基于雷达影像的矿区地面沉降监测系统的研究与实现

发布时间：2018-03-02 17:11

本文选题：矿区地面沉降　切入点：D-InSAR　出处：《中国矿业大学》2017年硕士论文　论文类型：学位论文

【摘要】：近些年来,由于对石油、天然气、固体矿产、地下水等资源的过度开采直接导致了全球范围的地面沉降。基于雷达影像数据的矿区地面沉降分析与地面沉降监测系统的研究为合理开采地下矿产资源、控制地面沉降提供决策依据,也为矿区生态系统恢复与保护提供理论和技术支持,对矿区地质环境的治理具有重要的意义。本文在研究了D-InSAR技术与SBAS-InSAR技术获取地表微小形变的原理与流程的基础上,基于ENVI、SARscape与ArcGIS Engine混合编程技术,使用IDL与C#语言开发了矿区地面沉降监测系统,实现了多重功能:(1)数据读取、图层管理、地图浏览、空间分析等GIS基本功能;(2)多视、图像滤波、地理编码等SAR数据预处理功能;(3)基于D-InSAR技术与SBAS-InSAR技术的矿区地面沉降监测分析功能。详细阐述了系统需求分析与总体设计,分析了每个模块的设计思路、技术流程与核心代码,实现了基于SAR数据的地面沉降监测系统的主要功能模块。系统调试通过后,选择青海省西宁市大通矿区为实验研究区,结合星载TerraSAR-X卫星影像,对系统各功能模块进行测试验证。针对系统数据预处理模块的验证,对原始的SSC雷达影像数据进行多视、滤波与地理编码等预处理操作,分析了不同的参数与滤波方法对SAR影像预处理结果的影响。针对系统地面沉降监测模块的验证,本文分别使用基于D-InSAR技术与SBAS-InSAR技术分析与研究了研究区地面沉降情况,提取了研究区20131001-20131103、20131103-20131206、20131206-20140130、20140130-20140428时间段内的地面沉降沉降结果图与地面沉降时间序列图,结果表明大通矿区存在明显的地面沉降现象。矿区内共出现3个沉降中心,分别为小煤洞村、大煤洞村与元树尔村沉降区。研究区总沉降面积在4个时间段内分别为3.811km2、3.748km2、4.949km2与2.311km2,最大平均中心沉降量分别为45.7mm、48.9mm、56.7mm与45.8mm,并且沉降区域有连成整体的趋势。研究区内沉降速率最高达到156mm/year,沉降值最高达到91mm,在沉降区内选取10个不同采样点作为研究对象,发现其沉降趋势基本保持一致。
[Abstract]:In recent years, for oil, natural gas, solid minerals, The over-exploitation of groundwater and other resources directly leads to the global land subsidence. The research of ground subsidence analysis and land subsidence monitoring system based on radar image data is the rational exploitation of underground mineral resources. Control of land subsidence provides a basis for decision-making, and also provides theoretical and technical support for ecosystem restoration and protection in mining areas. This paper studies the principle and flow of D-InSAR technology and SBAS-InSAR technology to obtain small deformation of the ground surface, and based on the mixed programming technology of ENVISARscape and ArcGIS Engine, it is of great significance to control the geological environment of mining area. The land subsidence monitoring system in mining area is developed by using IDL and C # language. The basic functions of GIS, such as reading data, layer management, map browsing, spatial analysis and so on, are realized, such as multi-function: 1) data reading, image filtering, etc. Based on D-InSAR technology and SBAS-InSAR technology, the ground subsidence monitoring and analysis function of mining area is introduced. The system requirement analysis and overall design are described in detail, and the design idea, technical flow and core code of each module are analyzed. The main function module of the land subsidence monitoring system based on SAR data is realized. After the system is debugged and passed, the Datong mining area of Xining City in Qinghai Province is selected as the experimental research area, and combined with the satellite image of spaceborne TerraSAR-X. According to the verification of the system data preprocessing module, the original SSC radar image data are preprocessed with multi-view, filtering and geographic coding, etc. This paper analyzes the influence of different parameters and filtering methods on the pre-processing results of SAR images. According to the verification of the system land subsidence monitoring module, this paper analyzes and studies the ground subsidence in the study area based on D-InSAR technology and SBAS-InSAR technology, respectively. The results and time series diagrams of land subsidence in 20131001-20131103 / 20131206 / 20131206-20140130/ 20140130-20140428 have been extracted. The results show that there are obvious land subsidence phenomena in Datong mining area. There are three subsidence centers in the mining area, one is small coal cave village, the other is small coal cave village. The total subsidence area of the study area is 3.811km2 3.748km2 4.949km2 and 2.311km2, respectively, and the maximum average central subsidence is 45.7mm-1 / 48.9mm / 56.7mm and 45.8mm respectively. The highest velocity is 156mm / year, and the highest sedimentation value is 91mm. Ten different sampling points are selected as the research objects in the settlement area. It is found that the settlement trend is basically consistent.
【学位授予单位】：中国矿业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TD76;P642.26

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