LI Tao, ZOU Yingjie, FAN Hongdong, et al. InSAR mining subsidence basin detection method based on DBD-Net[J]. Safety in Coal Mines, 2024, 55(4): 177−186. DOI: 10.13347/j.cnki.mkaq.20230593
    Citation: LI Tao, ZOU Yingjie, FAN Hongdong, et al. InSAR mining subsidence basin detection method based on DBD-Net[J]. Safety in Coal Mines, 2024, 55(4): 177−186. DOI: 10.13347/j.cnki.mkaq.20230593

    InSAR mining subsidence basin detection method based on DBD-Net

    More Information
    • Received Date: April 27, 2023
    • Revised Date: June 28, 2023
    • At present, the detection of mining subsidence basins by interferometric synthetic aperture radar (InSAR) mainly relies on underground mining data or human visual interpretation. To solve this problem, this paper proposes a deformation basin detection network (DBD-Net) for large-scale InSAR interferograms. At the same time, in order to train the network, a sample database of mining subsidence basins is established by using real differential interferogram data and simulated interferogram data. In Shendong Mining Area and Yanzhou Mining Area, three differential interference images with different time baselines were selected to verify the network performance. The results show that the detection accuracy of deformation basin detection network (DBD-Net) in large-scale InSAR interferograms for mining subsidence basins is 81.87%. Most of the missed and false detection areas are areas with serious noise pollution and unclear characteristics.

    • [1]
      钱鸣高. 煤炭的科学开采[J]. 煤炭学报,2010,35(4):529−534.

      QIAN Minggao. On sustainable coal mining in China[J]. Journal of China Coal Society, 2010, 35(4): 529−534.
      [2]
      朱建军,李志伟,胡俊. InSAR变形监测方法与研究进展[J]. 测绘学报,2017,46(10):1717−1733.

      ZHU Jianjun, LI Zhiwei, HU Jun, et al. Research progress and methods of InSAR for deformation monitoring[J]. Acta Geodaetica Et Cartographica Sinica, 2017, 46(10): 1717−1733.
      [3]
      FAN H D, LU L, YAO Y H. Method combining probability integration model and a small baseline subset for time series monitoring of mining subsidence[J]. Remote Sensing, 2018, 10(9): 1444. doi: 10.3390/rs10091444
      [4]
      YANG Z F, LI Z W, ZHU J J, et al. Locating and defining underground goaf caused by coal mining from space-borne SAR interferometry[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 135: 112−126. doi: 10.1016/j.isprsjprs.2017.11.020
      [5]
      赵立峰,范洪冬,渠俊峰,等. 基于DS-InSAR的张双楼煤矿长时序地表形变监测方法[J]. 金属矿山,2021(8):142−149.

      ZHAO Lifeng, FAN Hongdong, QU Junfeng, et al. Long time-series surface deformation monitoring method of Zhangshuanglou coal mine based on DS-InSAR[J]. Metal Mine, 2021(8): 142−149.
      [6]
      DU S, WANG Y J, ZHENG M N, et al. Goaf locating based on InSAR and probability integration method[J]. Remote Sensing, 2019, 11(7): 812. doi: 10.3390/rs11070812
      [7]
      邹英杰,范洪冬,孙叶,等. 基于SAR技术的开采沉陷全盆地分区形变提取方法[J]. 煤矿安全,2022,53(11):229−235.

      ZOU Yingjie, FAN Hongdong, SUN Ye, et al. A method for extracting sub-regional deformation of mining subsidence based on SAR technology[J]. Safety in Coal Mines, 2022, 53(11): 229−235.
      [8]
      陈筠力,李威. 国外SAR卫星最新进展与趋势展望[J]. 上海航天,2016,33(6):1−19.

      CHEN Junli, LI Wei. Recent advances and trends of SAR satellites in foreign countries[J]. Aerospace Shanghai, 2016, 33(6): 1−19.
      [9]
      FAN H D, GAO X X, YANG J K, et al. Monitoring mining subsidence using a combination of phase-stacking and offset-tracking methods[J]. Remote Sensing, 2015, 7(7): 9166−9183. doi: 10.3390/rs70709166
      [10]
      冯春凤,范洪冬,温斌繁,等. 基于堆叠稀疏自动编码器的SAR图像变化检测[J]. 激光杂志,2018,39(11):29−33.

      FENG Chunfeng, FAN Hongdong, WEN Binfan, et al. Change detection of SAR images based on stacked sparse automatic encoder[J]. Laser Journal, 2018, 39(11): 29−33.
      [11]
      HAO M, ZHOU M C, CAI L P. An improved graph-cut-based unsupervised change detection method for multispectral remote sensing images[J]. International Journal of Remote Sensing, 2021, 42(11): 4005−4022. doi: 10.1080/01431161.2021.1881182
      [12]
      冯文彬,厉舒南,田昊,等. 基于融合边缘优化的煤矿图像语义分割方法[J]. 煤矿安全,,2022,53(2):136−141

      FENG Wenbin, LI Shunan, TIAN Hao, et al. Images semantic segmentation method based on fusion edge optimization[J].Safety in Coal Mines, 2022, 53(2): 136−141.
      [13]
      李腾腾. 基于SAR/InSAR的地下煤炭采空区特征反演方法研究[D]. 徐州:中国矿业大学,2022.
      [14]
      WU Z P, WANG T, WANG Y J, et al. Deep learning for the detection and phase unwrapping of mining-induced deformation in large-scale interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1−18.
      [15]
      SPOORTHI G E, GORTHI S, GORTHI R K. PhaseNet: A deep convolutional neural network for two-dimensional phase unwrapping[J]. IEEE Signal Processing Letters, 2019, 26(1): 54−58. doi: 10.1109/LSP.2018.2879184
      [16]
      王志勇,李路,王建,等. 基于HOG特征的InSAR矿区开采沉陷盆地检测方法[J]. 中国矿业大学学报,2021,50(2):404−410.

      WANG Zhiyong, LI Lu, WANG Jian, et al. A method of detecting the subsidence basin from InSAR interferogram in mining area based on HOG features[J]. Journal of China University of Mining & Technology, 2021, 50(2): 404−410.
      [17]
      宋俊贤. 基于U-Net的雷达差分干涉影像采煤沉陷区提取方法及应用研究[D]. 北京:中国地质大学(北京),2021.
      [18]
      LI S, XU H P, GAO S, et al. An interferometric phase noise reduction method based on modified denoising convolutional neural network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 4947−4959. doi: 10.1109/JSTARS.2020.3017808
      [19]
      ALI Z, IRTAZA A, MAQSOOD M. An efficient U-Net framework for lung nodule detection using densely connected dilated convolutions[J]. Journal of Supercomputing, 2022, 78(2): 1602−1623. doi: 10.1007/s11227-021-03845-x
      [20]
      ZHANG K, ZUO W M, CHEN Y J, et al. Beyond a gaussian denoiser: residual learning of deep CNN for image denoising[J]. IEEE Transactions on Image Processing, 2017, 26(7): 3142−3155. doi: 10.1109/TIP.2017.2662206
      [21]
      张兵,崔希民. 开采沉陷动态预计的分段Knothe时间函数模型优化[J]. 岩土力学,2017,38(2):541-548.

      ZHANG Bing, CUI Ximin. Optimization of segmented Knothe time function model for dynamic prediction of mining subsidence[J]. Rock and Soil Mechanics, 2017, 38(2): 541-548.
      [22]
      张兵,赵玉玲,崔希民,等. 基于优化时间函数的采动地表任意点沉陷动态预计[J]. 煤炭科学技术,2020,48(10):143−149.

      ZHANG Bing, ZHAO Yuling, CUI Ximin, et al. Dynamic prediction of mining-induced subsidence for any surface point based on optimized time function[J]. Coal Science and Technology, 2020, 48(10): 143−149.
    • Related Articles

      [1]LYU Pengfei, LIU Tianqi, BAO Xinyang, LI Xuping. Numerical Analysis of Dynamic Instability Response Characteristics in Roadways Triggered by Shock Wave[J]. Safety in Coal Mines, 2020, 51(9): 240-244.
      [2]YANG Haobo. Deformation Characteristics and Supporting Parameters Optimization of Broken Surrounding Rock Roadway[J]. Safety in Coal Mines, 2019, 50(9): 160-163.
      [3]XU Junjian. Deformation Characteristics and Supporting Measures of Secondary Dynamic Pressure Roadway with Large Section[J]. Safety in Coal Mines, 2018, 49(10): 159-162.
      [4]CHEN Shan, MA Xiaoli. Numerical Simulation Research on Large Cross Section T-type Roadway Surrounding Rock Intersection Instability Control[J]. Safety in Coal Mines, 2016, 47(11): 61-64.
      [5]WANG Gensheng, ZHANG Wei, ZHU Youheng, WANG Donglin, ZHANG Mingpeng. Instability Mechanism and Supporting Contro Technologyl of Soft Thick Roof in Large Section Coal Roadway[J]. Safety in Coal Mines, 2016, 47(7): 170-173.
      [6]ZHANG He. Support Design and Failure Characteristics of Mudstone-roof Gateway in Deep Mine[J]. Safety in Coal Mines, 2014, 45(9): 129-132.
      [7]HUANG Youhong, MA Haifeng, YANG Zhao. Deformation Characteristic and Support Technology of Extra-wide Mining Roadway[J]. Safety in Coal Mines, 2014, 45(7): 70-73.
      [8]XU Shou-ren, WANG Hong-xia, ZHANG Yuan-yuan. Study and Application of Roadway Support Technology in Soft Coal Seam[J]. Safety in Coal Mines, 2012, 43(12): 97-99.
      [9]ZHANG Shuan-cai. 强矿压小煤柱分层掘进巷道高强联合支护技术[J]. Safety in Coal Mines, 2012, 43(10): 86-88.
      [10]HAN Wen-mei, CHEN Jun-sheng. Stress State Analysis of Mine Roadway Support[J]. Safety in Coal Mines, 2012, 43(7): 185-187.
    • Cited by

      Periodical cited type(5)

      1. 李鹏. 基于FLAC3D模拟的14103辅运顺槽支护参数设计. 江西煤炭科技. 2025(01): 90-92+96 .
      2. 马清水,郭瑞,韩伟,宋永明,梁燕翔,刘耀,王佳明. 基于长短期记忆网络的煤巷支护设计参数预测. 能源与环保. 2025(02): 235-240 .
      3. 李迎富,宋武,杨子仪,赵江梅,曾凡刚. 掘进巷道锚杆支护优化研究. 煤矿机械. 2024(05): 116-118 .
      4. 陈万辉,郭瑞,韩伟,宋永明,梁燕翔,刘耀,王佳明,许娜,孟波. 煤矿巷道支护方案智能设计研究. 工矿自动化. 2024(08): 76-83+90 .
      5. 彭启友,缪易辰,潘文,尚江淮. 吸能锚杆数值模拟研究现状与展望. 建筑技术. 2024(20): 2546-2551 .

      Other cited types(1)

    Catalog

      Article views (22) PDF downloads (5) Cited by(6)

      /

      DownLoad:  Full-Size Img  PowerPoint
      Return
      Return