Lithosphere–Atmosphere–Ionosphere Coupling: Opportunities and challenges from phenomenon observation to physical prediction

Natural hazards (NHs), such as earthquakes and volcanic eruptions, generate disturbances that propagate through the atmosphere and ionosphere, leaving distinct signatures. The study of these ionospheric signatures has evolved into a promising interdisciplinary field. Over the past two decades, advancements in remote sensing technologies, particularly the GNSS, have significantly expanded our understanding of lithosphere–atmosphere–ionosphere coupling (LAIC) mechanisms. Recent studies indicate that, for some large coseismic and coeruptive events, ionospheric signatures may provide complementary constraints on source location and physical properties, suggesting their potential value for future near-real-time (NRT) monitoring and hazard assessment. Despite remarkable progress in this field, a significant scientific gap remains between the observation of postevent disturbances and the achievement of reliable prehazard forecasting. The core challenges lie in identifying weak signals of lithospheric activity amid strong background noise and establishing a unified theoretical framework. This article systematically reviews the development, key methodologies, and cutting-edge progress of LAIC research and proposes an integrated analytical framework of “technological evolution–signal separation–physical mechanisms.” This review demonstrates that coseismic ionospheric disturbances (CIDs), volcanic Lamb wave effects, and other coeruptive ionospheric disturbances now have a clearer physical interpretation and are amenable to quantitative analysis. However, for the much-debated preseismic ionospheric precursors, the reliability of signal identification and the unification of physical mechanisms remain weak links in current research. Ultimately, it is concluded that, for LAIC research to move from phenomenological correlation to physical prediction, it must address three core challenges: signal attribution, observational systems, and physical theory. Future breakthroughs will depend heavily on the synergistic development of physics-informed artificial intelligence (AI) algorithms, a trans-spheric coordinated observation system, and a unified, interpretable physical model.