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1.武汉大学测绘学院,湖北 武汉,430079
2.武汉大学地球空间环境与大地测量教育部重点实验室,湖北 武汉,430079
3.自然资源部地球物理大地测量重点实验室,湖北 武汉,430079
Received:25 January 2026,
Published:05 August 2026
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许才军,尹汇民.同震电离层扰动:观测、机理研究与应用新进展[J].武汉大学学报(信息科学版),2026,51(8):1562-1583.
XU Caijun,YIN Huimin.Co-seismic Ionospheric Disturbances: Recent Advances in Observations, Mechanisms, and Applications[J].Geomatics and Information Science of Wuhan University,2026,51(8):1562-1583.
许才军,尹汇民.同震电离层扰动:观测、机理研究与应用新进展[J].武汉大学学报(信息科学版),2026,51(8):1562-1583. DOI:10.13203/j.whugis20250325
XU Caijun,YIN Huimin.Co-seismic Ionospheric Disturbances: Recent Advances in Observations, Mechanisms, and Applications[J].Geomatics and Information Science of Wuhan University,2026,51(8):1562-1583. DOI:10.13203/j.whugis20250325
同震电离层扰动(co-seismic ionospheric disturbances
CIDs)不仅是探索固体地球与近地空间耦合动力学的独特物理窗口,也为地震与海啸等自然灾害的早期预警提供了前沿解决方案。该领域的研究进展已清晰地勾勒出一条从现象观测到应用实践的发展脉络。以全球导航卫星系统总电子含量测量为核心的观测技术演进,揭示了CIDs的关键物理特征,包括N型波、传播各向异性及多模态现象。这些现象的背后,是以声学-重力波为核心的物理机制及其日益精细的数值建模与仿真技术。其两大核心应用潜力在于:一是通过反演扰动特征来快速估算地震震源参数;二是在海啸早期预警中扮演“空中信使”的关键角色。一个显著的趋势是,以混合深度学习架构和物理信息机器学习为代表的数据驱动方法,正为CIDs的自动、近实时探测带来范式转变。然而,从科学演示走向业务化应用的道路仍面临诸多挑战,包括如何在强背景噪声中可靠辨识信号、提升模型的物理保真度,以及构建全球协同的观测与验证体系。未来的突破方向在于构建一个将多技术观测、高保真度物理模型与人工智能深度融合的综合预警体系。CIDs研究的深化必将为地球系统科学的发展和自然灾害应对能力的提升做出重要贡献。
Co-seismic ionospheric disturbances (CIDs) not only provide a unique physical window into the coupling dynamics between the solid Earth and near-Earth space but also offer a cutting-edge approach for the early warning of natural hazards such as earthquakes and tsunamis. Progress in this field has traced a clear trajectory from phenomenological observation to practical application. The evolution of observational techniques
centered on global navigation satellite system total electron content measurements
has been pivotal in revealing key physical characteristics of CIDs
including N-shaped waves
propagation anisotropy
and multi-modal phenomena. Underpinning these observations are physical mechanisms dominated by acoustic-gravity waves
which are increasingly validated through refined numerical modeling and simulation. The two primary application potentials of CIDs lie in:(1) the rapid estimation of seismic source parameters by inverting disturbance features
and (2) their role as a critical “atmospheric messenger” in tsunami early warning systems. A prominent trend is the paradigm shift driven by data-driven methods—particularly hybrid deep learning architectures and physics-informed machine learning — in the automated and near-real-time detection of CIDs. However
the path from scientific demonstration to operational application faces several challenges
including the reliable identification of signals amidst strong background noise
enhancement of model physical fidelity
and the establishment of a globally coordinated observation and verification framework. Future breakthroughs are expected to come from an integrated warning system that deeply fuses multi-technique observations
high-fidelity physical models
and artificial intelligence. Continued advances in CIDs research are poised to make significant contributions to both Earth system science and our capacity to respond to natural disasters.
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