水安全问题是关系中华民族永续发展的战略问题。城市雨洪、矿井水等既是灾害又是难得的非常规水资源，对其进行深部地质贮存，兼具水资源战略储备和减灾防灾的双重效益。然而，深贮过程中目标层选择尚不规范、应力场—渗流场—水化学场—微生物场等多场耦合机理不明导致增渗扩容不可控、深贮后目标层地下水环境演变规律不清、地质环境效应评估方法缺失，这制约了水资源深部地质贮存技术的规模化应用。围绕水资源深部地质贮存的地质环境效应核心科学问题，构建了涵盖水文地质精细评价、目标层智能优选、地质环境演化、致裂扩容增储、微生物调控及多场耦合的完整科学构想框架。以内地矿井水和粤港澳大湾区城市雨洪深贮为典型对象，通过定量评价深部砂岩、石灰岩等目标层的水文地质结构和赋存地质环境，融合机器学习与多源地质数据，建立环境友好型深贮目标层的智能优选方法；开展水—岩—微生物耦合模拟和野外深贮试验等，构建基于AI 高解析模型与数字孪生的温度场—应力场—渗流场—水化学场—微生物场耦合数值模型，揭示水资源深贮水岩作用机理、微生物群落对渗流通道开闭作用的机理和赋存地质环境演变规律，构建深贮地质环境效应评价方法与差异化风险识别、预警与防控对策。预期成果将为破解水资源地质贮存的技术瓶颈、推动非常规水资源安全高效利用提供科学依据。

Abstract：Water security is a strategic issue for the sustainable development of the Chinese nation. Urban stormwater and mine water serve not only as hazards but also as valuable unconventional water resources. Their deep geological storage presents dual benefits of strategic water resource reserves and disaster mitigation. Nevertheless, the large-scale application of this technology is hindered by the absence of standardized criteria for target layer selection, unclear coupling mechanisms of the stress-seepage-hydrochemical-microbial fields leading to uncontrollable permeability enhancement and capacity expansion, ambiguous evolutionary patterns of groundwater environments in target layers following storage, and a lack of methodologies for assessing geoenvironmental effects. This study addresses the core scientific issue of geo-environmental effects of deep geological water storage by constructing a comprehensive scientific framework that encompasses refined hydrogeological evaluation, intelligent target layer optimization, geological environment evolution, fracturinginduced capacity expansion, microbial regulation, and multi-field coupling. Focusing on mine water from inland mining areas and urban stormwater in the Guangdong-Hong Kong-Macao Greater Bay Area, the framework quantitatively evaluates the hydrogeological structure and host geological environment of deep target layers such as sandstone and limestone. By integrating machine learning with multi-source geological data, it establishes an environmentally friendly intelligent optimization method for selecting deep storage target layers. Through waterrock- microbe coupling simulations and field deep storage experiments, a coupled numerical model of the thermalstress- seepage-hydrochemical-microbial fields is developed using AI high-resolution modeling and digital twin technologies. This model reveals the mechanisms of water–rock interactions during deep water storage, elucidates the role of microbial communities in regulating seepage channel opening and closure, and delineates the evolutionary patterns of the host geological environment. Furthermore, an assessment method for geoenvironmental effects of deep storage is established, accompanied by differentiated risk identification, early warning, and prevention and control strategies. The expected results will provide a scientific basis for resolving technical bottlenecks in geological water storage and advancing the safe and efficient utilization of unconventional water resources.