Are Ionospheric Disturbances Spatiotemporally Invariant Earthquake Precursors? A Multi-Decadal 100-Station Study

Earthquake prediction remains one of the central unsolved problems in geophysics, and ionospheric variability offers a promising yet debated window into the earthquake preparation process through lithosphere–atmosphere–ionosphere coupling. Progress has been hindered by methodological limitations in prior studies, including the use of inappropriate performance metrics for highly imbalanced seismic data, the reliance on geographically and temporally narrow data, and inclusion of inherent spatial or temporal features that artificially inflate model performance while preventing the discovery of genuine ionospheric precursors. To address these challenges, we introduce a global, temporally validated machine learning framework grounded in thirty-eight years of ionospheric observations from more than a hundred ionosonde stations. We eliminate lookahead bias through strict temporal partitioning, prevent overlapping precursor windows across samples to eliminate autocorrelation artifacts and apply sophisticated feature selection to exclude spatial and temporal identifiers, enabling prevention of data leakage and coincidence effects. We investigate whether spatiotemporally invariant ionospheric precursors exist across diverse seismic regions, addressing the field’s reliance on geographically isolated case studies. Cross-regional validation shows that our models yield modest classification skill above chance levels, with our best-performing model achieving a weighted F1 score of 71% though performance exhibits pronounced sensitivity to temporal validation configuration, suggesting these results represent an upper bound on operational accuracy. While multimodal fusion with complementary precursor channels could possibly improve performance, our focus remains on establishing whether ionospheric observations alone contain learnable, region-independent seismic signatures. These findings suggest that ionospheric precursors, if they exist as universal phenomena, exhibit weaker cross-regional consistency than previously reported in case studies, raising questions about their standalone utility for earthquake prediction while indicating potential value as one component within multimodal observation systems.