Refined subsidence monitoring and dynamic prediction in narrow and long mining areas based on InSAR and probabilistic integral method

被引:0
|
作者
Wang, Zhiwei [1 ]
Zhao, Yue [1 ]
Wang, Peng [2 ]
Wang, Xiang [1 ]
Jiang, Aihui [3 ]
Zhang, Guojian [1 ]
Li, Wanqiu [1 ]
Liu, Jiantao [1 ]
机构
[1] Shandong Jianzhu Univ, Sch Surveying & Geoinfomat, Jinan 250101, Peoples R China
[2] Shandong Energy Zaozhuang Min Grp Sanhekou Min Co, Jining 277605, Peoples R China
[3] Shandong Normal Univ, Coll Geog & Environm, Jinan 250358, Peoples R China
来源
SCIENTIFIC REPORTS | 2024年 / 14卷 / 01期
关键词
D-InSAR; AFID; IDPIM-H; Refined dynamic subsidence monitoring; RADAR; MODEL;
D O I
10.1038/s41598-024-76037-1
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Continuous exploitation in mining areas damages the surrounding environment and has various severe geological impacts. Hence, long-term monitoring of mining areas is crucial to reducing these impacts. Differential interferometric synthetic aperture radar (D-InSAR) is widely applied to monitor the subsidence in mining areas, but it cannot obtain accurate large-gradient subsidence result in the centre of the subsidence basin in mining areas due to the de-coherence phenomenon. The probability integral method (PIM) is a prediction method that can cooperate with D-InSAR (D-InSAR PIM, DPIM) to solve the problem. However, with this method, there is early convergence of the edge subsidence in narrow and long mining areas. Moreover, the PIM can only predict the spatial domain; it cannot achieve dynamic prediction. To address the above problems in the traditional DPIM data processing process, in this study, firstly, the traditional PIM was improved by adjusting the radius of the parameter and constructed an improved DPIM (IDPIM) method. The hybrid algorithm was applied to solve the parameters of the IDPIM method and then acquire subsidence results, thus solving the early convergence of edge subsidence problem characteristic of traditional PIM prediction in mining. Additionally, an area-weighting based fusion method was proposed to integrate the IDPIM results and the D-InSAR results (Area-weighting based fusion of the IDPIM and D-InSAR results, AFID) achieving whole-basin refined subsidence in mining areas. Secondly, based on a summary of subsidence laws in mining areas, the Hossfeld model was introduced and combined with the IDPIM method (IDPIM Hossfeld, IDPIM-H) to construct a subsidence dynamic prediction method. This achieved dynamic prediction of the subsidence in mining areas. A coal mine in Ordos was used as the study area, and the feasibility of the IDPIM method, the AFID method and the DPIM-H method was verified through a comparative analysis of leveling data. The results demonstrated that: (1) The results of the IDPIM method showed 8% and 66% improvement in RMSE along the striking and dip lines, respectively, over the D-InSAR results, improving the early convergence of the DPIM along the dip direction of the mine. (2) The results of the AFID method provide a 69% improvement in whole-basin RMSE over the D-InSAR results, which improves the lack of monitoring capacity in the D-InSAR technology center. (3) The results of the DPIM-H method provide a 35% improvement in basin-wide RMSE over the D-InSAR results, solving the problem of low temporal resolution of the D-InSAR technology and realizing the dynamic prediction with high temporal resolution. These findings provide a theoretical basis for future refined exploration of dynamic subsidence in mining areas
引用
收藏
页数:17
相关论文
共 50 条
  • [1] Subsidence monitoring using D-InSAR and probability integral prediction modelling in deep mining areas
    Fan, H. D.
    Cheng, D.
    Deng, K. Z.
    Chen, B. Q.
    Zhu, C. G.
    SURVEY REVIEW, 2015, 47 (345) : 438 - 445
  • [2] Detailed mining subsidence monitoring combined with InSAR and probability integral method
    Chen Yang
    Tao QiuXiang
    Liu GuoLin
    Wang LuYao
    Wang FengYun
    Wang Ke
    CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2021, 64 (10): : 3554 - 3566
  • [3] Monitoring and Prediction of Surface Subsidence in Mining Areas by Integrating SBAS-InSAR and ELM
    Gao N.
    Pu Q.
    Journal of Engineering Science and Technology Review, 2024, 17 (01) : 45 - 53
  • [4] Prediction Method for Dynamic Subsidence Basin in Mining Area Based on SBAS-InSAR and Time Function
    Hu, Jibiao
    Yan, Yueguan
    Dai, Huayang
    He, Xun
    Lv, Biao
    Han, Meng
    Zhu, Yuanhao
    Zhang, Yanjun
    REMOTE SENSING, 2024, 16 (11)
  • [5] A Dynamic Prediction Method of Deep Mining Subsidence Combines D-InSAR Technique
    Wang XunChun
    Zhang Yue
    Jiang XingGe
    Zhang Peng
    2011 3RD INTERNATIONAL CONFERENCE ON ENVIRONMENTAL SCIENCE AND INFORMATION APPLICATION TECHNOLOGY ESIAT 2011, VOL 10, PT C, 2011, 10 : 2533 - 2539
  • [6] SBAS-InSAR monitoring method of ground subsidence in mining areas by fusion with measured data
    Chai, Huabin
    Hu, Jibiao
    Geng, Sijia
    Meitan Xuebao/Journal of the China Coal Society, 2021, 46 : 17 - 24
  • [7] FUSION OF INSAR AND BDS FOR SUBSIDENCE DEFORMATION MONITORING IN MINING AREAS: SISTEM AND KALMAN FILTERING METHOD
    Ye Yu
    Chen Huaixin
    Wang Haoyu
    Ren Chengjie
    Bai Chengwu
    2022 19TH INTERNATIONAL COMPUTER CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (ICCWAMTIP), 2022,
  • [8] Deriving Dynamic Subsidence of Coal Mining Areas Using InSAR and Logistic Model
    Yang, Zefa
    Li, Zhiwei
    Zhu, Jianjun
    Yi, Huiwei
    Hu, Jun
    Feng, Guangcai
    REMOTE SENSING, 2017, 9 (02)
  • [9] Surface dynamic subsidence prediction method based on mining sufficiency degree
    Li Q.
    Guo J.
    Dai H.
    Meitan Xuebao/Journal of the China Coal Society, 2020, 45 (01): : 160 - 167
  • [10] An Efficient and Fully Refined Deformation Extraction Method for Deriving Mining-Induced Subsidence by the Joint of Probability Integral Method and SBAS-InSAR
    Liu, Hui
    Yuan, Mingze
    Li, Mei
    Li, Ben
    Zhang, Haoyuan
    Wang, Jinzheng
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2023, 61