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<p>Land subsidence is a critical geological phenomenon affecting coastal deltas, megacities, and agricultural regions worldwide. It causes ground elevation loss and infrastructure damage, exacerbates coastal flood risks when combined with sea-level rise, and threatens ecological security and sustainable development. This study synthesizes 483 subsidence cases across 43 countries from 408 publications, combining quantitative analysis with case studies to assess spatial distribution, driving mechanisms, and evolving trends in monitoring and modeling technologies. Results show that human activities dominate subsidence hotspots, primarily located in coastal plains and river deltas (e.g., Shanghai, Jakarta, and Mexico City). Groundwater overexploitation (31.18%) contributes substantially more than other factors. Climatic factors do not directly explain subsidence variations but indirectly affect the balance between groundwater extraction and recharge. Composite aquifers (72.8% of all aquifers) are most susceptible to differential subsidence due to their thickness variability and structural complexity. Regarding monitoring technologies, InSAR has become the primary method for millimeter-scale subsidence monitoring, though its accuracy requires enhancement through integration with hydrogeological models and ground measurements. From the perspective of AI-geoscience integration, this review identifies a paradigm shift from single-method monitoring toward multimodal data fusion and intelligent modeling, including deep learning for deformation forecasting (LSTM), computer vision for InSAR quality enhancement and trigger recognition, and multimodal models for heterogeneous data analysis. Developing intelligent analytical frameworks that integrate multimodal AI with mechanistic models, along with promoting multi-source data sharing and interdisciplinary collaboration, represents a key pathway for mitigating subsidence risks. These findings provide a theoretical foundation for advancing geohazard research within the emerging paradigm of AI-driven geoscience.</p>

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A global systematic review of land subsidence drivers, technologies, and future directions from monitoring to intelligence

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月3日

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  • Yuqing Zhang,
  • Yongzhang Zhou,
  • Lujia Niu,
  • Yijia Guo

摘要

Land subsidence is a critical geological phenomenon affecting coastal deltas, megacities, and agricultural regions worldwide. It causes ground elevation loss and infrastructure damage, exacerbates coastal flood risks when combined with sea-level rise, and threatens ecological security and sustainable development. This study synthesizes 483 subsidence cases across 43 countries from 408 publications, combining quantitative analysis with case studies to assess spatial distribution, driving mechanisms, and evolving trends in monitoring and modeling technologies. Results show that human activities dominate subsidence hotspots, primarily located in coastal plains and river deltas (e.g., Shanghai, Jakarta, and Mexico City). Groundwater overexploitation (31.18%) contributes substantially more than other factors. Climatic factors do not directly explain subsidence variations but indirectly affect the balance between groundwater extraction and recharge. Composite aquifers (72.8% of all aquifers) are most susceptible to differential subsidence due to their thickness variability and structural complexity. Regarding monitoring technologies, InSAR has become the primary method for millimeter-scale subsidence monitoring, though its accuracy requires enhancement through integration with hydrogeological models and ground measurements. From the perspective of AI-geoscience integration, this review identifies a paradigm shift from single-method monitoring toward multimodal data fusion and intelligent modeling, including deep learning for deformation forecasting (LSTM), computer vision for InSAR quality enhancement and trigger recognition, and multimodal models for heterogeneous data analysis. Developing intelligent analytical frameworks that integrate multimodal AI with mechanistic models, along with promoting multi-source data sharing and interdisciplinary collaboration, represents a key pathway for mitigating subsidence risks. These findings provide a theoretical foundation for advancing geohazard research within the emerging paradigm of AI-driven geoscience.