基于时序InSAR、GPS、影像偏移测量3种监测数据的滑坡失稳破坏预测研究

Study of landslide failure prediction based on TS-InSAR,GPS and image offset monitoring

基于时序InSAR(Interferometric Synthetic Aperture Radar),GPS和影像偏移三种位移测量数据,采用逆速度法(INV)和斜率法(SLO)两种预测模型计算滑坡变形破坏的发展趋势,并探讨3种数据与预测模型结合存在的问题,初步探索基于遥感测量的时序变形数据的滑坡监测预警技术。对于四川省茂县新磨村2017年"6·24"滑坡,采用滑前45景Sentinel–1数据进行时序InSAR监测,结果表明在滑前2个月主变形区呈现明显加速现象,预测模型得到可在滑坡破坏前2d预警;四川美姑河作坊洗边坡18个月6个GPS点监测到边坡累计1米多的变形和部分时段的加速变形,坡面也存在明显的错断现象,但预测图和剩余寿命图显示出边坡将逐渐稳定的趋势,2年来的现场观察和目前滑坡的状态验证其准确性;对金沙江上游白格滑坡各变形区进行滑坡破坏时间预测,变形最快的区域得到了较为准确的预测结果。研究结果表明:(1)INV和SLO方法结合遥感时序变形监测可以对滑坡的发展趋势甚至溃滑时间做出预测,是一种高效经济的监测预警手段;(2)INV和SLO方法在滑坡预测工作中各有优势,SLO模型的预测结果比INV模型的预测结果偏保守,但在边坡减速变形过程中INV模型的预测结果可以指示出恢复稳定的趋势,SLO模型更易得到无效数据。两者的预测结果呈现出互补的特性,2种模型同时使用比单独使用得到的预测结果更可靠。

Based on TS-InSAR(time series-interferometric synthetic aperture radar),GPS and image offset measurement data,this paper employs inverse velocity model(INV)and slope model(SLO)to calculate landslide failure trend,discusses the existing questions about the combination of the three types of data and prediction models and explores a landslide monitoring and warning technique based on time-series deformation data measured by remote sensing.TS-InSAR monitoring of landslide in Xinmo Village,Maoxian County,Sichuan Province occurred on 24 June 2017,indicates that the main displacement acceleration started two months ahead before failure,and the prediction models can provide early warning two days in advance.Although the deformation accumulated over 1 meter and accelerated in part of the time for 18 months continuously monitored by 6 GPS monitoring stations and observed serious ground fissures at the Zuofangxi slope in Meigu River of Sichuan Province,the predicted and residual life plots of two models showed that this slope is gradually stable.Field investigation verified the judgment and the current state of this slope.The failure time of Baige landslide in the upper stream of the Jinsha River is predicted by INV and SLO methods using satellite imagery offset measure data and predicted accurately in the most rapidly deformed area.The results show that(1)INV and SLO combined with remote sensing time-series deformation monitoring can predict the development trend and even the time of landslide failure,which is an efficient and economic monitoring and forecasting method.(2)Both INV and SLO have their advantages in landslides prediction,the results of the SLO model are more conservative than that of the INV model.But in the process of slope deceleration,the INV model can indicate the trend to restore stability,and the SLO model is more likely to get an invalid forecast.The prediction results of the two models are complementary,and the two models are more reliable when used together than when used alone.