基于InSAR技术的甘肃积石山震区活动滑坡识别与动态形变监测

刘晓杰, 赵超英, 李滨, 王文达, 张勤, 高杨, 陈立权, 王宝行, 郝君明, 杨校辉

刘晓杰, 赵超英, 李滨, 王文达, 张勤, 高杨, 陈立权, 王宝行, 郝君明, 杨校辉. 基于InSAR技术的甘肃积石山震区活动滑坡识别与动态形变监测[J]. 武汉大学学报 ( 信息科学版), 2025, 50(2): 297-312. DOI: 10.13203/j.whugis20240054
引用本文: 刘晓杰, 赵超英, 李滨, 王文达, 张勤, 高杨, 陈立权, 王宝行, 郝君明, 杨校辉. 基于InSAR技术的甘肃积石山震区活动滑坡识别与动态形变监测[J]. 武汉大学学报 ( 信息科学版), 2025, 50(2): 297-312. DOI: 10.13203/j.whugis20240054
LIU Xiaojie, ZHAO Chaoying, LI Bin, WANG Wenda, ZHANG Qin, GAO Yang, CHEN Liquan, WANG Baohang, HAO Junming, YANG Xiaohui. Identification and Dynamic Deformation Monitoring of Active Landslides in Jishishan Earthquake Area (Gansu, China) Using InSAR Technology[J]. Geomatics and Information Science of Wuhan University, 2025, 50(2): 297-312. DOI: 10.13203/j.whugis20240054
Citation: LIU Xiaojie, ZHAO Chaoying, LI Bin, WANG Wenda, ZHANG Qin, GAO Yang, CHEN Liquan, WANG Baohang, HAO Junming, YANG Xiaohui. Identification and Dynamic Deformation Monitoring of Active Landslides in Jishishan Earthquake Area (Gansu, China) Using InSAR Technology[J]. Geomatics and Information Science of Wuhan University, 2025, 50(2): 297-312. DOI: 10.13203/j.whugis20240054

基于InSAR技术的甘肃积石山震区活动滑坡识别与动态形变监测

基金项目: 

国家重点研发计划 2022YFC3004302

甘肃省科技重大专项 23ZDFA007

甘肃省青年科技基金 23JRRA830

详细信息
    作者简介:

    刘晓杰,博士,博士后,讲师,研究方向为InSAR地质灾害监测。Xiaojie_Liu_cd@163.com

    通讯作者:

    赵超英,博士,教授。zhaochaoying@163.com

Identification and Dynamic Deformation Monitoring of Active Landslides in Jishishan Earthquake Area (Gansu, China) Using InSAR Technology

  • 摘要:

    2023-12-18,甘肃省临夏回族自治州积石山县发生Ms 6.2地震,造成150余人遇难及大量建筑物倒塌,并诱发了同震滑坡灾害。此外,地震还将加速活动滑坡的形变,严重威胁人民生命财产和基础设施的安全,亟需开展震区活动滑坡快速识别与动态形变监测研究。基于此,提出了一种基于合成孔径雷达干涉测量(interferometric synthetic aperture radar,InSAR)技术的地震区活动滑坡自动化识别与动态形变监测框架。采用2017年3月至2023年12月的升轨与降轨Sentinel-1影像,反演获得InSAR相位梯度速率、年平均形变速率及时间序列,建立了DeepLabv3深度学习滑坡自动化识别方法,快速绘制研究区域的活动滑坡编目图,并开展滑坡空间分布特征研究;并采用序贯InSAR技术实现了新获得的合成孔径雷达影像的快速处理,进行滑坡形变的动态监测,及时捕获地震造成的滑坡形变加速信号。研究结果表明,在积石山震中70 km范围内分布有2 021个不同尺度的潜在活动滑坡,集中在6个高密度分布区,主要分布在距断层22 km、距河流28 km、距道路10 km范围内和高程3 400 m以下、坡度为15°~35°的区域,且主要沿着正北向、东北及正东向分布;本次地震造成积石山县及黄河沿线部分区域的滑坡形变出现显著加速,降低了斜坡的稳定性,现场调查验证了动态形变监测结果的可靠性。所提方法可为类似地震事件的活动滑坡快速调查及动态监测提供重要技术指导,研究成果可为震后灾区重建及次生滑坡灾害风险评估提供科学数据支撑。

    Abstract:
    Objectives 

    On December 18, 2023, an Ms 6.2 earthquake occurred in Jishishan County, Gansu Province, China. The earthquake caused more than 150 deaths and extensive collapse of buildings, and triggered a mass of coseismic landslides. In addition, the earthquake will also accelerate the deformation of active landslides, seriously threatening the safety of people's lives and property and infrastructure. Therefore, it is imperative to carry out research on the rapid identification and dynamic deformation monitoring of active landslides in earthquake-affected areas.

    Methods 

    We propose a novel framework for the automatic identification and dynamic deformation monitoring of active landslides in earthquake areas based on interferometric synthetic aperture radar (InSAR) technology. First, the InSAR phase-gradient rate, deformation rate and time series are calculated by utilizing both the ascending and descending Sentinel-1 synthetic aperture radar (SAR) images acquired during March 2017 to December 2023; and then, an approach for the automatic identification of active landslides is established using DeepLabV3 deep learning algorithm. As a result, the inventory map of active landslides in the study area was produced using the proposed method, and the spatial distribution characteristics of the landslides were investigated. Second, newly acquired SAR images were rapidly processed using the sequential InSAR method, thus achieving the dynamic deformation monitoring of landslides and the timely capture of earthquake-induced acceleration deformation signal.

    Results 

    The InSAR results revealed that there are 2 021 active landslides with varying dimensions within a 70 km radius of the epicenter, and six regions exhibited particularly dense landslide distributions. The spatial distribution of the mapped landslides exhibited a clustering pattern: primarily within 22 km of faults, 28 km of rivers, and 10 km of roads. Additionally, they were predominantly located at elevations below 3 400 m and on slopes ranging 15°-35°, and also mainly distributed along the north, north-east, and east directions.

    Conclusion 

    The deformation of some detected landslides within Jishishan County and along the Yellow River were significantly accelerated as a result of the earthquake event, thus reducing the stability of the slope. The reliability of the InSAR-derived results was verified through the field geological investigation. The proposed method offers significant technical value for the rapid investigation and dynamic monitoring of active landslides in similar seismic events, and the research findings provide scientific data support for post-earthquake reconstruction and secondary landslide disaster risk assessment.

  • 感谢欧洲空间局提供的Sentinel⁃1 SAR影像及30 m空间分辨率的DEM数据;以及甘肃省地矿局第三地质矿产勘查院协助野外调查验证。
    http://ch.whu.edu.cn/cn/article/doi/10.13203/j.whugis20240054
  • 图  1   研究区域概况及地震构造背景

    Figure  1.   Overview of the Study Area and Seismotectonic Background

    图  2   升轨(a)与降轨(b)干涉图时空基线分布

    注:黑线表示历史存档SAR数据的干涉图;红线表示新增SAR数据的干涉图。

    Figure  2.   Temporal and Spatial Baseline Distribution of the Ascending (a) and Descending (b) Interferograms

    图  3   深度学习活动滑坡自动化识别与序贯InSAR形变动态监测流程图

    Figure  3.   Flowchart of Automatic Landslide Identification Using Deep Learning and Dynamic Deformation MonitoringUsing Sequential InSAR

    图  4   2017年3月至2023年12月升轨(a)与降轨(b)InSAR相位梯度速率图

    Figure  4.   InSAR Phase Gradient Velocity of Ascending(a) and Descending(b) Images During March 2017 to December 2023

    图  5   基于DeepLabV3模型的InSAR相位梯度速率活动滑坡自动化识别框架

    Figure  5.   Automatic Identification Framework of Active Landslides Using InSAR Phase Gradient RateBased on the DeepLabV3 Model

    图  6   研究区域2017年3月至2023年12月升轨(a, c)与2018年3月至2023年12月降轨(b, d) Sentinel⁃1影像地表形变速率及累积形变

    注:负值(红色)表示远离SAR卫星视线方向的地表形变,正值(蓝色)表示靠近卫星视线方向的地表形变。

    Figure  6.   Deformation Rate and Cumulative Deformation of the Study Area Calculated with Ascending(March 2017 to December 2023) (a, c) and Descending(March 2018 to December 2023) (b, d) Sentinel-1 Images

    图  7   InSAR探测活动滑坡分布编目图及密度图

    Figure  7.   Inventory Map and Distribution Density Map of Active Landslides Detected by InSAR Deformation

    图  8   InSAR探测的4个典型滑坡的光学遥感影像(Google Earth)(a⁃d)及地表形变速率(a1⁃d1)

    Figure  8.   Optical Remote Sensing Images (Google Earth) (a⁃d) and Deformation Rates (a1⁃d1) of Four Exemplary Landslides Mapped by InSAR

    图  9   P1~P4点降轨Sentinel-1影像形变时间序列

    Figure  9.   Time Series of Deformation at Points P1-P4 Calculated with the Descending Sentinel-1 Images

    图  10   滑坡分布与断层(a)、高程(b)、坡度(c)、河流(d)、道路(e)及坡向(f)的关系

    Figure  10.   Relationship Between the Landslide Distribution and the Fault (a), Elevation (b),Slope Angle (c), River (d),Road (e) and Slope Aspect (f)

    图  11   林边村滑坡Google Earth影像(a)、年平均形变速率(b)、现场照片(c、d)及形变时间序列(e)

    Figure  11.   Google Earth Image (a), Annual Average Deformation Rate (b), Scene Photos (c,d) andDeformation Time Series (e) of the Linbiancun Landslide

    图  12   积石峡水库滑坡Google Earth影像(a)、年平均形变速率(b)及时间序列(c)

    Figure  12.   Google Earth Image (a), Annual Average Deformation Rate (b) andTime Series (c) of the Jishixia Reservoir Landslide

    表  1   升轨与降轨Sentinel-1影像基本参数

    Table  1   Basic Parameters of the Ascending and Descending Sentinel-1 Images

    统计项Sentinel⁃1升轨Sentinel⁃1降轨
    方位角/(°)-13.16-166.93
    入射角/(°)39.4833.75
    距离向与方位向分辨率/m2.33×13.972.33×13.97
    多视空间分辨率/m1515
    时间跨度2017⁃03⁃20—2023⁃12⁃262018⁃03⁃27—2023⁃12⁃26
    影像数量/景184157
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-02-16
  • 网络出版日期:  2024-04-08
  • 刊出日期:  2025-02-04

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