Land subsidence monitoring based on InSAR and inversion of aquifer parameters

被引:0
|
作者
Zhang Ziwen
Yijun Liu
Feng Li
Qi Li
Wujian Ye
机构
[1] Guangdong University of Technology,School of Information Engineering
[2] Guangdong Polytechnic Normal University,School of Automobile and Transportation Engineering
关键词
InSAR; CWT; Parameter inversion; Groundwater; Subsidence;
D O I
暂无
中图分类号
学科分类号
摘要
In order to accurately separate the elastic and inelastic deformation information caused by aquifer compression in the land subsidence signal, and to invert the hydrogeological parameters of high spatial and temporal resolution to better apply the groundwater-ground subsidence model, a CWT (Continuous Wavelet Transform) separation method for aquifer elastic and inelastic deformation signals based on CWT is adopted, and the deformation signal is extracted by InSAR technology. The large-scale synthetic aperture radar dataset obtained by Envisat satellite from 2007 to 2009 is collected to obtain the surface deformation characteristic of the area by SBAS-InSAR technology, and then the independence provided by the observation well is used. Using the independent water level data provided by the observation wells, combined with the vertical InSAR deformation component and the head data, the CWT method is used to separate the periodic deformation signal components and long-term trends. Finally, the isolated signal component is used to invert the elastic and inelastic storage coefficient based on the ground subsidence model. The settlement signal separation method used in this paper makes up for the shortcomings of the two kinds of information in the previous settlement signal that are difficult to separate, which allows for more accurate inversion of aquifer parameters and helps to understand the aquifer parameters and continuously manage groundwater resources.
引用
收藏
相关论文
共 50 条
  • [31] Monitoring Land Subsidence Using PS-InSAR Technique in Rawalpindi and Islamabad, Pakistan
    Khan, Junaid
    Ren, Xingwei
    Hussain, Muhammad Afaq
    Jan, M. Qasim
    REMOTE SENSING, 2022, 14 (15)
  • [32] Monitoring land subsidence of Jakarta (Indonesia) using leveling, GPS survey and InSAR techniques
    Abidin, HZ
    Andreas, H
    Gamal, M
    Djaja, R
    Subarya, C
    Hirose, K
    Maruyama, Y
    Murdohardono, D
    Rajiyowiryono, H
    Window on the Future of Geodesy, 2005, 128 : 561 - 566
  • [33] Monitoring Land Subsidence along the Subways in Shanghai on the Basis of Time-Series InSAR
    Zhang, Jinhua
    Ke, Changqing
    Shen, Xiaoyi
    Lin, Jinxin
    Wang, Ru
    REMOTE SENSING, 2023, 15 (04)
  • [34] A New Inversion Method for Obtaining Underwater Spatial Information of Subsidence Waterlogging Based on InSAR Technology and Subsidence Prediction
    Zhu, Xiaojun
    Qiu, Mingjian
    Zhang, Pengfei
    Ni, Errui
    Zhang, Jianxin
    Quan, Li'ao
    Liu, Hui
    Yang, Xiaoyu
    WATER, 2024, 16 (07)
  • [35] SURFACE SUBSIDENCE MONITORING IN GANZHOU AREA BASED ON SBAS-INSAR
    Li, Xinyi
    Zhou, Lv
    Ma, Jun
    Zhu, Zilin
    Li, Xin
    Huang, Ling
    XXIV ISPRS CONGRESS: IMAGING TODAY, FORESEEING TOMORROW, COMMISSION III, 2022, 43-B3 : 293 - 299
  • [36] InSAR-based monitoring and analysis of ground subsidence in old goaf
    Deng, Kazhong
    Wang, Liuyu
    Fan, Hongdong
    Caikuang yu Anquan Gongcheng Xuebao/Journal of Mining and Safety Engineering, 2015, 32 (06): : 918 - 922
  • [37] InSAR techniques and applications for monitoring landslides and subsidence
    Rott, H
    Nagler, T
    Rocca, F
    Prati, C
    Mazzotti, A
    Keusen, H
    Liener, S
    Tarchi, D
    GEOINFORMATION FOR EUROPEAN-WIDE INTEGRATION, 2003, : 25 - 31
  • [38] Analysis and Evaluation of Land Subsidence along Linear Engineering Based on InSAR Data
    Ding, Pengpeng
    Jia, Chao
    Di, Shengtong
    Wu, Jing
    Wei, Ruchun
    KSCE JOURNAL OF CIVIL ENGINEERING, 2021, 25 (09) : 3477 - 3491
  • [39] Land Subsidence Assessment of an Archipelago Based on the InSAR Time Series Analysis Method
    Ma, Deming
    Zhao, Rui
    Li, Yongsheng
    Li, Zhengguang
    WATER, 2023, 15 (03)
  • [40] Land subsidence susceptibility mapping based on InSAR and a hybrid machine learning approach
    Alesheikh, Ali Asghar
    Chatrsimab, Zahra
    Rezaie, Fatemeh
    Lee, Saro
    Jafari, Ali
    Panahi, Mahdi
    EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES, 2024, 27 (02): : 255 - 267