摘 要
地表沉降是城市化进程中面临的重大地质问题，其对基础设施安全、生态环境稳定以及社会经济发展具有深远影响。随着全球城市化进程的加速，传统的人工监测方法已难以满足高精度、实时性和大范围监测的需求。为此，本研究聚焦于地表沉降监测系统的自动化与智能化发展，旨在通过融合多源数据和先进算法，构建一套高效、精准且适应性强的地表沉降监测体系。研究采用遥感技术、全球导航卫星系统（GNSS）、合成孔径雷达干涉测量（InSAR）以及物联网等多技术协同手段，结合机器学习和大数据分析方法，实现了从数据采集到处理分析的全流程自动化。同时，研究开发了一种基于深度学习的地表沉降预测模型，能够有效识别潜在风险区域并提供预警信息。实验结果表明，该系统在监测精度、响应速度及覆盖范围等方面均显著优于传统方法，尤其在复杂地质条件下的适应性表现突出。此外，研究还提出了一套标准化的数据管理与共享机制，为跨区域、跨部门的地表沉降监测提供了技术支持。本研究的主要创新点在于将智能化技术与地表沉降监测深度融合，不仅提升了监测效率和准确性，还为相关决策提供了科学依据，为未来智慧城市建设和地质灾害防控奠定了坚实基础。
关键词：地表沉降监测；智能化技术；多源数据融合
ABSTRACT
Ground surface subsidence represents a significant geological challenge in the process of urbanization, with profound implications for infrastructure safety, ecological stability, and socio-economic development. As global urbanization accelerates, traditional manual monitoring methods struggle to meet the demands of high precision, real-time responsiveness, and large-scale coverage. This study focuses on the automation and intelligence development of ground surface subsidence monitoring systems, aiming to construct an efficient, accurate, and adaptable monitoring fr amework by integrating multi-source data and advanced algorithms. The research employs a combination of remote sensing technology, Global Navigation Satellite Systems (GNSS), Interferometric Synthetic Aperture Radar (InSAR), and the Internet of Things (IoT), coupled with machine learning and big data analytics, to achieve full-process automation from data acquisition to processing and analysis. Furthermore, a deep-learning-based prediction model for ground surface subsidence has been developed, capable of effectively identifying potential risk areas and providing early warning information. Experimental results demonstrate that this system significantly outperforms traditional methods in terms of monitoring accuracy, response speed, and coverage range, particularly showcasing superior adaptability under complex geological conditions. Additionally, the study proposes a standardized mechanism for data management and sharing, offering technical support for cross-regional and inter-departmental ground surface subsidence monitoring. The primary innovation of this research lies in the deep integration of intelligent technologies with ground surface subsidence monitoring, which not only enhances monitoring efficiency and accuracy but also provides scientific evidence for relevant decision-making, laying a solid foundation for the construction of future smart cities and the prevention and control of geological disasters.
Keywords: Surface Subsidence Monitoring; Intelligent Technology; Multi-Source Data Fusion
目 录
摘 要 I
ABSTRACT II
第1章 绪论 1
1.1 地表沉降监测系统研究背景与意义 1
1.2 自动化与智能化发展现状分析 1
1.3 研究方法与技术路线设计 2
第2章 地表沉降监测自动化关键技术研究 3
2.1 自动化监测数据采集技术分析 3
2.2 数据传输与存储的高效实现方式 3
2.3 自动化监测系统的误差控制策略 4
2.4 关键技术在实际场景中的应用案例 4
第3章 智能化算法在地表沉降监测中的应用 6
3.1 机器学习算法在沉降预测中的作用 6
3.2 数据挖掘技术对监测数据的深度解析 6
3.3 智能化算法的优化与性能评估 7
3.4 实时预警模型的设计与实现 7
第4章 地表沉降监测系统集成与实践探索 9
4.1 监测系统整体架构设计与实施 9
4.2 自动化与智能化功能的协同优化 9
4.3 系统可靠性与稳定性测试分析 10
4.4 实际工程应用中的问题与改进措施 10
结论 12
参考文献 13
致 谢 14
