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

LIU Xiaojie; ZHAO Chaoying; LI Bin; WANG Wenda; ZHANG Qin; GAO Yang; CHEN Liquan; WANG Baohang; HAO Junming; YANG Xiaohui

doi:10.13203/j.whugis20240054

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

Jishishan Earthquake | 更新时间：2026-11-05

- Identification and Dynamic Deformation Monitoring of Active Landslides in Jishishan Earthquake Area （Gansu， China） Using InSAR Technology
- Geomatics and Information Science of Wuhan University Vol. 50, Issue 2, Pages: 297-312(2025)
- 作者机构：
  
  1.兰州理工大学土木工程学院，甘肃兰州，730050
  2.长安大学地质工程与测绘学院，陕西西安，710054
  3.中国地质科学院地质力学研究所，北京，100081
  4.闽江学院地理与海洋学院，福建福州，350108
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20240054
  CLC： P237
- Received：17 February 2024，
  
  Published：05 February 2025
- 稿件说明：
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刘晓杰,赵超英,李滨，等.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.

LIU Xiaojie,ZHAO Chaoying,LI Bin,et al.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.
刘晓杰,赵超英,李滨，等.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,et al.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

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.

关键词

Keywords

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