基于时间序列的InSAR和Kalman过的Gaojiawan山体滑坡倾斜方向变形的进化分析
Jingchuan Yao1, Runqing Zhan2,3, Jiliang Guo4,5
1China Academy of Railway Sciences Corporation Limited, Beijing, China.
这项研究通过将上升和下降的卫星数据与卡尔曼过相结合来增强滑坡监测,将时间分辨率提高到六天以进行准确的变形分析.
科学领域:
- 地质科学 地质科学
- 遥感 遥感 遥感 遥感
- 地质技术工程 地质技术工程
背景情况:
- 现有的山体滑坡监测方法缺乏准确性和时间分辨率.
- 单一合成孔径雷达 (SAR) 卫星数据受到重新访问周期的限制.
- 准确的变形监测对于了解滑坡演变和影响至关重要.
研究的目的:
- 开发一个动态重建和进化分析方法,用于滑坡沿斜坡变形的滑坡.
- 提高滑坡监测的时间分辨率和准确性.
- 评估滑坡变形对交通基础设施的影响.
主要方法:
- 利用时间序列交叉测量SAR (InSAR) 从上升和下降轨道的观测.
- 在InSAR处理中使用网格选择方法来过连贯点.
- 结合上升/下降数据来得出沿斜坡变形,并应用卡尔曼波来进行动态重建.
主要成果:
- 通过集成上升/下降SAR数据和卡尔曼过,实现了六天的监控时间分辨率.
- 确定了2016年1月和2021年6月的两个重大滑坡.
- 在上和下斜坡段之间观察到差异性滑动变形速率.
结论:
- 拟议的方法显著提高了山体滑坡监测的准确性和时间分辨率.
- 该研究提供了关于山体滑坡演化特征及其对基础设施的影响的见解.
- 加尔曼过与多轨道InSAR数据相结合,对于动态滑坡变形分析是有效的.
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