthquake windows available for training, the recurrent model may simply have had too little sequence data to exploit its architectural advantage.

The broader significance of the work lies in the convergence of two research communities. Space-weather modelers have long sought better TEC prediction, because unmodeled ionospheric delay is one of the largest error sources in precise positioning and satellite navigation. Seismologists, meanwhile, have spent decades searching for reliable precursory signals, and ionospheric anomalies have been reported before earthquakes in Japan, Peru, Türkiye, Morocco, Cyprus, Alaska and the Himalayas, using ground GNSS networks and satellites such as DEMETER. By showing that machine learning models trained on space-weather inputs can characterize the expected ionospheric state with high fidelity, the study provides a cleaner statistical framework for asking whether specific anomalies exceed what space weather alone can explain. The LAIC hypothesis gains a sharper test, and navigation science gains better models in the same stroke.

Cautions remain, and the authors are careful not to overclaim. Three earthquakes, however well studied, do not establish a universal precursor signature, and the field has a long history of anomalies that looked compelling for one event but failed to generalize. The coupling physics connecting fault rupture to ionospheric electron content is still debated, and confounding factors such as volcanic activity, which Japan has in abundance, complicate attribution. What this study demonstrates is methodological progress: a validated, cross-checked machine learning pipeline that predicts TEC more accurately than a standard empirical model during seismic episodes, with XGBoost and genetic optimization at the top of the leaderboard. If future work extends this framework to more events, more regions and longer archives of GNSS data, the dream of reading warning signs from the ionosphere before the ground moves may edge closer to operational reality. For now, the sky above Japan has proven to be a measurable, modelable witness to the violence below.

Subject of Research: Machine learning analysis and prediction of ionospheric TEC anomalies associated with the 2024 Japan earthquakes

Article Title: Analysis of ionospheric TEC anomalies and prediction using XGBoost, GRU and RF algorithms associated with 2024 Japan Earthquakes (Mw > 7.0)

Article References: Mukesh, R., Kiruthiga, S., Rubashri, J., Fathima, S. R., Priyavarshini, P., Dass, S. C., Sahanaa, A. R. S., Safana, S., Karthick, S., & Ratnam, D. V. (2026). Analysis of ionospheric TEC anomalies and prediction using XGBoost, GRU and RF algorithms associated with 2024 Japan Earthquakes (Mw > 7.0). Earth Science Informatics, 19(11), Article 184. https://doi.org/10.1007/s12145-026-02220-9

Image Credits: AI Generated

DOI: 10.1007/s12145-026-02220-9

Keywords: ionosphere, total electron content, earthquake precursors, XGBoost, genetic algorithm, GRU, Random Forest, PSO, RMSProp, NeQuick, Japan earthquakes, lithosphere-atmosphere-ionosphere coupling