摘 要
地表沉降是城市化进程中面临的重要地质问题，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。为有效应对这一挑战，本研究以地表沉降监测数据处理与沉降预测模型为核心，旨在构建一套高效、精准的分析方法体系。研究基于多源监测数据（如GPS、InSAR和水准测量），通过数据融合技术提高数据质量与空间分辨率，并引入改进的滤波算法以消除噪声干扰，从而实现对沉降过程的精确描述。在此基础上，提出了一种结合深度学习与传统时间序列分析的混合预测模型，该模型能够充分挖掘沉降数据中的非线性特征与长期趋势。实验结果表明，所提方法在多个典型沉降区域的应用中表现出优异的预测精度和稳定性，相较于传统方法平均误差降低约20%。此外，本研究还开发了基于地理信息系统的可视化平台，为沉降监测与预警提供了直观的技术支持。研究的主要创新点在于将深度学习技术与地质学知识深度融合，同时兼顾模型的可解释性和实用性，为地表沉降防控提供了科学依据和技术支撑，具有重要的理论价值和实际应用前景。
关键词：地表沉降；深度学习；混合预测模型
ABSTRACT
Surface subsidence is a critical geological issue faced during the process of urbanization, with profound implications for infrastructure safety, ecological stability, and socio-economic development. To effectively address this challenge, this study focuses on the processing of surface subsidence monitoring data and the development of a subsidence prediction model, aiming to construct an efficient and accurate analytical methodology system. Based on multi-source monitoring data, such as GPS, InSAR, and leveling measurements, data fusion techniques are employed to enhance data quality and spatial resolution, while an improved filtering algorithm is introduced to eliminate noise interference, thereby achieving precise desc riptions of the subsidence process. Furthermore, a hybrid prediction model combining deep learning and traditional time-series analysis is proposed, which can fully exploit the nonlinear characteristics and long-term trends embedded in subsidence data. Experimental results demonstrate that the proposed method exhibits superior prediction accuracy and stability when applied to multiple typical subsidence regions, reducing average errors by approximately 20% compared to conventional methods. Additionally, a visualization platform based on geographic information systems (GIS) has been developed, providing intuitive technical support for subsidence monitoring and early warning. The primary innovation of this study lies in the deep integration of deep learning technology with geological knowledge, balancing model interpretability and practicality, thus offering scientific evidence and technical support for the prevention and control of surface subsidence. This research holds significant theoretical value and practical application potential.
Keywords: Ground Surface Subsidence; Deep Learning; Hybrid Prediction Model
目 录
摘 要 I
ABSTRACT II
第1章 绪论 1
1.1 地表沉降监测的研究背景与意义 1
1.2 国内外地表沉降研究现状分析 1
1.3 本文研究方法与技术路线 2
第2章 地表沉降监测数据处理方法研究 3
2.1 数据预处理的关键步骤 3
2.2 异常值检测与修正方法 3
2.3 数据平滑与滤波技术应用 4
2.4 数据融合在沉降监测中的作用 4
第3章 沉降预测模型构建与优化研究 6
3.1 常见沉降预测模型概述 6
3.2 时间序列模型在沉降预测中的应用 6
3.3 机器学习模型的构建与验证 7
3.4 模型优化与参数调优策略 7
第4章 沉降预测模型的实际应用与效果评估 9
4.1 实际案例中数据处理流程 9
4.2 预测模型在典型区域的应用 9
4.3 模型预测精度评估方法 10
4.4 模型改进方向与未来展望 10
结论 12
参考文献 13
致 谢 14
