多时相SAR振幅信息的大量级沉陷区识别研究

    Large scale subsidence area identification based on multi temporal SAR amplitude information

    • 摘要: 针对传统雷达差分干涉测量技术(DInSAR)无法利用相位信息识别矿区大量级沉降区域地表活动信息的问题, 本文采用时序振幅分析方法,对16景Sentinel-1A的振幅数据进行多时相处理,提取了2017年5月13日至2018年2月25日开采沉陷区地表后向散射系数。通过建立研究区后向散射系数分析模型,分析了开采活动过程中地表后向散射系数时序变化特征,发现地表后向散射系数由工作面外边缘至工作面中心逐渐增大,在工作面中心区域变化剧烈;通过对比分析工作面走向和倾向地表特征观测点的沉降速率与后向散射系数的关系,发现两者在开采沉陷过程中平均相关系数r大于0.8,具有较强的相关性。实验结果表明,利用沉陷区地表后向散射系数能较好地分析地表动态沉降速率规律,是识别大量级沉降区域地表活动剧烈程度的指标,为矿区地表变形提供了新的分析手段,具有良好的应用前景。

       

      Abstract: Aiming at the problem that the traditional radar differential interferometry (DInSAR) technology can not use the phase information to identify the surface activity information of large-scale subsidence area in mining area.In this paper, the time series amplitude analysis method is used to process the amplitude data of 16 sentinel-1A scenes, and the surface backscattering coefficient of mining subsidence area from May 13, 2017 to February 25, 2018 is extracted.The time series variation characteristics of surface backscattering coefficient in the process of mining activities are analyzed by establishing the analysis model of backscattering coefficient in the study area.It is found that the surface backscattering coefficient increases gradually from the outer edge of the working face to the center of the working face, and changes sharply in the central area of the working face.By comparing the relationship between the settlement rate and backscattering coefficient of the observation points of the working face trend and tendency surface characteristics, it is found that the average correlation coefficient r is greater than 0.8 in the mining subsidence process, which has a strong correlation.The experimental results show that the backscattering coefficient can be used to analyze the law of surface dynamic subsidence rate.It is an index to identify the intensity of surface activity in large-scale subsidence area.It provides a new analysis method for surface deformation in mining area and has a good application prospect.

       

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