Journal of Jilin University(Earth Science Edition) ›› 2026, Vol. 56 ›› Issue (4): 1449-1464.doi: 10.13278/j.cnki.jjuese.20250005

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Application of Passive Surface-Wave Imaging to the Detection of Karst Development Areas

Xian Hailong1,2, Wu Chengeng1, 2, Yan Yingwei3, Su Ronghua4, Li Jing5, Sun Ruizhe5, Liu Cong1,2, Wang Haolong1, 2, Pi Wei1, 2   

  1. 1. Henan Institute of Geophysical Spatial Information Co., Ltd, Zhengzhou 450000, China
    2. Henan Geological and Geophysical Engineering Technology Research Center, Zhengzhou 450000, China
    3.  Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
    4.  Defense Engineering Institute, AMS, PLA, Beijing 100036, China
    5.  College of GeoExploration Science and Technology, Jilin University, Changchun 130026, China
  • Received:2025-01-06 Online:2026-07-26 Published:2026-08-11
  • Supported by:
    the National Science and Technology Major Project  (2025ZD1008400),the Major Special Project for National Key Laboratories of Jilin Province (SKL202502020JC) and the Basic Discipline and Interdisciplinary Breakthrough Program of the Ministry of Education (JYB2025XDXM803)

Abstract: Karst is a typical geological hazard in urban environments, and its development can affect the stability and safety of the infrastructures. Compared with the surrounding bedrock, the S-wave velocity of karst is relatively lower, and appear as low-velocity anomalies. Surface waves within ambient noise are highly sensitive to the S-wave velocity of underground media, with the higher-mode surface waves demonstrating even greater sensitivity to such low-velocity anomalies. However, the conventional staged inversion schemes for multimode dispersion curves rely on global optimization algorithms, resulting in substantial computational costs. To address this limitation, we propose an improved inversion workflow. In the first stage, the preconditioned steepest descent method, a local optimization algorithm, is employed to invert the identified fundamental-mode dispersion curve. In the second stage, the pattern search algorithm is utilized to invert all available dispersion data. During the iteration, the Kuhn-Munkres algorithm is implemented to dynamically assign the mode-orders to unlabeled dispersion points. The efficacy of the proposed method in characterizing shallow low-velocity interlayers is validated via synthetic tests based on a four-layer geological model containing a soft interlayer. Furthermore, the method is applied to the field passive-source data acquired in the karst development area of Longgang District, Shenzhen, to evaluate its practical utility. The results demonstrate that the higher-mode dispersion data serve as observational constraints independent of the fundamental-mode data, effectively enhancing the accuracy and vertical resolution of the inversion, and improving the delineation of soft interlayers. Additionally, the proposed approach successfully identified the underlying karst development area along the survey line. Notably, energy loss and modal aliasing of higher-mode surface waves dispersion are common phenomena in shallow surface wave data. Therefore, the dynamic mode-order identification is essential for robust multimode surface wave inversion.

Key words: passive surface-wave, karst development area, staged inversion, multi-order inversion, S-wave velocity

CLC Number: 

  • P631.4
[1] Yan Yingwei, Wang Zhejiang, Han Fei, Liu Cong, Zeng Fanjie. Nonlinear Inversion of Multi-Mode Surface Waves [J]. Journal of Jilin University(Earth Science Edition), 2019, 49(6): 1768-1779.
[2] Kang Jian, Wei Qinghai, Zhou Lin, Gao Yan, Zhang Yonggang, Gao Feng. Deep Structure Based on Seismic Array Observations in Daqing Area [J]. Journal of Jilin University(Earth Science Edition), 2016, 46(3): 900-910.
[3] JIANG Xiu-di, WEI Xiu-cheng, WANG Jian. The Application of P-Wave and S-Wave Velocity Inversion to the Identification of Gas Layer [J]. J4, 2006, 36(01): 117-0122.
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