Ionospheric and Atmospheric Precursors to Earthquakes: A Critical Review of Multi-Parametric Observations, Coupling Mechanisms, and Machine Learning Applications
DOI:
https://doi.org/10.70882/josrar.2026.v3i5.290Keywords:
Lithosphere-Atmosphere-Ionosphere Coupling, Earthquake Precursors, Ionospheric Anomalies, Total Electron Content, Machine LearningAbstract
The search for reliable pre-earthquake signals in the upper atmosphere has produced a large and heterogeneous body of literature over the past two decades, much of it organized around the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) hypothesis. This narrative-critical review synthesizes studies published between 2020 and 2026, identified through Scopus, Web of Science, Google Scholar and journal-archive searches and screened against stated eligibility and evidence-appraisal criteria, on ionospheric and atmospheric precursors to large earthquakes, covering Global Navigation Satellite System (GNSS)-derived Total Electron Content (TEC) anomalies, sub-ionospheric Very Low Frequency (VLF) and High Frequency (HF) radio propagation disturbances, and thermal atmospheric signatures. It further examines the proposed mechanical (acoustic-gravity wave), geochemical (radon-driven), and electromagnetic channels invoked to explain lithosphere-to-ionosphere energy transfer, and considers how solar and geomagnetic activity confound or mimic seismogenic signals. Beyond summarizing individual case studies, this review evaluates the field's methodological foundations: the near-universal reliance on retrospective, single-event case studies; the absence of standardized anomaly definitions and false-positive/false-negative statistics; and the growing but still under-validated use of deep machine learning for anomaly discrimination. We find that pre-seismic ionospheric perturbations are reported using several independent techniques, but the evidence is heterogeneous and rarely controlled, and causal attribution to seismogenic processes rather than coincidental space weather variability remains unresolved for the majority of reported cases. The present evidence does not support operational earthquake forecasting; we recommend prospective, statistically rigorous, and physically constrained research designs.
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Copyright (c) 2026 Racheal Foluke Oloruntola, Oludare Olukayode Babalola, Ganiyu Akanji Olaniyi, George Atilade Àlàgbé, Hameed Olalekan Bakare, Adedamola Julius Agbelemoge (Author)
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