基于探地雷达的路基地下异常体全波形反演

胡磊磊, 陈康, 黄德军, 杨荣

胡磊磊,陈康,黄德军,等. 基于探地雷达的路基地下异常体全波形反演[J]. 煤田地质与勘探,2022,50(11):195−202. DOI: 10.12363/issn.1001-1986.22.02.0066
引用本文: 胡磊磊,陈康,黄德军,等. 基于探地雷达的路基地下异常体全波形反演[J]. 煤田地质与勘探,2022,50(11):195−202. DOI: 10.12363/issn.1001-1986.22.02.0066
HU Leilei,CHEN Kang,HUANG Dejun,et al. Subgrade subsurface anomalous body full-waveform inversion based on ground-penetrating radar[J]. Coal Geology & Exploration,2022,50(11):195−202. DOI: 10.12363/issn.1001-1986.22.02.0066
Citation: HU Leilei,CHEN Kang,HUANG Dejun,et al. Subgrade subsurface anomalous body full-waveform inversion based on ground-penetrating radar[J]. Coal Geology & Exploration,2022,50(11):195−202. DOI: 10.12363/issn.1001-1986.22.02.0066

 

基于探地雷达的路基地下异常体全波形反演

基金项目: 广西地方标准制修订项目(2022-1505)
详细信息
    作者简介:

    胡磊磊,1986年生,男,广西都安人,高级工程师,从事工程地球物理勘察研究工作. E-mail:2390041@qq.com

    通讯作者:

    陈康,1965年生,男,广西柳州人,高级工程师,从事工程物探、矿产物探及相关技术管理工作. E-mail:chenkang_gsi@163.com

  • 中图分类号: P631

Subgrade subsurface anomalous body full-waveform inversion based on ground-penetrating radar

  • 摘要:

    路面塌陷及地下空洞隐患往往较为隐蔽且事发突然,造成了人们生命和财产的巨大损失,对于道路塌陷及地下空洞隐患的检测分析显得至关重要。探地雷达(Groud Penetrating Radar, GPR)因其具有精度高、效率快、连续无损、实时成像等优点,是目前城市道路塌陷隐患探测的主要方法。针对GPR传统目标函数全波形反演(Full Waveform Inversion, FWI)中激励源子波估计不准确而导致反演准确性和可靠性降低的问题,提出了一种褶积型目标函数FWI算法。对于路面塌陷及地下空洞2种情况,通过建立合成数据模型,与传统目标函数FWI的反演结果进行对比,说明了褶积型目标函数FWI算法在激励源子波估计不准确的情况下依然可以得到良好的反演结果,验证了该算法的有效性;最后将该算法用于2组不同灾害类型的GPR实测数据中,分析反演得到的地下介质相对介电常数分布情况,验证了褶积型目标函数FWI算法对于实测数据的实用性,从而为路基地下异常体探测提供理论依据。

    Abstract:

    The hidden dangers of road collapse and underground voids are usually difficult to notice and easy to occur in sudden. They have caused huge losses in people's lives and properties. Therefore, the detection and analysis of road collapse and underground voids are crucial. The ground-penetrating radar (GPR) is the main method for detecting the hidden dangers of urban road collapse currently by virtue of its high accuracy, high efficiency, continuous non-destructiveness and real-time imaging. A convolution type objective function FWI algorithm was proposed to solve the problem of the lower inversion accuracy and practicability caused by the inaccurate estimation of GPR conventional objective function full-waveform inversion (FWI). For the two situations, i.e., road collapse and underground voids, synthetic data models were established, and compared with the inversion results obtained using the conventional objective function FWI, which indicated that the convolution objective function FWI algorithm can still provide favorable inversion results under the condition of inaccurate excitation source estimation. Accordingly, the effectiveness of the algorithm was verified. Finally, the algorithm was used in the GPR measured data of the two groups of different defect types, and the relative dielectric constant distribution of the underground medium was obtained through analysis and inversion, which verified the practicability of the convolution type objective function FWI algorithm for measured data. In this way, the theoretical basis related to the underground anomalous body detection for the subgrade was provided, and guidance was offered for practical production.

  • 图  1   路面塌陷异常相对介电常数分布

    Fig.  1   Relative dielectric constant distribution in case of road collapse exceptions

    图  2   路面塌陷异常正演剖面

    Fig.  2   Forward section of road collapse exceptions

    图  3   路面塌陷异常传统目标函数和褶积型目标函数FWI结果对比

    Fig.  3   Comparison between results from conventional objective function and convolution type objective function

    图  4   路面塌陷异常FWI重构误差曲线

    Fig.  4   Error curve of ground surface collapse model reconstruction

    图  5   地下空洞异常相对介电常数分布

    Fig.  5   Relative dielectric constant distribution in case of underground void exceptions

    图  6   路面塌陷异常正演剖面

    Fig.  6   Forward section of road collapse exceptions

    图  7   褶积型目标函数FWI结果

    Fig.  7   Convolution type objective function FWI result

    图  8   地下复杂介质模型重构误差曲线

    Fig.  8   Error curve of reconstruction for underground complex medium models

    图  9   实测路面现场工作记录

    Fig.  9   Field work record for measured pavement

    图  10   实测路面的GPR剖面

    Fig.  10   GPR profile of measured pavement

    图  11   实测数据全波形反演介电常数分布

    Fig.  11   Dielectric constant distribution in full-waveform inversion for measured data

    图  12   实测路面GPR剖面

    Fig.  12   GPR profile of measured pavement

    图  13   实测数据全波形反演介电常数分布

    Fig.  13   Dielectric constant distribution in full-waveform inversion for measured data

    表  1   合成数据全波形反演效率对比

    Table  1   Comparison of full-waveform inversion efficiency for synthetic data

    算法网格大小使用道数记录时长/ns迭代次数单次迭代耗时/s总耗时/s
    传统算法120×300300302045.234 31 885.363 8
    本文算法120×300300302047.234 61 269.668 5
    下载: 导出CSV

    表  2   合成数据全波形反演效率

    Table  2   Full-waveform inversion efficiency for synthetic data

    网格大小使用道数记录时长/ns迭代次数单次迭代耗时/s总耗时/s
    200×4002004020156.834 04 186.338 7
    下载: 导出CSV

    表  3   实测数据全波形反演效率

    Table  3   Full-waveform inversion efficiency for measured data

    网格大小使用道数记录时长/ns迭代次数单次迭代耗时/s总耗时/s
    实测1200×200200404035.590 21 502.007 3
    实测2200×4004006040247.106 411 723.320 0
    下载: 导出CSV
  • [1]

    FENG Deshan,CAO Cen,WANG Xun. Multiscale full–waveform dual–parameter inversion based on total variation regularization to on−ground GPR data[J]. IEEE Transactions on Geoscience and Remote Sensing,2019,57(11):9450−9465. DOI: 10.1109/TGRS.2019.2926626

    [2]

    BIANCHINI C L,TOSTI F,ECONOMOU N,et al. Signal Processing of GPR Data for Road Surveys[J]. Geosciences,2019,9(2):96. DOI: 10.3390/geosciences9020096

    [3]

    HU Da,LI Shuai,CHEN Junjie,et al. Detecting,locating,and characterizing voids in disaster rubble for search and rescue[J]. Advanced Engineering Informatics,2019,42:100974. DOI: 10.1016/j.aei.2019.100974

    [4] 许献磊,杨峰,乔旭,等. 基于GPR的城市道路地下病害差值检测方法研究[J]. 科学技术与工程,2016,16(12):83−88. DOI: 10.3969/j.issn.1671-1815.2016.12.014

    XU Xianlei,YANG Feng,QIAO Xu,et al. Research on the difference detection method of the disease in the roadbed based on GPR[J]. Science Technology and Engineering,2016,16(12):83−88. DOI: 10.3969/j.issn.1671-1815.2016.12.014

    [5] 曾雄鹰, 王佳龙, 梁晓东, 等. 基于双频高动态探地雷达技术的道路地下病害检测研究[J/OL]. 地球物理学进展, 2022: 1–12 [2022-07-19]. http://kns.cnki.net/kcms/detail/11.2982.P.20220316.2200.072.html.

    ZENG Xiongying, WANG Jialong, LIANG Xiaodong, et al. Research on road underground disease detection by dual–frequency GPR based on high–dynamic range technology[J/OL]. Progress in Geophysics, 2022: 1−12 [2022-07-19]. http://kns.cnki.net/kcms/detail/11.2982.P.20220316.2200.072.html.

    [6] 吴旭东,何文勇,龙万学,等. 基于探地雷达的路基病害正演模拟及分析[J]. 中外公路,2020,40(增刊2):105−109.

    WU Xudong,HE Wenyong,LONG Wanxue,et al. Forward simulation and analysis of roadbed diseases based on ground penetrating radar[J]. Journal of China and Foreign Highway,2020,40(Sup.2):105−109.

    [7] 郭士礼,段建先,张建锋,等. 探地雷达在城市道路塌陷隐患探测中的应用[J]. 地球物理学进展,2019,34(4):1609−1613. DOI: 10.6038/pg2019CC0438

    GUO Shili,DUAN Jianxian,ZHANG Jianfeng,et al. Application of GPR in urban road hidden diseases detection[J]. Progress in Geophysics (in Chinese),2019,34(4):1609−1613. DOI: 10.6038/pg2019CC0438

    [8] 金光来,臧国帅,蔡文龙,等. 基于探地雷达的路面结构完整性定量化评价方法[J]. 公路,2020,65(5):16−20.

    JIN Guanglai,ZANG Guoshuai,CAI Wenlong,et al. Quantitative evaluation method of pavement structure integrity based on ground penetrating radar[J]. Highway,2020,65(5):16−20.

    [9]

    RODES J P,MARTŃEZ A,PÉREZ–GRACIA V. GPR spectra for monitoring asphalt pavements[J]. Remote Sensing,2020,12(11):1749. DOI: 10.3390/rs12111749

    [10]

    RASOL M A,PÉREZ–GRACIA V,FERNANDES F M,et al. GPR laboratory tests and numerical models to characterize cracks in cement concrete specimens,exemplifying damage in rigid pavement[J]. Measurement,2020,158:107662. DOI: 10.1016/j.measurement.2020.107662

    [11] 王超,林振荣,李洁. HHT在探地雷达检测路基质量中的应用[J]. 地球物理学进展,2021,36(4):1711−1716. DOI: 10.6038/pg2021EE0173

    WANG Chao,LIN Zhenrong,LI Jie. Application of Hilbert–Huang transform in detecting the quality of roadbed by ground penetrating radar[J]. Progress in Geophysics (in Chinese),2021,36(4):1711−1716. DOI: 10.6038/pg2021EE0173

    [12]

    CIAMPOLI L B,TOSTI F,ECONOMOU N,et al. Signal processing of GPR data for road surveys[J]. Geosciences,2019,9(96):1−20.

    [13]

    . LIU Tao, KLOTZSCHE A, PONDKULE M, et al. Estimation of subsurface cylindrical object properties from GPR full–waveform inversion[C]//2017 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR), 2017.

    [14]

    KLOTZSCHE A,VEREECKEN H,KRUK J. Review of cross–hole ground–penetrating radar full–waveform inversion of experimental data:Recent developments,challenges,and pitfalls[J]. Geophysics,2019,84(6):H13−H28. DOI: 10.1190/geo2018-0597.1

    [15]

    JAZAYERI S,KRUSE S,HASAN I,et al. Reinforced concrete mapping using full−waveform inversion of GPR data[J]. Construction and Building Materials,2019,229:117102. DOI: 10.1016/j.conbuildmat.2019.117102

    [16]

    KLOTZSCHE A,VEREECKEN H,KRUK J. GPR full–waveform inversion of a variably saturated soil−aquifer system[J]. Journal of Applied Geophysics,2019,170:103823. DOI: 10.1016/j.jappgeo.2019.103823

    [17] 杨涛,张会星,史才旺. 不依赖子波的弹性波混合域全波形反演[J]. 石油地球物理勘探,2019,54(2):348−355.

    YANG Tao,ZHANG Huixing,SHI Caiwang. Wavelet−independent elastic wave full waveform inversion in hybrid domain[J]. Oil Geophysical Prospecting,2019,54(2):348−355.

    [18] 李庆洋,黄建平,李振春. 基于Student’s t分布的不依赖子波最小二乘逆时偏移[J]. 地球物理学报,2017,60(12):4790−4800. DOI: 10.6038/cjg20171220

    LI Qingyang,HUANG Jianping,LI Zhenchun. Source–independent least–squares reverse time migration using Student’s t distribution[J]. Chinese Journal of Geophysics (in Chinese),2017,60(12):4790−4800. DOI: 10.6038/cjg20171220

    [19]

    LEI Jianwei,WANG Zibin,FANG Hongyuan,et al. Analysis of GPR wave propagation in complex underground structures using CUDA–implemented conformal FDTD method[J]. International Journal of Antennas and Propagation,2019,2019:1−11.

    [20] 冯德山,杨良勇,王珣. 探地雷达FDTD数值模拟中不分裂卷积完全匹配层对倏逝波的吸收效果研究[J]. 地球物理学报,2016,59(12):4733−4746. DOI: 10.6038/cjg20161232

    FENG Deshan,YANG Liangyong,WANG Xun. The unsplit convolutional perfectly matched layer absorption performance analysis of evanescent wave in GPR FDTD forward modeling[J]. Chinese Journal of Geophysics (in Chinese),2016,59(12):4733−4746. DOI: 10.6038/cjg20161232

    [21]

    ÖZKAYA U,ÖZTŰRK Ş,MELGANI F,et al. Residual CNN+ Bi–LSTM model to analyze GPR B scan images[J]. Automation in Construction,2021,123:103525. DOI: 10.1016/j.autcon.2020.103525

图(13)  /  表(3)
计量
  • 文章访问数:  371
  • HTML全文浏览量:  16
  • PDF下载量:  66
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-02-15
  • 修回日期:  2022-08-25
  • 网络出版日期:  2022-11-04
  • 刊出日期:  2022-11-24

目录

    /

    返回文章
    返回