QIN Hongnan, MA Haitao, YU Zhengxing, LIU Yuxi. Landslide Early Warning Method Based on Dynamic High Frequency Data of Ground-Based Radar Interferometry[J]. Geomatics and Information Science of Wuhan University, 2024, 49(8): 1330-1336. DOI: 10.13203/j.whugis20220152
Citation: QIN Hongnan, MA Haitao, YU Zhengxing, LIU Yuxi. Landslide Early Warning Method Based on Dynamic High Frequency Data of Ground-Based Radar Interferometry[J]. Geomatics and Information Science of Wuhan University, 2024, 49(8): 1330-1336. DOI: 10.13203/j.whugis20220152

Landslide Early Warning Method Based on Dynamic High Frequency Data of Ground-Based Radar Interferometry

More Information
  • Received Date: December 09, 2022
  • Available Online: July 21, 2022
  • Objectives 

    Mine slope instability is one of the main factors restricting the safety production of open-pit mines in China. Ground-based synthetic aperture radar interferometry technology has been gradually introduced into the application of slope safety monitoring and early warning prediction in open-pit mines. However, the high-frequency rolling update characteristics of ground radar interferometry data lead to large data error accumulation and unobvious curve mutation characteristics.

    Methods 

    Processing the original data by dislocation subtraction and velocity reciprocal method can effectively reduce the vibration of high-frequency data, improve the readability of critical sliding data. After data processing, it can highlight the trend characteristics of key deformation data. The research is based on the analysis of cumulative displacement curve, velocity curve and reciprocal velocity curve group treated with different periods.

    Results 

    It is found that there are three characteristic points in the curve group: Sudden deformation increase point, velocity increase point and stable vibration point. Through these characteristic points, the slope landslide disaster can be predicted. The trend of key deformation data can be highlighted by using the three feature points of deformation sudden increase point, velocity growth point and stable vibration point.

    Conclusions 

    Through the identification of three feature points, the possible landslide can be effectively identified in advance and the landslide time can be predicted, which provides a new technical path and solution for landslide early warning and prediction analysis based on ground-based interferometric radar.

  • [1]
    许强, 董秀军, 李为乐. 基于天-空-地一体化的重大地质灾害隐患早期识别与监测预警[J]. 武汉大学学报(信息科学版), 2019, 44(7): 957-966.

    Xu Qiang, Dong Xiujun, Li Weile. Integrated Space-Air-Ground Early Detection, Monitoring and Warning System for Potential Catastrophic Geoha-zards[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 957-966.
    [2]
    刘国祥, 张波, 张瑞, 等. 联合卫星SAR和地基SAR的海螺沟冰川动态变化及次生滑坡灾害监测[J]. 武汉大学学报(信息科学版), 2019, 44(7): 980-995.

    Liu Guoxiang, Zhang Bo, Zhang Rui, et al. Monitoring Dynamics of Hailuogou Glacier and the Se-condary Landslide Disasters Based on Combination of Satellite SAR and Ground-Based SAR[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 980-995.
    [3]
    陆会燕, 李为乐, 许强, 等. 光学遥感与InSAR结合的金沙江白格滑坡上下游滑坡隐患早期识别[J]. 武汉大学学报(信息科学版), 2019, 44(9): 1342-1354.

    Lu Huiyan, Li Weile, Xu Qiang, et al. Early Detection of Landslides in the Upstream and Downstream Areas of the Baige Landslide, the Jinsha River Based on Optical Remote Sensing and InSAR Technologies[J]. Geomatics and Information Science of Wuhan University, 2019, 44(9): 1342-1354.
    [4]
    刘斌, 葛大庆, 王珊珊, 等. TOPS和ScanSAR模式InSAR在广域地灾隐患识别中的联合应用[J]. 武汉大学学报(信息科学版), 2020, 45(11): 1756-1762.

    Liu Bin, Ge Daqing, Wang Shanshan, et al. Combining Application of TOPS and ScanSAR InSAR in Large-Scale Geohazards Identification[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11): 1756-1762.
    [5]
    吴星辉, 马海涛, 张杰. 地基合成孔径雷达的发展现状及应用[J]. 武汉大学学报(信息科学版), 2019, 44(7): 1073-1081.

    Wu Xinghui, Ma Haitao, Zhang Jie. Development Status and Application of Ground-Based Synthetic Aperture Radar[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 1073-1081.
    [6]
    Antonello G, Casagli N, Farina P, et al. Ground-Based SAR Interferometry for Monitoring Mass Movements[J]. Landslides, 2004, 1(1): 21-28.
    [7]
    Wang Y P, Tan W X, Hong W,et al. Ground-Based SAR for Man-Made Structure Deformation Monitoring[C]//The 1st International Work-shop Spatial Information Technologies for Monitoring the Deformation of Large-Scale Man-Made Li-near Features, Hong Kong, China,2010.
    [8]
    Antonello G, Tarchi D, Casagli N, et al. SAR Interferometry from Satellite and Ground-Based System for Monitoring Deformations on the Stromboli Volcano[C]//IEEE International Geoscience and Remote Sensing Symposium, Anchorage, USA, 2004.
    [9]
    Luzi G, Pieraccini M, Mecatti D, et al. Ground-Based Radar Interferometry for Landslides Monito-ring: Atmospheric and Instrumental Decorrelation Sources on Experimental Data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(11): 2454-2466.
    [10]
    葛大庆, 戴可人, 郭兆成, 等. 重大地质灾害隐患早期识别中综合遥感应用的思考与建议[J]. 武汉大学学报(信息科学版), 2019, 44(7): 949-956.

    Ge Daqing, Dai Keren, Guo Zhaocheng, et al. Early Identification of Serious Geological Hazards with Integrated Remote Sensing Technologies: Thoughts and Recommendations[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 949-956.
    [11]
    李振洪, 宋闯, 余琛, 等. 卫星雷达遥感在滑坡灾害探测和监测中的应用: 挑战与对策[J]. 武汉大学学报(信息科学版), 2019, 44(7): 967-979.

    Li Zhenhong, Song Chuang, Yu Chen, et al. Application of Satellite Radar Remote Sensing to Landslide Detection and Monitoring: Challenges and Solutions[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 967-979.
    [12]
    王延平, 许强, 郑光, 等. 速度倒数法滑坡预警模型流变试验研究[J]. 岩土力学, 2015, 36(6): 1606-1614.

    Wang Yanping, Xu Qiang, Zheng Guang, et al. A Rheology Experimental Investigation on Early War-ning Model for Landslide Based on Inverse-Velocity Method[J]. Rock and Soil Mechanics, 2015, 36(6): 1606-1614.
    [13]
    周吕, 郭际明, 胡纪元, 等. 基于二维形变场的地基SAR精度验证与分析[J]. 武汉大学学报(信息科学版), 2019, 44(2): 289-295.

    Zhou Lü, Guo Jiming, Hu Jiyuan, et al. Accuracy Verification and Analysis of Ground-Based Synthe-tic Aperture Radar Based on Two-Dimensional Deformation Field[J]. Geomatics and Information Science of Wuhan University, 2019, 44(2): 289-295.
    [14]
    Bardi F, Frodella W, Ciampalini A, et al. Integration Between Ground Based and Satellite SAR Data in Landslide Mapping: The San Fratello Case Study[J]. Geomorphology, 2014, 223: 45-60.
    [15]
    许强, 李为乐, 董秀军, 等. 四川茂县叠溪镇新磨村滑坡特征与成因机制初步研究[J]. 岩石力学与工程学报, 2017, 36(11): 2612-2628.

    Xu Qiang, Li Weile, Dong Xiujun, et al. The Xinmocun Landslide on June 24, 2017 in Maoxian, Sichuan: Characteristics and Failure Mechanism[J]. Chinese Journal of Rock Mechanics and Engineering, 2017, 36(11): 2612-2628.
    [16]
    秦宏楠, 马海涛, 于正兴. 地基SAR技术支持下的滑坡预警预报分析方法[J]. 武汉大学学报(信息科学版), 2020, 45(11): 1697-1706.

    Qin Hongnan, Ma Haitao, Yu Zhengxing. Analysis Method of Landslide Early Warning and Prediction Supported by Ground-Based SAR Technology[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11): 1697-1706.
    [17]
    高玮, 冯夏庭. 基于灰色-进化神经网络的滑坡变形预测研究[J]. 岩土力学, 2004, 25(4): 514-517.

    Gao Wei, Feng Xiating. Study on Displacement Predication of Landslide Based on Grey System and Evolutionary Neural Network[J]. Rock and Soil Mechanics, 2004, 25(4): 514-517.
    [18]
    贺小黑, 王思敬, 肖锐铧, 等. 协同滑坡预测预报模型的改进及其应用[J]. 岩土工程学报, 2013, 35(10): 1839-1848.

    He Xiaohei, Wang Sijing, Xiao Ruihua, et al. Improvement and Application of Synergetic Forecast Model for Landslides[J]. Chinese Journal of Geotechnical Engineering, 2013, 35(10): 1839-1848.
    [19]
    罗文强, 冀雅楠, 王淳越, 等. 多监测点滑坡变形预测的似乎不相关模型研究[J]. 岩石力学与工程学报, 2016, 35(S1): 3051-3056.

    Luo Wenqiang, Ji Yanan, Wang Chunyue, et al. Research on Seemingly Unrelated Regressions Model of Landslide Displacement Prediction of Multiple Monitoring Points[J]. Chinese Journal of Rock Mechanics and Engineering, 2016, 35(S1): 3051-3056.
    [20]
    张俊, 殷坤龙, 王佳佳, 等. 基于时间序列与PSO-SVR耦合模型的白水河滑坡位移预测研究[J]. 岩石力学与工程学报, 2015, 34(2): 382-391.

    Zhang Jun, Yin Kunlong, Wang Jiajia, et al. Displacement Prediction of Baishuihe Landslide Based on Time Series and PSO-SVR Model[J]. Chinese Journal of Rock Mechanics and Engineering, 2015, 34(2): 382-391.
    [21]
    杜娟, 殷坤龙, 柴波. 基于诱发因素响应分析的滑坡位移预测模型研究[J]. 岩石力学与工程学报, 2009, 28(9): 1783-1789.

    Du Juan, Yin Kunlong, Chai Bo. Study of Displacement Prediction Model of Landslide Based on Response Analysis of Inducing Factors[J]. Chinese Journal of Rock Mechanics and Engineering, 2009, 28(9): 1783-1789.
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