Kinematics and mechanism analysis of Tangjiawan landslide on the Xinshi—Jinyang Highway in Sichuan Province
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摘要:
研究滑坡变形特征对于分析滑坡形成机理和制定防治措施至关重要。文章以工程诱发的唐家湾滑坡为研究对象，通过工程施工前后的6期无人机影像得到高分辨率DOM，基于相邻两期DOM中识别的特征点作为监测点，根据其位置的变化得出地表位移矢量数据，进而结合地质勘探和深部位移监测分析滑坡变形特征和形成机理。研究表明：工程建设前滑坡区无明显变形（第1个观测周期），工程施工后的第2观测周期（2021-03-15—2021-06-06）、第3 观测周期（2021-06-06—2021-09-08）和第4观测周期（2021-09-08—2021-11-03）滑坡主滑区平均变形速率分别为53.0，103.2和62.5 mm/d，至第5观测周期（2021-11-03—2022-01-03）变形速率趋于0；第2观测周期滑坡后缘的弃渣堆载是滑坡的直接触发因素，降雨促进了滑坡变形的发展，而随着雨季的结束和前缘的堆载反压滑坡变形速率逐渐降低；利用多期无人机高清影像可获取大范围、长时序地表变形信息，可作为一种有效的滑坡变形监测手段。
Abstract:Studying the kinematics of landslides is crucial for analyzing failure mechanism and designing remedial measures. This paper focuses on the Tandjiawan landslide that occurred during a highway construction. Five periods of high-resolution digital orthophoto maps (DOM) were generated using unmanned aerial vehicle (UAV)- based photogrammetry, spanning both pre- and post- landslide conditions. Two successive UAV orthophotos were treated as observation periods, and corresponding features were identified in both images to establish monitoring points. Furthermore, two-dimensional displacement vectors were then computed by comparing orthographic images from each observation period based on these corresponding features. The analysis of kinematics and failure mechanism were conducted in conjunction with geological surveys and inclinometer measurements. The findings reveal that there was no significant deformation in the landslide area before the engineering construction of the highway (1st observation period). After construction, during the second observation period (March 15, 2021, to June 6, 2021), the third observation period (June 6, 2021, to September 8, 2021), and the fourth observation period (September 8, 2021, to November 3, 2021), the average deformation rates of the main sliding area of the landslide were 53.0 mm/day, 103.2 mm/day and 62.5 mm/day, respectively. By the fifth observation period (November 3, 2021, to January 3, 2022), the deformation rates had trended towards zero. The deposition of spoil at the rear of the landslide during the second observation period was the direct triggering factor, and rainfall facilitated the development of landslide deformation. As the rainy season ended and the front-end loading increased, the landslide deformation rate gradually decreased. This paper demonstrates that multi-period UAV photogrammetry can provide spatiotemporal surface deformation information for landslide areas, serving as an effective tools for landslide deformation monitoring.
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Key words:
- UAV images /
- photogrammetry /
- landslide /
- kinematics
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表 1 数据汇总
Table 1. Summary of the data
阶段 使用设备 像控设置 影像分辨率
/cm获取日期 滑前 直升机搭载飞思相机（1亿像素）
和Optech eclipse 激光雷达沿线测绘
地面控制点<10 2018-05-15 DJI Mavic2 勘察期间
测绘控制点<3 2019-03-26 滑后 DJI Mavic2 滑坡周边4个
地面控制点<3 2021-06-06 DJI Mavic2 <3 2021-09-08 DJI Mavic2 <3 2021-11-03 DJI Mavic2 <3 2022-01-03 
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表 2 各观测周期的观测时长和监测点数量
Table 2. Observation period duration and number of monitoring points for each observation period
观测期 起止日期 时间间隔/d 监测点数量/个 1 2018-05-15—2019-03-26 325 17 2 2019-03-26—2021-06-06 803 7 3 2021-06-06—2021-09-08 94 25 4 2021-09-08—2021-11-03 56 68 5 2021-11-03—2022-01-03 61 49
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表 3 各监测周期监测点平均变形量及变形速率
Table 3. Average displacement vectors and average deformation rates of monitoring points for each monitoring period
分区 数据类型 第1观测期
（2018-05-15—
2019-03-26）第2观测期
（2019-03-26—
2021-06-06）第3观测期
（2021-06-06—
2021-09-08）第4观测期
（2021-09-08—
2021-11-03）第5观测期
（2021-11-03—
2022-01-03）I区 平均变形量/m <0.1 — 3.8 0.7 — 平均变形速率/（mm·d−1） 0 — 40.4 12.5 — II-1区 变形量平均值/m <0.1 — — 3.2 0.140 平均变形速率/（mm·d−1） 0 — — 57.1 0.002 II-2区 变形量平均值/m <0.1 4.4 9.7 3.5 0.090 平均变形速率/（mm·d−1） 0 53.0 103.2 62.5 0 注：—标示该区内施工导致地形变化，无法取得匹配监测点；第2观测周期变形速率计算起始时间为工程开始施工的2021年3月15日。
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