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基于时间序列InSAR和卡尔曼滤波的高家湾滑坡坡向变形演化分析

Evolutionary analysis of slope direction deformation in the Gaojiawan landslide based on time-series InSAR and Kalman filtering.

作者信息

Yao Jingchuan, Zhan Runqing, Guo Jiliang, Wang Wei, Yuan Muce, Li Guangyu, Zhang Bo, Zhang Rui

机构信息

China Academy of Railway Sciences Corporation Limited, Beijing, China.

China Railway Desigin Corporation, Tianjin, China.

出版信息

PLoS One. 2024 Dec 31;19(12):e0316100. doi: 10.1371/journal.pone.0316100. eCollection 2024.

Abstract

The existing landslide monitoring methods are unable to accurately reflect the true deformation of the landslide body, and the use of a single SAR satellite, affected by its revisit cycle, still suffers from the limitation of insufficient temporal resolution for landslide monitoring. Therefore, this paper proposes a method for the dynamic reconstruction and evolutionary characteristic analysis of the Gaojiawan landslide's along-slope deformation based on ascending and descending orbit time-series InSAR observations using Kalman filtering. Initially, the method employs a gridded selection approach during the InSAR time-series processing, filtering coherent points based on the standard deviation of residual phases, thereby ensuring the density and quality of the extracted coherent points. Subsequently, the combination of ascending and descending orbit data converts the landslide's line of sight (LOS) deformation into along-slope deformation. Finally, the Kalman filtering method is utilized for dynamic reconstruction of the landslide deformation, and an analysis of the evolutionary characteristics of the landslide is conducted to explore its impact on transportation infrastructure, thereby significantly improving the temporal resolution and accuracy of landslide monitoring. To verify the feasibility of the algorithm, this paper selects the Gaojiawan landslide as a typical study area. Based on the ascending and descending Sentinel-1 SAR data from 2016 to 2023, it extracts the temporal series of slope body deformation to further explore its impact on the internal transportation infrastructure of the slope body. Experimental results show that the combination of ascending and descending SAR data and Kalman filtering has improved the time resolution of landslide monitoring to six days. It was found that two significant slips occurred in the slope body in January 2016 and June 2021, while other periods were relatively stable. Further discussion and analysis reveal that there is a difference in the slip deformation rate between the upper and lower parts of the slope body, and the shear stress caused by dislocation deformation.

摘要

现有的滑坡监测方法无法准确反映滑坡体的真实变形情况,且使用单一的合成孔径雷达(SAR)卫星时,受其重访周期影响,在滑坡监测的时间分辨率方面仍存在不足。因此,本文提出一种基于升轨和降轨时间序列干涉合成孔径雷达(InSAR)观测数据,利用卡尔曼滤波的高家湾滑坡顺坡变形动态重构及演化特征分析方法。该方法在InSAR时间序列处理过程中,首先采用网格化选取方式,基于残余相位标准差对相干点进行滤波,从而保证提取相干点的密度和质量。随后,通过升轨和降轨数据的结合,将滑坡的视线(LOS)变形转换为顺坡变形。最后,利用卡尔曼滤波方法对滑坡变形进行动态重构,并对滑坡的演化特征进行分析,以探究其对交通基础设施的影响,从而显著提高滑坡监测的时间分辨率和精度。为验证算法的可行性,本文选取高家湾滑坡作为典型研究区域。基于2016年至2023年的升轨和降轨哨兵 - 1 SAR数据,提取坡体变形的时间序列,进一步探究其对坡体内部交通基础设施的影响。实验结果表明,升轨和降轨SAR数据与卡尔曼滤波相结合,将滑坡监测的时间分辨率提高到了6天。研究发现,坡体在2016年1月和2021年6月发生了两次显著滑动,而其他时期相对稳定。进一步的讨论和分析表明,坡体上下部的滑动变形速率存在差异,且存在位错变形引起的剪应力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85aa/11687915/d442cb434bd9/pone.0316100.g001.jpg

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