ISSN 1003-8035 CN 11-2852/P

    光纤传感技术在地质安全监测中的应用

    张晨阳, 刘志谦, 陈国旭, 赵奇, 梁日晖, 谭道远, 陈之毅, 黄雨

    张晨阳，刘志谦，陈国旭，等. 光纤传感技术在地质安全监测中的应用[J]. 中国地质灾害与防治学报，2025，36(2): 1-17. DOI: 10.16031/j.cnki.issn.1003-8035.202501015
    引用本文: 张晨阳，刘志谦，陈国旭，等. 光纤传感技术在地质安全监测中的应用[J]. 中国地质灾害与防治学报，2025，36(2): 1-17. DOI: 10.16031/j.cnki.issn.1003-8035.202501015
    shu
    ZHANG Chenyang，LIU Zhiqian，CHEN Guoxu，et al. Application of fiber optic sensing technology in geological safety monitoring[J]. The Chinese Journal of Geological Hazard and Control，2025，36(2): 1-17. DOI: 10.16031/j.cnki.issn.1003-8035.202501015
    Citation: ZHANG Chenyang，LIU Zhiqian，CHEN Guoxu，et al. Application of fiber optic sensing technology in geological safety monitoring[J]. The Chinese Journal of Geological Hazard and Control，2025，36(2): 1-17. DOI: 10.16031/j.cnki.issn.1003-8035.202501015
    shu

    光纤传感技术在地质安全监测中的应用

    • 1.

      香港理工大学，土木及环境工程学系

    • 2.

      南京大学，地球科学与工程学院

    • 3.

      同济大学，土木工程学院, 地下建筑与工程系

    Application of fiber optic sensing technology in geological safety monitoring

    • 1.

      Hong Kong Polytechnic University, Department of Civil and Environmental Engineering

    • 2.

      School of Earth Science and Engineering, Nanjing University

    • 3.

      Tongji University, School of Civil Engineering, Department of Underground Architecture and Engineering

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      摘要
    • 摘要:

      全球气候变化带来的地质灾害风险不断加剧，有效的监测与预警是减轻地质灾害影响的关键。本文旨在探讨光纤传感技术在地质安全监测中的应用与发展。通过文献综述的方法，系统梳理了光纤传感技术的原理及其在滑坡灾害、隧道灾害和地震监测三个领域中的应用进展。综述表明，光纤传感技术能够实时监测应变、温度、振动、水、气等数据，为地质灾害的预警和防治提供重要支持。此外，该技术能够同时获取多种物理参数的耦合信息，增强了对复杂环境中地质体失稳机制的理解及防护结构安全性的评估。尽管光纤传感技术在监测中表现出色，但在光纤布设、数据处理和实时预警系统的优化方面仍面临挑战。未来的发展应着重提高光纤监测系统的耐用性及有效性，进一步结合人工智能技术进行数据分析，以提高地质灾害判识及预警模型的有效性及监测系统的智能化水平。

      Abstract:

      The rapid development of global industrialization and urbanization, coupled with the dramatic impact of climate change, has significantly increased the risk of geological disasters. Effective monitoring and early warning systems are essential for mitigating the impact of these hazards. This paper explores the application and development of fiber optic sensing technology in geological safety monitoring. Through a systematic literature review, the principles of fiber optic sensing technology and its progress in the application within three areas—landslide disasters, tunnel disasters, and seismic monitoring—are examined. The review indicates that fiber optic sensing technology can monitor strain, temperature, vibration, fluid, and gas characteristics in real time, providing crucial support for the early warning and mitigation of geological hazards. Additionally, this technology can capture coupled information of various physical parameters simultaneously, enhancing the understanding of the destabilization mechanisms in geological bodies under complex environments and the assessment of protective structure safety. Despite its excellent performance in geological hazard monitoring, fiber optic sensing technology faces challenges in optimizing fiber optic layout, data processing, and real-time early warning systems. Future developments should focus on improving the durability and effectiveness of fiber optic monitoring systems and integrating artificial intelligence to enhance the accuracy of geological hazard identification and prediction models, as well as the intelligence level of monitoring systems.

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    • 0.   引言

      由碎石土、尾矿砂等颗粒材料堆积而成的松散体在自然界和工业生产活动中广泛存在，普遍具有结构松散、孔隙度大、颗粒间结合力差等特点，有复杂的力学性质和相对较高的失稳风险。受自然条件和人类活动等影响，松散体在斜坡地形中更易发生地质灾害。因此如何有效预测斜坡地形松散体边坡滑移破坏的过程对于矿山的生产安全和实际工程项目的实施具有重大意义。目前松散体边坡的监测方式主要有位移监测和变形监测，包含光学遥感、三维激光扫描、GPS、RS等技术[1 − 5]。但以上技术都存在明显局限性，监测范围有限并且对边坡内部变形和滑坡孕育过程情况不能及时掌握。声发射（acoustic emission，AE）是基于声波的发射和检测，并且和检测对象内部结构的断裂或者变形息息相关。由于松散体滑移破坏过程中，其内部松散颗粒间的相互作用力也会产生弹性波，这使得AE技术能够识别其信号，及时发现和记录边坡破坏的发展过程，在滑坡灾害前做出有效的预警。因此，AE技术在近年来的边坡稳定性监测中逐渐得到更多关注。

      近年来，针对松散体滑坡的研究重点从滑坡监测技术的相关话题逐渐转向滑坡变形的监测预警和边坡稳定性的相关评估研究[6 − 9]。Codeglia等[10]提出一种利用AE监测边坡稳定性的思路，认为AE趋势与内部应力、外部斜坡载荷等有关。Berg等[11]在前人研究基础上在加拿大的和平河地区进行了声发射实地试验。Hu等[12]提出了用声发射和微震联合监测的方法，并获得了更准确的预测结果。此外，不少学者一直以来针对松散体边坡的组成物质开展室内试验，探寻其在不同试验过程中声发射信号特征，Chen等[13]对BFRP混凝土粘结滑移破坏过程特征进行了研究。Deng等[14]尝试搭建用于滑坡监测的声发射阵列系统，并进行了相关试验。李文彪等[15]将声发射用于松散颗粒介质边坡或路堤稳定性研究。胡训健等[16]模拟了单轴压缩实验中细观结构的非均质性对岩石颗粒的声发射特性的影响。吴鑫等[17]研究了不同剪切速率下松散颗粒的声发射信号特征。

      综上所述，目前关于松散体边坡监测相关研究已取得一定进展，但是其滑移过程的声学规律研究仍不充分。因此本研究将搭建斜坡试验平台，开展松散体模拟滑坡试验，并考虑连续改变装置倾角，监测松散体静止、蠕变到滑移整个过程的声发射信号演化规律，为松散体边坡稳定性监测提供研究思路和数据支撑。

      1.   试验设计

      采用中国ISO标准砂（GSB08-1337）作为试验材料，利用高频振筛机筛分出0.5～1 mm的砂粒作为试验原材料，试件尺寸Φ61.8×40 mm。试验装置如图1—2所示，主要由升降装置、滑坡装置和声发射数据采集系统3个部分组成。

      图  1  装置设计图
      Figure  1.  Device design diagram
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      图  2  装置实景图
      Figure  2.  Actual view of the installation
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      升降装置由滑轮、固定物和绳子组成，滑轮采用不锈钢材质，长度为85 mm，滑轮轮直径为25 mm。滑坡装置由2个透明的0.3 m×0.3 m×0.8 m的空心长方体模型箱组成。滑坡装置的滑道分为2层，底层作为表面层，将试验砂均匀地粘在倾斜滑道上来模拟自然条件下的坡面；上层为堆积层，将试验砂均匀堆放在底层上形成堆积层来模拟松散体。

      声发射系统为北京软岛DS5-16B多通道声发射仪，采样频率为3MHz，默认触发阈值为100 mV，频率范围为100～400 kHz，模拟滤波器信号设为直通，储存设置为波形。在滑坡装置安装3个陶瓷压电传感器全程记录声发射信号参数与波形信息。传感器型号是RS-2A，尺寸为Φ18.8×15 mm，接口类型为M5-KY，放大器为40 dB。该声发射仪能够实现声发射信号的采集、回放、波形处理和声源定位，并基于阈值法获取时差，进而采用穷举算法进行定位。同时安装2个30 fps、分辨率为1080 p的高清摄像头记录试验过程。如图1所示传感器设置在倾斜箱体的300，600 mm处的底部背面和水平箱体位置200 mm处。摄像头和倾斜箱体的传感器位置相互对应，在其上方300 mm处。

      试验开始前将倾斜滑道与水平面的初始高度设置为450 mm，并将400 g粒径分布为0.5～1 mm的标准砂均匀堆积在倾斜滑道上，以模拟自然条件斜坡地形上的松散体。在堆放标准砂时要保持滑道上的堆积层不会发生滑移现象。试验装置起始角度为34°，试验过程倾角变化范围在34°～39°。试验开始后，保持0.75 mm/s的速度启动升降装置，在上升过程中由于倾角不断地发生变化使得松散体经历静止，蠕变到滑移整个过程。当松散体滑移过程结束后即停止试验同时停止采集相关数据，通过声发射传感器和高清摄像头分别采集得到松散体的整个滑移过程的信号数据和视频图像。通过多组重复试验研究松散体滑移的启动机制、速度变化、变形特征以及滑移前兆。

      本试验进行了3组重复试验，结合布置的2个位置互相对应的声发射探头和高清摄像头一共得到了6组速度数据。各组速度均值分别为0.018，0.013，0.012，0.026，0.011，0.015 m/s，标准差为0.0056，平均误差为0.0041，最大误差为0.0102，变异系数为0.35。变异系数的大小与数据稳定性有着密切的关系。通常情况下，变异系数越小，表示数据集中的观测值越稳定，波动性较小。因此，本试验误差较小，结果较为稳定。

      2.   数据分析

      2.1   数据预处理

      为研究松散体滑移过程中的AE信号规律，选择了2个关键时间节点，如图3所示将整个滑移过程分为3个阶段。时间节点选取为视频观察到的颗粒滑动点和滑移点。I阶段为平稳阶段，对应滑坡过程中的平稳阶段，该阶段无滑动现象；II阶段为蠕变阶段，对应滑坡过程中的滑动阶段和滑移前阶段，该阶段能观察到个别及少数颗粒滑动；III阶段为滑移阶段，对应滑坡中的滑坡阶段，该阶段能观察到较多颗粒滑动甚至宏观滑坡现象。

      图  3  AE数据预处理图
      Figure  3.  AE data preprocessing chart
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      2.2   振铃计数及AE能量阶段分析

      松散体在滑移过程中的AE信号特征是：振铃计数和能量随滑移过程呈现先逐渐增大后衰减，最终趋于稳定。具体表现如图4所示：I阶段时，振铃计数保持不变；在II阶段时，振铃计数逐渐上升，达到较高点后保持平稳，此时颗粒间的相互作用力在不断增加，松散体自身的状态逐渐不稳定，滑移门槛值为5000次，当振铃计数达到门槛值后发生滑移现象。在III阶段振铃计数逐渐衰减。此时松散颗粒重新排列或沉降，从而导致松散体内部结构和稳定性得到改善，使得振铃计数逐渐减小。若发生二次滑移，则振铃计数会有波动变化。在滑移结束时，振铃计数变化逐渐稳定。

      图  4  振铃计数和能量随时间变化图
      Figure  4.  Plot of ring counts and energy over time
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      滑移过程中AE能量释放规律与振铃计数演化规律类似。如图4所示，相比振铃计数，能量的滑移门槛值并不固定，当能量大于1500 mV·ms后，易发生滑移现象。试验发生二次滑移的原因是由于松散颗粒之间的相互作用产生的，遵循Omori定律。其反映的是大的断裂声发射信号引发的次生断裂信号行为。在本试验中是随着滑移过程的进行，松散体内部结构重新发生变化，使得松散体出现新的不稳定状态进而发生二次滑移现象。

      2.3   b值分析

      b值（b-value）是小事件数与大事件数的比值，b值常作为判定裂隙发展情况的参数[17]。b值的计算公式可表示为：

      $$ b=\frac{20\times\mathrm{lge}}{(A-A_{\mathrm{min}})} $$ (1)

      式中：e——自然常数；

      A、$ {A}_{{\mathrm{min}}} $——平均幅值和最小幅值/mV。

      b值在计算时将其门槛值设为0.004；频率设置为3 MHz，将原始数据划分为900组，每组数据集样本数为100000。b值随时间的演化规律如图5所示，具体表现为：b值在滑移过程中逐渐减小，这是由于滑移过程引起的颗粒间的摩擦和力链重排等事件越来越频繁和显著，即大事件比例不断增加。随着滑移过程的进行，b值减小趋势逐渐放缓。b值门槛值为0.2，当b值达到门槛值后易发生滑移现象；在III阶段b值逐渐回升。在滑移结束时，b值变化逐渐稳定。

      图  5  b值和频谱重心随时间变化图
      Figure  5.  Plot of b-value and spectral centre of gravity over time
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      2.4   频谱分析

      采用快速傅里叶变换（fast Fourier transform，FFT）对松散体滑移试验AE信号数据进行处理得到功率谱，进一步获得主频、频谱重心等信息。主频即峰值频率，频谱重心计算公式如下：

      $$ FC=\frac{{\displaystyle\int }_{0}^{+\infty }fP\left(f\right){\mathrm{d}}f}{{\displaystyle\int }_{0}^{+\infty }P\left(f\right){\mathrm{d}}f} $$ (2)

      式中：FC——频谱重心/Hz；

      f——频谱重心的横坐标；

      (f)——信号的功率谱。

      频谱重心随时间的演化规律如图5所示，在I阶段时，频谱重心保持在400 kHz左右，此时松散体内部结构相对稳定，没有明显的内部位移或结构变化。外部应力达到了一个稳定状态，松散体正处于相对静止或受均衡的应力状态。在II阶段，频谱重心在临滑移前期已经有30～50 kHz的降幅，说明在该时期松散体已经开始发生微观结构变化或应力分布变化，从而导致越来越多的颗粒开始出现滑动情况。在此之后频谱重心在350～450 kHz之间进行波动变化，这种波动变化预示着松散体内部结构正在不断发生变化，一般与松散体内部微观裂缝扩展、颗粒重新排列或颗粒间压力变化相关，最终导致了宏观滑坡现象的发生。

      具体表现为：在I阶段时无论是振铃计数、能量、b值、频谱重心还是PIV分析得到的颗粒速度图像曲线均保持相对稳定。在II阶段，振铃计数和能量呈不断上升的趋势，b值不断减小，频谱重心在临滑移前期有30～50 kHz的降幅，而后过程中频谱重心在350～450 kHz之间进行波动变化。频谱重心的震荡时间区域正好对应振铃计数和能量数值相对较高及b值相对较低的时间区域。临近滑移点时，颗粒图像测速法（particle image velocimetry，PIV）速度图像曲线开始缓慢增长。其中，b值变化最早，对松散体的状态变化的更敏感。在III阶段，PIV速度图像曲线先增后减，随着滑移过程结束，各参数变化逐渐归于稳定（图6—7）。

      图  6  声发射结合PIV滑移过程分析图
      Figure  6.  acoustic emission combined with PIV slip process analysis diagrams
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      图  7  各试验组综合分析图
      Figure  7.  Comprehensive analysis charts for each test group
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      此外，还注意到在II阶段时，AE信号变化出现在PIV速度变化之前。当松散体发生滑动或滑移行为前，声发射（AE）参数就已经发生了相应的变化。这段AE参数变化的时间视为松散体滑坡发生前的“窗口期”，并可能将其用于松散体滑坡预测和预警[18 − 20]。

      3.   滑移阶段预测

      AE试验数据提供了关于松散体的状态变化的声学信号，这些信号可以识别松散体滑坡前的“窗口期”，并作为预测依据。考虑选取AE信号参数为预测指标，根据不同阶段AE信号参数对应的规律变化来预测松散体滑移过程的不同阶段[21]。由于b值对松散体状态变化较为敏感，所以选取b值为基本参数，结合各试验组b值演化情况，基于此将松散体滑移过程分为5个阶段。划分依据和计算过程如下：

      $$ {R}_{b}=\left\{\begin{split} &{R}_{b}5 \quad\quad b\leqslant 1\\ &{R}_{b}4 \quad\quad 1 < b\leqslant 3\\ &{R}_{b}3 \quad\quad 3 < b < 4\\ &{R}_{b}2 \quad\quad 4\leqslant b < 5\\ &{R}_{b}1 \quad\quad b\geqslant 5 \end{split}\right. $$ (3)

      式中：$ {R}_{b} $——基于b值的预测阶段；

      $ {R}_{b}1 $～$ {R}_{b}5$——滑移阶段（$ {R}_{b}5$），滑移预警阶段II （${R}_{b}4 $），滑移预警阶段I（${R}_{b}3 $），滑动 阶段（${R}_{b}2 $）和平稳阶段（${R}_{b}1 $），$ {R}_{b} $等 级越高表示阶段越危险；

      b——小事件数与大事件数的比值，与滑坡危险程 度成反比，b值越小表示阶段越危险。

      预测情况如表1所示，滑移点为图3右侧竖线，提前时间为b值第一次到达（3，4）间的时间点。结果表明预警点（${R}_{b}3 $对应的滑移预警所致）出现在实际滑移发生之前，预测的滑移阶段包含了实际滑移阶段，且所有试验组均符合规律。当达到$ {R}_{b}5 $后，即使后面阶段发生变化，仍需警惕后续滑坡现象的发生。说明了Rb预测模型能提前预测松散体滑移现象的发生，考虑到b值门槛值的变化性和不确定性，未来可以建立多种声发射参数预测模型，并与各类机器学习的方法相结合，建立新的模型来预测松散体滑移过程，从而提高预测模型的准确性和可靠性。

      表  1  各试验组预测时刻表
      Table  1.  Predicted timetable for each test group
      编号 实际滑移点 滑移预警点 提前时间/帧
      SY1Z1T 510 439 71
      SY1Z2T 700 486 214
      SY2Z1T 650 565 85
      SY2Z2T 660 586 74
      SY3Z1T 720 672 48
      SY3Z2T 738 558 180
      　　注：SY1Z1T含义为试验1组第1通道，下同理。
      下载: 导出CSV 
      | 显示表格

      尽管现阶段将声发射技术实际应用于松散体滑坡工程仍有一定的难度，但可以利用相关技术一些尝试，如利用有源波导技术，改进有源、普通波导杆或WEAD波导杆等，克服AE信号在非固结材料中的快速衰减，并将其应用于实际松散体边坡场景，如渣土场，尾矿库等，以开展边坡风险预测预警。

      4.   结论

      （1）通过连续改变装置倾角，模拟松散体在斜坡地形上经历从静止到蠕变再到滑移的整个过程，利用数据挖掘来揭示松散体滑动变形过程中声发射信号的演化规律以及捕捉滑移前兆，对松散体滑移过程相关研究进行了一定的补充和完善。

      （2）试验数据揭示了松散体滑移前存在一个重要的“窗口期”，通过声发射技术更早监测到松散体内部的变化信号，延长预警时间来提高监测和预警系统的效率，说明AE技术具有识别松散体滑坡前兆的潜力。

      （3）本研究可应用于监测由生产活动或开采资源后形成的松散体边坡稳定性，并在实际生产和工程中及时感知潜在的滑动风险，从而有助于在地质灾害事故发生前采取及时、有针对性的措施，最大程度地减少或避免潜在的生命财产损失。

    • 图  1   FBG传感器原理示意图[16]

      Figure  1.   Schematic diagram of FBG sensor principle

      下载: 全尺寸图片 幻灯片

      图  2   不同的光纤散射光的频率及放射强度示意图

      Figure  2.   Schematic diagram of scattered light frequency and radiation intensity from different optical fibers

      下载: 全尺寸图片 幻灯片

      图  3   （a） Liu等人提出的一种基于PCF的传感器结构[94]；（b） 基于PCF的温度补偿甲烷敏感光纤传感器结构[94]。

      Figure  3.   (a) A PCF-based sensor structure proposed by Liu et al.; (b) PCF-based temperature-compensated methane-sensitive optical fiber sensor structure.

      下载: 全尺寸图片 幻灯片

      图  4   （a）测量甲烷气体浓度的LPFG结构[96]；（b）用于甲烷气体监测的LPFG 结构[97]。

      Figure  4.   (a) LPFG structure for methane gas concentration measurement; (b) LPFG structure for methane gas monitoring.

      下载: 全尺寸图片 幻灯片

      表  1   常用的光纤传感技术总结

      Table  1   Summary of common fiber optic sensing technologies

      名称基本原理优点缺点
      FBG布拉格衍射可封装成不同传感器，直观显示变形和压力等信息。仅能提供准分布式测量
      BOCDA受激布里渊散射光相关域分析测量时间短，空间分辨率高，精度高，可测绝对温度和应变。测量距离短，不可测断点，双端通路系统。
      BOCDR自发布里渊散射光相关域分析单端测量，精度高，可测断点，可测绝对温度和应变。测量时间长
      BOFDA受激布里渊散射光频域分析精度高，空间分辨率高，可测绝对温度和应变。光源相干性，不可测断点，双端通路系统。
      BOTDA受激布里渊散射光时域分析动态范围大，测试时间短，高精度，高空间分辨率，
      可测绝对温度和应变。      		不可测断点，双端通路系统。
      BOTDR自发布里渊散射光时域分析单端测量，可测断点，可测绝对温度和应变。测量时间长，空间分辨率低。
      OFDR瑞利散射频域分析精度高，空间分辨率高。测试距离相对受限。
      OTDR瑞利散射光时域分析单端测量，便携，直观快速，可测断点。间接方式测量光纤应变且误差大，不能用于测温。
      COFDR瑞利散射相干光频域反射分析单端测量，可测断点，测试时间短，高精度，高空间分辨率。测量距离短，仅测量相对变化值。
      COTDR瑞利散射相干光时域反射分析单端测量，可测断点，测试时间短，高精度，高空间分辨率。测量距离短，仅测量相对变化值。
      DAS瑞利散射光时域/频域分析大范围监测，较高的空间分辨率成本较高
      ROTDR拉曼散射光时域分析单端检测，仅对温度敏感，测量距离长。空间分辨率低，精度低。
      ROFDR拉曼散射光频域分析单端检测，仅对温度敏感，精度高，空间分辨率高。存在光源相干性问题。
      下载: 导出CSV
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    目录

    摘要
    HTML全文

    • 0.   引言

    • 1.   试验设计

    • 2.   数据分析

    • 2.1   数据预处理

    • 2.2   振铃计数及AE能量阶段分析

    • 2.3   b值分析

    • 2.4   频谱分析

    • 3.   滑移阶段预测

    • 4.   结论

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