基于探地雷达属性分析的隧道内溶洞三维可视化研究

周东; 刘毛毛; 刘宗辉; 王业田; 孙伟杰

doi:10.11779/CJGE20211277

基于探地雷达属性分析的隧道内溶洞三维可视化研究 English Version

周东^{1, 2,},
刘毛毛¹,
刘宗辉^{1, 2, 3, ,},
王业田^{1, 2},
孙伟杰¹

1.
广西大学土木建筑工程学院, 广西南宁 530004
2.
广西大学广西防灾减灾与工程安全重点实验室, 广西南宁 530004
3.
广西新发展交通集团有限公司, 广西南宁 530028

基金项目:

国家自然科学基金青年基金项目 51708136

广西科技基地和人才专项项目桂科AD19245153

详细信息

作者简介:
作者简介：周东(1962—)，男，教授，博士生导师，主要从事隧道地质预报、环境岩土工程方面的研究工作。E-mail：zhd@gxu.edu.cn

通讯作者:
刘宗辉, E-mail: lzh8@gxu.edu.cn

中图分类号: TU43;U456
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出版历程
- 收稿日期: 2021-10-31
- 网络出版日期: 2023-02-23
- 刊出日期: 2023-01-31

3D visualization of karst caves in tunnels based on GPR attribute analysis

ZHOU Dong^{1, 2,},
LIU Maomao¹,
LIU Zonghui^{1, 2, 3, ,},
WANG Yetian^{1, 2},
SUN Weijie¹

1.
College of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China
2.
Guangxi Key Laboratory of Disaster Prevention and Engineering Safety, Guangxi University, Nanning 530004, China
3.
Guangxi Xinfazhan Communications Group Co., Ltd., Nanning 530028, China

摘要

摘要: 针对探地雷达法隧道内溶洞探测过程中存在位置标定模糊和形状确定困难等问题，提出了一种新的三维可视化方法。首先利用F-K偏移成像技术处理每条测线上的探地雷达数据，并根据坐标信息合成三维数据体，增强不同测线间的横向联系。然后通过属性分析提高探地雷达视图效果和有效反射数据的对比度，进而利用K-Means聚类方法提取溶洞反射数据的可视化振幅阈值参数。最后利用三维属性体和等值面提取技术实现隧道溶洞探地雷达三维可视化。模型试验和现场案例分析结果验证了本文方法的有效性，应用结果表明最大谱振幅属性不仅能够提高探地雷达视图效果，还能增强探地雷达数据体中有效反射数据的区分度，是本文方法较佳的输入属性。研究成果在一定程度上解决了探地雷达传统三维可视化方法振幅阈值设置时过度依赖解译人员经验问题，该方法适用于沉积岩层等层状介质解释。
- 探地雷达 /
- 岩溶隧道 /
- 隐伏溶洞 /
- 属性分析 /
- K-Means聚类 /
- 三维可视化
Abstract: The ground penetrating radar (GPR) can be used to detect and determine the scale, shape and position of hidden karst caves in tunnel construction, and it is very important for the protection of the tunnel construction safety and the hazard geology treatment. Due to the complexity of tunnel detection environment, the location calibration and the shape determination of the results for the traditional GPR 2D detection are difficult. However, due to the strong subjectivity of amplitude threshold setting, there is great uncertainty in the visualization process of GPR 3D data obtained based on the multiple survey lines. A 3D visualization method for the tunnel karst cave of GPR data is proposed. Firstly, to improve the imaging accuracy of karst cave targets, the F-K method is used to process each GPR B-scan. According to the coordinate information of GPR data, the GPR 3D data of the karst cave is synthesized to enhance the horizontal connection between different lines. Then, to improve the view effects and enhance the contrast of the effective reflection data, the method of GPR attribute analysis is used. The amplitude threshold of GPR 3D visualization of hidden karst cave is further extracted by using the K-means cluster method. Finally, the GPR 3D visualization of the tunnel karst cave can be realized by combining the attribute volume and the isosurface extraction technology. The reliability and adaptability of this method are verified by the model tests and field case analysis. The maximum spectral amplitude attribute is the optimal attribute of GPR signal in the proposed method, which may improve the radar view effect and enhance the contrast between the background and the effective reflection for GPR data. Furthermore, the proposed method solves the problem that the amplitude threshold setting of GPR 3D visualization excessively depends on the experience judgment of interpreters, and the results will be valuable for the stratigraphic analysis such as sedimentary strata.
- ground penetrating radar /
- karst tunnel /
- hidden karst cave /
- attribute analysis /
- K-means clustering /
- 3D visualization

HTML全文

图 1 岩溶隧道探地雷达三维探测示意图

Figure 1. Schematic diagram of GPR 3D detection in karst tunnel

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图 2 频谱类属性示意图

Figure 2. Schematic diagram of frequency attributes

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图 3 探地雷达三维可视化流程图

Figure 3. Work flow of method of GPR 3D visualization

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图 4 物理模型试验几何模型和现场照片

Figure 4. Geometric model and field image of physical model

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图 5 探地雷达二维剖面和三维数据体

Figure 5. GPR images and 3D data

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图 6 不同探地雷达数据体在8 ns处切片对比

Figure 6. Comparison of depth slices of different 3D volumes at 8 ns

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图 7 原始数据和不同属性体三维可视化结果对比

Figure 7. Comparison of 3D visualization results between original data and different attribute volumes

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图 8 溶洞模型上反射面提取结果

Figure 8. Extracted results of upper reflection interface of karst cave model

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图 9 隧道定位和现场照片

Figure 9. Site case map and tunnel images

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图 10 现场网格测线布置示意图

Figure 10. Schematic diagram of layout of site grid survey lines

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图 11 第1条测线探地雷达剖面及其偏移成像

Figure 11. GPR image of line 1 and its migration imaging

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图 12 探地雷达深度切片

Figure 12. Deep slices of GPR data

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图 13 基于最大谱振幅属性的隧道下伏溶洞三维可视化

Figure 13. 3D visualization of the hidden karst cave based on maximum spectral amplitude attribute

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图 14 隧道下伏溶洞顶板面提取结果

Figure 14. Top panel of hidden karst cave

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