Monitoring and forecasting analysis of a landslide in Xinmo, Mao County, using Sentinel-1 data
Monitoring and forecasting analysis of a landslide in Xinmo, Mao County, using Sentinel-1 data
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利用Sentinel-1数据对茂县新磨山体滑坡进行监测预报分析
DOI：
10.3319/tao.2018.10.16.01
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Yang Liu
中科院分区：
文献类型：
--
作者：
Yuxin Liu;Caijun Xu;Yang Liu
2017年6月24日，四川省茂县新磨村发生特大山体滑坡。选取Sentinel-1卫星的合成孔径雷达(SAR)图像，利用小基线集(SBAS)技术对滑坡进行监测，得到了源区的变形时间序列，并与加速蠕变模型进行了比较。滑坡前的位移时间序列清楚地显示了与瞬时蠕变、稳态蠕变和第三次蠕变相关的运动过程。通过计算5个代表性区域的平均位移，确定了主要变形区。选取滑坡发生前3个月的时间序列，采用逆速法分别计算滑坡的破坏时间和联合计算滑坡的破坏时间。结果表明，主变形区的时间序列比边缘变形区的时间序列能更好地拟合逆速度的线性模型，预测时间更接近实际破坏时间。利用主变形区三个区域的时间序列计算出的预测时间为6月25日，与实际破坏时间仅相差一天。文章历史：收到2018年3月7日修订2018年10月2日受理2018年10月16日
On 24 June 2017, an enormous landslide struck the village of Xinmo in Mao County, Sichuan Province. Synthetic aperture radar (SAR) images from the Sentinel-1 satellite are chosen to monitor the landslide using the small baseline set (SBAS) technology, following which the deformation time series are obtained for the source area and are found to be consistent with the accelerated creep model. The displacement time series before the landslide clearly show movement processes associated with transient creep, steady-state creep and tertiary creep. The main deformation area is ascertained by calculating the average displacement of 5 representative regions. Three-month time series before the landslide are selected to calculate the failure time of the landslide both separately and together using the inverse-velocity method. The results show that the time series of the main deformation area can fit a linear model of the inverse velocities better than those of the marginal area, and the forecasted time is closer to the actual failure time. The forecasted time calculated using the time series of three regions in main deformation area is June 25, which is only one day apart from the actual failure time. Article history: Received 7 March 2018 Revised 2 October 2018 Accepted 16 October 2018