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Volume 45 Issue 2
Apr.  2026
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Article Navigation >  CARSOLOGICA SINICA  > 2026  >  45(2): 342-351, 398
GUAN Depeng, GAO Yong, LI Yi, LIU Yaohui. Research on karst detection based on full waveform inversion imaging technology[J]. CARSOLOGICA SINICA, 2026, 45(2): 342-351, 398. doi: 10.11932/karst20260205
Citation: GUAN Depeng, GAO Yong, LI Yi, LIU Yaohui. Research on karst detection based on full waveform inversion imaging technology[J]. CARSOLOGICA SINICA, 2026, 45(2): 342-351, 398. doi: 10.11932/karst20260205
shu

Research on karst detection based on full waveform inversion imaging technology

doi: 10.11932/karst20260205
  • 1.

    China Railway First Group Co., Ltd., Shaanxi, Xi'an 710054, China

  • 2.

    China Railway First Group Second Engineering Co., Ltd., Hebei, Tangshan 063004, China

  • 3.

    School of Mechanics＆Civil Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China

  • Received Date: 2025-03-18
  • Accepted Date: 2025-10-23
  • Rev Recd Date: 2025-10-10
    Abstract
  • The geological development of karst areas is highly complex, and accurately detecting underground karst caves presents a significant technical challenge to ensure the safety of construction projects. Karst features, including underground caves and subterranean rivers, are widely distributed across China. Characterized by high concealment and spatial randomness, these geological structures are highly prone to inducing disasters such as ground collapse and water or mud inrush. While traditional geophysical methods are effective at certain depths, they tend to rely heavily on wave velocity parameters but underutilize waveform information, such as amplitude and phase. These results in significant errors in identifying cave boundaries and a high rate of missed detections.To enhance detection accuracy, this study proposes a combined approach that integrates the surface wave method with the cross-hole elastic wave method and introduces full waveform inversion imaging technology to create a comprehensive detection system. The surface wave method is cost-effective and suitable for rapid surveys, though its interpretation is significantly affected by surface heterogeneity. The cross-hole elastic wave method, while effective in minimizing shallow interference by utilizing hole-based excitation and reception, offers superior resolution at greater depths but incurs higher costs and requires pre-drilling.Full waveform inversion technology utilizes all available waveform information-such as wave velocity, amplitude, and phase-based on numerical inversion of the wave equation. It overcomes the limitations of the traditional horizontal layered medium assumption, significantly improving the accuracy of karst cave boundary identification. A field test conducted in a karst area in Guizhou adopted a joint observation approach, yielding promising results. Full waveform inversion clearly delineated the spatial distribution of karst caves (depth of 6 to 12 m, lateral range from 15 to 25 m), with imaging resolution far surpassing that of first arrival wave analysis and Synchronous Iterative Reconstruction Technology (SIRT). A serial inversion strategy, progressing from low to high frequencies, effectively suppressed noise and ensured stable convergence to the global optimum. The conjugate gradient iterative algorithm was used for inversion, with an attenuation boundary width set at 2 m, a grid size of 0.2 m × 0.2 m, and serial optimization divided into three stages at 20 Hz, 30 Hz, and 40 Hz. The error decreased progressively with increasing iterations, demonstrating the method's numerical stability and convergence.The results further highlight that joint inversion of P-wave and S-wave data offers mutual verification, with a high degree of consistency in identifying the anomaly locations. The boundary of the S-wave inversion result was clearer, in contrast to the first arrival wave method, which only provides a one-dimensional wave velocity curve. SIRT reconstruction, on the other hand, is prone to boundary ambiguities and geometric distortion. In comparison to traditional methods that rely solely on first arrival travel time, full waveform inversion leverages the dynamic characteristics of the wave field, significantly enhancing the ability to identify small anomalous bodies and complex boundaries.Additionally, comparing the inversion results from the surface wave and cross-hole elastic wave methods reveals good consistency in identifying the main forms of the karst caves. However, cross-hole data provided more reliable deep structural details, whereas surface wave inversion exhibited local false anomalies due to shallow inhomogeneities. This underscores the importance of the combined surface and hole-based observation method. The fusion of these methods maximizes the complementary strengths of the surface wave method's efficiency and the cross-hole method's detailed depth resolution, offering a high-reliability, adaptable technical solution for engineering geological surveys in complex karst environments, with substantial engineering application value.

     

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    • References(21)
  • [1]
    Liu Dong, Liu Minghong, Sun Huaifeng, Liu Rui, Lu Xushan. Detection and comprehensive treatment for giant karst caves under the tunnel floor: A case study in Guangxi, China[J]. Environmental Earth Sciences, 2024, 83(23): 650.
    [2]
    Lan Riyan, Liu Zonghui, Liu Maomao, Guan Qiyu, Yan Yuanfang, Sun Huaifeng, Zhou Dong. Detection of karst caves during tunnel construction using ground-penetrating radar and advanced drilling: A case study in Guangxi Province, China[J]. Near Surface Geophysics, 2022, 20(3): 265-278.
    [3]
    Wang Jinchao, Wang Chuanying, Han Zenqiang, Zou Xianjian, Wang Yiteng, Wang Chao, Sheng Hu. Characteristic parameters extraction method of hidden karst cave from borehole radar signal[J]. International Journal of Geomechanics, 2020, 20(8): 04020113.
    [4]
    Su Maoxin, Zhao Ying, Xue Yiguo, Wang Peng, Xia Teng, Zhang Kai, Li Congcong.Progressive fine integrated geophysical method for karst detection during dubway construction[J]. Pure and Applied Geophysics, 2021, 178(1): 91-106.
    [5]
    Liu Y H, Li S C, Li L N, Li Z. Detection of beaded karst caves in subway works by mixed-source surface wave survey: A case study[J]. Lithosphere, 2024(3): 135.
    [6]
    Lou G C, Song Y, Man L C. A new method for detecting karst and groundwater by 3D seismic wave: case study of the karst tunnel in Zhangjihuai Railway, China[J]. Bulletin of Engineering Geology and the Environment, 2023.82(12): 451.
    [7]
    Ba X Z, Li L P, Wang J, Zhang W, Fang Z D, Sun S Q, Liu Z H, Xiong Y F. Near-surface site investigation and imaging of karst cave using comprehensive geophysical and laser scanning: a case study in Shandong, China[J]. Environmental Earth Sciences, 2020, 79(12): 298.
    [8]
    李录娟, 贾龙, 殷仁朝. 基于孔中雷达反射成像特征的岩溶定量化评价[J]. 中国岩溶, 2021, 40(5): 901-906.

    Li Lujuan, Jia Long, Yin Renchao. Karst quantitative evaluation based on the characteristics of borehole radar reflection[J]. Carsologica Sinica, 2021, 40(5): 901-906.
    [9]
    彭程. 地质雷达和TGP地震波法在高家坪特长隧道岩溶探测中的应用[J]. 工程地球物理学报, 2022, 19(3): 328-333.

    Peng Cheng. Application of geological radar and TGP seismic wave methodin karst exploration of Gaojiaping extra Long tunnel[J]. Chinese Journal of Engineering Geophysics, 2022, 19(3): 328-333.
    [10]
    Pasierb B, Gajek G, Urban J. Integrated Geophysical and Geomorphological Studies of Caves in Calcarenite Limestones (Jaskinia pod Świecami Cave, Poland)[J], Surveys in Geophysics, 2024, 45(3): 663-694.
    [11]
    代方园, 高扬, 宿庆伟, 胡韬, 耿付强, 董亚楠. 瞬变电磁与跨孔CT成像探测岩溶分布及形态特征的应用: 以山东省济南地区为例[J]. 中国岩溶, 2022, 41(2): 308-317, 328.

    Dai Fangyuan, Gao Yang, Su Qingwei, Hu Tao, Geng Fuqiang, Dong Yanan. Application of transient electromagnetism and cross-hole CT imaging to detect karst distribution and morphological characteristics: A case study of Jinan, Shandong Province[J]. Carsologica Sinica, 2022, 41(2): 308-317, 328.
    [12]
    牟晓东. 基于颜色融合技术的综合跨孔层析岩溶探测方法[J]. 物探与化探, 2024, 48(6): 1730-1740.

    Mou Xiaodong. A comprehensive crosshole tomography method for karst identification based on color fusion technology[J]. Geophysics & Geochemical Exploration, 2024, 48(6): 1730-1740.
    [13]
    Lu X L, Hu X Q, Xu Z Y. Research Article Tunnel Concealed Karst Cave Joint Detection by Tunnel Seismic and Transient Electromagnetic[J]. Lithosphere, 2022 (1).
    [14]
    宋同, 李欣欣, 张伟, 胡涛, 郑晓慧. 基于加窗互相关函数的微动面波岩溶塌陷探测[J]. 中国岩溶, 2024, 43(4): 937-947.

    Song Tong, Li Xinxin, Zhang Wei, Hu Tao, Zheng Xiaohui. Detection of karst collapses through microtremor surface waves based on windowing cross-correlation function[J]. Carsologica Sinica, 2024, 43(4): 937-947.
    [15]
    高振, 王伟, 王祥春, 李开富. 椭球定位速度分析方法在黑松驿地区超前预报中的应用[J]. 地球物理学进展, 2023, 38(2): 790-802.

    Gao Zhen, Wang Wei, Wang Xiangchun, Li Kaifu. Application of ellipsoidal positioning velocity analysis method in advance forecast in Heisongyi area[J]. Progress in Geophysics, 2023, 38(2): 790-802.
    [16]
    刘旭斌, 申翔宇, 闵新皓. 基于超前地质预报的大型岩溶隧道处理技术[J]. 现代隧道技术, 2022, 59(S1): 881-891.

    Liu Xubin, Shen Xiangyu, Min Xinhao. Large-scale karst tunnel treatment technology based on advance geological prediction[J]. Model Tunnelling Technology, 2022, 59(S1): 881-891.
    [17]
    王荣东, 张成杰, 马锦国, 李伟科, 周明文, 宋明艺, 章志勇. 面波CMP法和跨孔弹性波CT技术在岩溶探测中的应用[J]. 工程地球物理学报, 2025, 22(1): 83-90.

    Wang Rongdong, Zhang Chengjie, Ma Jinguo, Li Weike, Zhou Mingwen, Song Mingyi, Zhang Zhiyong. The comprehensive application of surface wave CMP method and crosshole seismic CT technology in karst exploration[J]. Chinese Journal of Engineering Geophysics, 2025, 22(1): 83-90.
    [18]
    Thiel N, Herweck T, Bohlen T. Comparison of acoustic and elastic full-waveform inversion of 2D towed-streamer data in the presence of salt[J]. Geophysical Prospecting, 2019, 67 (2): 349-361.
    [19]
    Vigh D, Xu J, Cheng X. Sparse-node acquisition for data fitting velocity model building[J]. Geophysical Prospecting, 2023, 71(8): 1540-1550.
    [20]
    Nguyen T D, Tran K T, Mcvay M. Evaluation of unknown foundations using surface-based full waveform tomography[J]. Journal of Bridge Engineering, 2016, 21: 5.
    [21]
    Jiang P, Wang Q Y, Ren Y X, Yang S L, Li N B. Full waveform inversion based on inversion network reparameterized velocity[J]. Geophysical Prospecting, 2024, 72(1): 52-67.
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