摘 要
矿山开采活动引发的地表形变问题对生态环境和工程安全构成重大威胁，因此开展地表形变监测技术的研究具有重要的理论意义和实践价值。本研究以矿山开采为背景，旨在探索高精度、高效能的地表形变监测方法，并评估其在实际工程中的应用效果。研究综合运用了合成孔径雷达干涉测量（InSAR）、全球导航卫星系统（GNSS）以及地面测量等多源数据融合技术，通过构建时空分析模型，实现了对矿山开采区域地表形变的动态监测与精准量化。结果表明，InSAR技术能够有效捕捉大范围、微小尺度的地表形变特征，而GNSS则在局部高精度监测中表现出显著优势，二者结合可弥补单一技术的局限性，从而提供更为全面的地表形变信息。此外，本研究提出了一种基于机器学习的地表形变预测模型，该模型能够根据历史监测数据准确预测未来形变趋势，为矿山开采规划及灾害预警提供了科学依据。研究表明，多技术协同监测方案不仅提高了地表形变监测的精度和效率，还为矿山开采过程中的环境影响评估和安全管理提供了新的技术手段。本研究的主要创新点在于首次将多源遥感数据与机器学习算法相结合，形成了适用于复杂矿山环境的地表形变监测与预测体系，为相关领域的研究和技术应用奠定了坚实基础。
关键词：地表形变监测；InSAR技术；GNSS技术
ABSTRACT
Surface deformation induced by mining activities poses significant threats to ecological environments and engineering safety, making the study of surface deformation monitoring technologies theoretically and practically important. This research, set against the backdrop of mining operations, aims to explore high-precision and high-efficiency methods for monitoring surface deformation and evaluate their application effects in real-world projects. By integrating multi-source data fusion techniques such as Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite System (GNSS), and terrestrial surveying, a spatiotemporal analysis model was constructed to achieve dynamic monitoring and precise quantification of surface deformation in mining areas. The results indicate that InSAR technology effectively captures large-scale, subtle deformation characteristics, while GNSS demonstrates significant advantages in localized high-precision monitoring. The combination of these two techniques compensates for the limitations of single technologies, providing more comprehensive surface deformation information. Additionally, this study proposes a machine-learning-based surface deformation prediction model capable of accurately forecasting future deformation trends based on historical monitoring data, offering scientific support for mining planning and disaster early warning. The research shows that a multi-technology collaborative monitoring scheme not only enhances the accuracy and efficiency of surface deformation monitoring but also provides new technical means for environmental impact assessment and safety management during mining operations. A major innovation of this study lies in its pioneering integration of multi-source remote sensing data with machine learning algorithms, forming a monitoring and prediction system suitable for complex mining environments, which lays a solid foundation for research and technological applications in related fields.
Keywords: Surface Deformation Monitoring; InSAR Technology; Gps Technology
目 录
摘 要 I
ABSTRACT II
第1章 绪论 1
1.1 矿山开采与地表形变监测的背景 1
1.2 地表形变监测技术的研究意义 1
1.3 国内外研究现状分析 2
1.4 本文研究方法概述 2
第2章 地表形变监测技术原理与方法 3
2.1 地表形变监测的基本概念 3
2.2 常用地表形变监测技术分类 3
2.3 遥感技术在形变监测中的应用 4
2.4 GPS技术的地表形变监测功能 4
2.5 InSAR技术的特点与优势 5
第3章 矿山开采中的地表形变特征分析 6
3.1 矿山开采对地表形变的影响机制 6
3.2 不同类型矿山的地表形变表现 6
3.3 地质条件对形变特征的影响 7
3.4 时间序列下的地表形变规律 7
3.5 形变数据的采集与处理方法 8
第4章 地表形变监测技术在矿山安全中的应用 9
4.1 监测技术在矿山灾害预警中的作用 9
4.2 数据驱动的矿山开采风险评估 9
4.3 实时监测系统的构建与优化 10
4.4 典型案例分析与经验总结 10
4.5 提高监测效率的技术路径 11
结论 12
参考文献 13
致 谢 14
