区域地面沉降研究进展与展望

朱琳; 宫辉力; 李小娟; 周超凡; 叶淼; 王海刚; 张可; 韩苗苗

doi:10.16030/j.cnki.issn.1000-3665.202212043

区域地面沉降研究进展与展望

1.
首都师范大学资源环境与旅游学院，北京　100048

2.
水资源安全北京实验室，北京　100048

3.
城市环境过程与数字模拟国家重点实验室培育基地，北京　100048

4.
地面沉降机理与防控教育部重点实验室，北京　100048

5.
河北沧州平原区地下水与地面沉降国家野外科学观测研究站，河北沧州　061000

6.
中国地质环境监测院（自然资源部地质灾害技术指导中心），北京　100081

基金项目: 国家自然科学基金项目（42271082；42271487；42201081）

详细信息

作者简介: 朱琳（1980—），女，博士，教授，主要从事水循环、地质灾害过程模拟和风险评价。E-mail：lin.zhu@cnu.edu.cn

通讯作者: 宫辉力（1956—），男，博士，教授，主要从事地理信息系统和遥感、信息水文地质的教学与交叉研究。E-mail：4039@cnu.edu.cn

中图分类号: P642.26

收稿日期: 2022-12-29

修回日期: 2023-06-09

录用日期: 2023-07-19

刊出日期: 2024-07-15

Research progress and prospect of land subsidence

1.
College of Resource Environment and Tourism, Capital Normal University, Beijing　100048, China

2.
Beijing Laboratory of Water Resources Security, Beijing　100048, China

3.
Laboratory Cultivation Base of Environment Process and Digital Simulation, Beijing,　100048, China

4.
Key Laboratory of Mechanism, Prevention and Mitigation of Land Subsidence, MOE, Beijing　100048, China

5.
Hebei Cangzhou Groundwater and Land Subsidence National Observation and Research Station, Cangzhou, Hebei　061000, China

6.
China Institute of Geo-Environment Monitoring（Guide Center of Prevention Technology for Geo-hazards，MNR），Beijing　100081，China

More Information

Corresponding author: GONG Huili, 4039@cnu.edu.cn

Received Date: 29 December 2022

Revised Date: 09 June 2023

Accepted Date: 19 July 2023

Publish Date: 15 July 2024

摘要

摘要:
地面沉降是全球性重大地质环境问题，区域差异性地面沉降已经对城市基础设施、线性轨道交通和地下空间开发利用形成重大威胁，制约着经济和社会的可持续发展。围绕区域地表形变信息获取、地面沉降演变机制和模拟方面的研究进展进行系统阐述，重点分析了InSAR形变监测和多源形变数据融合的区域地表形变信息获取技术，基于室内土工试验数据和长时序观测数据，利用相关分析、统计分析和机器学习等方法分析地面沉降演变与各影响因素的关系。在此基础上，探讨了地下水流场—土体变形模型、数理统计模型和机器学习模型等地面沉降过程模拟模型的优缺点。发现多源形变数据融合能够提高区域地表形变信息的时空分辨率，地质构造、地层岩性、地下水开采和动静载荷等影响因素的差异性是造成地面沉降差异性演化的机制，地面沉降数学模型的计算效率与可解释性难以兼顾是当前沉降模拟存在的主要问题。据文献检索，当前研究主要关注地下水超量开采引发的地面沉降。进而提出未来区域地面沉降研究方向：在气候变化叠加新水情、新数据背景下，充分融合遥感大数据与野外观测站实测小数据集，耦合基于物理机制的模型和机器学习模型，优化集成InSAR、GeoAI、云平台等技术的最新进展，揭示全球气候变化和人类活动综合作用下的区域地面沉降演变机制，为区域地面沉降防控和城市安全提供技术支撑。
- 地面沉降 /
- 合成孔径雷达干涉测量 /
- 演化机制 /
- 机器学习 /
- 耦合模型
Abstract:
Land subsidence is a worldwide geological hazard. Differential land subsidence has posed the major threat to urban infrastructure, linear rail transit and underground space development and utilization, and also restricted the sustainable development of the economy and society. This paper systematically elaborates on the research progresses on land deformation acquisition, evolution mechanism and simulation of land subsidence, and focuses on the land deformation acquisition technology based on InSAR monitoring and multi-source deformation data fusion, as well as the correlation analysis, statistical analysis, machine learning and other methods to analyze the relationship between the evolution of land subsidence and various influencing factors based on geotechnical experiment and long time series observation data. On this basis, the advantages and disadvantages of land subsidence simulation models such as groundwater flow field-land deformation model, mathematical statistical model and machine learning model are explored. It is found that multi-source deformation data fusion can improve the spatiotemporal resolution of land deformation. The differences in geological structure, lithology, groundwater exploitation, and dynamic and static loads are factors contributing to the differential evolution of land subsidence. The difficulty in balancing the computational efficiency and interpretability of mathematical models for land subsidence is the main problem in simulation. According to the literature review, the current researches mainly focus on land subsidence caused by groundwater over-exploitation. This paper further proposes the future research directions for land subsidence, under the background of climate change, new hydrological condition and dataset, and based on the fusion of data through remote sensing and field observations, integrating the latest progress of InSAR, GeoAI, cloud platform and other technologies to reveal the evolution mechanism of land subsidence considering the climate change and anthropic activities and provide technical support for regional land subsidence prevention and urban safety.
- land subsidence /
- InSAR /
- evolutionary mechanism /
- machine learning /
- coupling model

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区域地面沉降研究进展与展望

作者简介: 朱琳（1980—），女，博士，教授，主要从事水循环、地质灾害过程模拟和风险评价。E-mail：lin.zhu@cnu.edu.cn

通讯作者: 宫辉力（1956—），男，博士，教授，主要从事地理信息系统和遥感、信息水文地质的教学与交叉研究。E-mail：4039@cnu.edu.cn

Research progress and prospect of land subsidence

Corresponding author: GONG Huili, 4039@cnu.edu.cn

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