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<p>Subsurface fluids govern the distribution of strategic resources, genesis of geohazards, and global biogeochemical cycles. However, conventional single-parameter and short-term observations are inadequate for capturing the multi-scale nonlinear dynamics of these systems, leaving critical gaps in mechanistic understanding and prediction. To address this challenge, this review proposes an integrated “Space-Air-Ground-Borehole” stereoscopic monitoring framework. Within this framework, satellite gravimetry and InSAR provide basin-scale spatial coverage, airborne platforms bridge regional observational gaps, ground-based networks deliver high-precision in-situ data, and borehole monitoring captures deep fluid dynamics inaccessible to surface methods. The synergy among these four tiers, enhanced by multi-parameter fusion and Artificial Intelligence (AI)-driven multi-process modelling, transforms fragmented, scale-limited datasets into a coherent, predictive understanding of subsurface fluid behavior. Through a unified “Technology, Parameter and Application” linkage, integrated, long-term multi-parameter monitoring can support future advances in groundwater resources management, geothermal reservoir engineering, earthquake hydrochemical precursor detection, Carbon Sequestration, and deep-fluid geoscientific investigations. The proposed framework bridges the long-standing scale gap between point-based borehole measurements and remote-sensing observations, thereby advancing subsurface fluid science from passive, isolated monitoring toward integrated, intelligent, and predictive capability, and providing both a conceptual reference and an actionable roadmap to support global water security, carbon-neutrality goals, and disaster mitigation.</p>

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Long-Term Multi-Parameter Monitoring of Subsurface Fluids: Advances, Applications, and Future Prospects

  • 期刊论文

  • 发表日期：

    2026年6月20日

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  • Sihang Han,
  • Yifei Xing,
  • Guangzheng Jiang,
  • Chuanzhi Liao,
  • Shu Hu,
  • Zhenyu Li,
  • Bohan Zeng

摘要

Subsurface fluids govern the distribution of strategic resources, genesis of geohazards, and global biogeochemical cycles. However, conventional single-parameter and short-term observations are inadequate for capturing the multi-scale nonlinear dynamics of these systems, leaving critical gaps in mechanistic understanding and prediction. To address this challenge, this review proposes an integrated “Space-Air-Ground-Borehole” stereoscopic monitoring framework. Within this framework, satellite gravimetry and InSAR provide basin-scale spatial coverage, airborne platforms bridge regional observational gaps, ground-based networks deliver high-precision in-situ data, and borehole monitoring captures deep fluid dynamics inaccessible to surface methods. The synergy among these four tiers, enhanced by multi-parameter fusion and Artificial Intelligence (AI)-driven multi-process modelling, transforms fragmented, scale-limited datasets into a coherent, predictive understanding of subsurface fluid behavior. Through a unified “Technology, Parameter and Application” linkage, integrated, long-term multi-parameter monitoring can support future advances in groundwater resources management, geothermal reservoir engineering, earthquake hydrochemical precursor detection, Carbon Sequestration, and deep-fluid geoscientific investigations. The proposed framework bridges the long-standing scale gap between point-based borehole measurements and remote-sensing observations, thereby advancing subsurface fluid science from passive, isolated monitoring toward integrated, intelligent, and predictive capability, and providing both a conceptual reference and an actionable roadmap to support global water security, carbon-neutrality goals, and disaster mitigation.