首页|Potential sliding zone recognition method for the slow-moving landslide based on the Hurst exponent

Potential sliding zone recognition method for the slow-moving landslide based on the Hurst exponent

扫码查看
原文链接
  • 万方数据
  • 维普
The abrupt occurrence of the Zhongbao landslide is totally unexpected,resulting in the destruction of local infrastructure and river blockage.To review the deformation history of the Zhongbao landslide and prevent the threat of secondary disasters,the small baseline subsets(SBAS)technology is applied to process 59 synthetic aperture radar(SAR)images captured from Sentinel-1A satellite.Firstly,the time series deformation of the Zhongbao landslide along the radar line of sight(LOS)direction is calculated by SBAS technology.Then,the projection transformation is conducted to determine the slope displacement.Furthermore,the Hurst exponent of the surface deformation along the two directions is calculated to quantify the hidden deformation development trend and identify the unstable deformation areas.Given the suddenness of the Zhongbao landslide failure,the multi-temporal interferometric synthetic aperture radar(InSAR)technology is the ideal tool to obtain the surface deformation history without any moni-toring equipment.The obtained deformation process indicates that the Zhongbao landslide is generally stable with slow creep deformation before failure.Moreover,the Hurst exponent distribution on the landslide surface in different time stages reveals more deformation evolution information of the Zhongbao landslide,with partially unstable areas detected before the failure.Two potential unstable areas after the Zhongbao landslide disaster are revealed by the Hurst exponent distribution and verified by the GNSS monitoring results and deformation mechanism discussion.The method combining SBAS-InSAR and Hurst exponent proposed in this study could help prevent and control secondary landslide disasters.

Zhongbao landslideInterferometric synthetic aperture radar(InSAR)technologyHurst exponentDeformation processUnstable area identification

Haiqing Yang、Lili Qu、Lichuan Chen、Kanglei Song、Yong Yang、Zhenxing Liang

展开 >

State Key Laboratory of Coal Mine Disaster Dynamics and Control,School of Civil Engineering,Chongqing University,Chongqing,400045,China

Technology Innovation Center of Geohazards Automatic Monitoring,Ministry of Natural Resources(Chongqing Institute of Geology and Mineral Resources),Chongqing,401120,China

National Key R&D Program of Chinagraduate research and innovation foundation of Chongqing,ChinaSpecial project for performance incentive and guidance of scientific research institutions in Chongqing

2021YFB3901403CYS23115cstc2021jxjl120011

2024

10.1016/j.jrmge.2023.08.007

岩石力学与岩土工程学报(英文版)
中国科学院武汉岩土力学所中国岩石力学与工程学会武汉大学

岩石力学与岩土工程学报(英文版)

CSTPCD
影响因子：0.404
ISSN：1674-7755
年,卷(期)：2024.16(10)