被动源面波成像在岩溶发育区探测中的应用

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

摘要

岩溶是城市环境中的一种典型地质病害体,其发育会影响地表建筑的稳定性与安全性.相比于围岩,岩溶的横波速度较低,以低速异常体形式体现.环境噪声中的面波对横波速度敏感,其中的高阶面波对低速异常更加敏感.然而,由于使用了全局算法,目前的分阶段多阶面波频散曲线反演方法计算量较大.为此,本文对其做出改进:在第一阶段反演中使用局部优化算法中的预处理最速下降算法反演识别出的基阶面波频散曲线;在第二阶段反演中使用模式搜索算法反演所有的频散数据,并在迭代中使用Kuhn-Munkres算法动态确定未定阶次频散数据的阶次.以四层含软弱夹层地质模型反演测试为例,测试改进后方法识别浅层介质低速夹层结构的效果;并将其应用于深圳市龙岗区岩溶发育区的实际被动源数据,以评估改进后反演方法的实用性.结果表明:高阶面波频散数据是独立于基阶面波频散数据的观测约束,可提高反演结果的精度和垂向分辨率,增强对软弱夹层的识别能力;在深圳市龙岗区成功识别出了测线下方的岩溶发育区.高阶面波频散能量缺失与混叠是浅层面波数据中的一种常见现象,在多阶面波频散曲线反演中需要动态识别高阶面波频散数据的阶次.

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.

关键词

被动源面波 / 岩溶发育区 / 分阶段反演 / 多阶反演 / 横波速度

Key words

passive surface-wave / karst development area / staged inversion / multi-order inversion / S-wave velocity

参考文献

[1] 沈志平, 陈发达, 张虎, 等. HVSR探测方法在城市岩溶勘察中的应用[J]. 地球物理学进展, 2022, 37(4): 1757-1763.

[2] Shen Zhiping, Chen Fada, Zhang Hu, et al. Application of HVSR Detection Method in Urban Karst Investigation[J]. Progress in Geophysics, 2022, 37(4): 1757-1763.

[3] 胡祥云, 杨迪坤, 刘少华, 等. 环境与工程地球物理的发展趋势[J]. 地球物理学进展, 2006, 21(2): 598-604.

[4] Hu Xiangyun, Yang Dikun, Liu Shaohua, et al. The Developing Trends of Environmental and Engineering Geophysics[J]. Progress in Geophysics, 2006, 21(2): 598-604.

[5] 曾维望, 许玉莹, 成跃. 基于地震属性分析的采空区探测技术与应用[J]. 物探与化探, 2012, 36(增刊1): 78-82.

[6] Zeng Weiwang, Xu Yuying, Cheng Yue. The Detection of Goaf Based on Seismic Attribute Analysis Technology and Its Application[J]. Geophysical & Geochemical Exploration, 2012, 36(Sup. 1): 78-82.

[7] 程建远, 孙洪星, 赵庆彪, 等. 老窑采空区的探测技术与实例研究[J]. 煤炭学报, 2008, 33(3): 251-255.

[8] Cheng Jianyuan, Sun Hongxing, Zhao Qingbiao, et al. The Detection of Excavated Region in Coal Mine and Case Study[J]. Journal of China Coal Society, 2008, 33(3): 251-255.

[9] 徐佩芬, 李传金, 凌甦群, 等. 利用微动勘察方法探测煤矿陷落柱[J]. 地球物理学报, 2009, 52(7): 1923-1930.

[10] Xu Peifen, Li Chuanjin, Ling Suqun, et al. Mapping Collapsed Columns in Coal Mines Utilizing Microtremor Survey Methods[J]. Chinese Journal of Geophysics, 2009, 52(7): 1923-1930.

[11] 马丽, 金溪, 贺正东. 微动物探方法探测采空区[J]. 中国煤炭地质, 2013, 25(5): 50-54.

[12] Ma Li, Jin Xi, He Zhengdong. Gob Area Detection Through Microtremor Surveying[J]. Coal Geology of China, 2013, 25(5): 50-54.

[13] 杨镜明, 魏周政, 高晓伟. 高密度电阻率法和瞬变电磁法在煤田采空区勘查及注浆检测中的应用[J]. 地球物理学进展, 2014, 29(1): 362-369.

[14] Yang Jingming, Wei Zhouzheng, Gao Xiaowei. The Application of the Methods of High-Density Resistivity Method and Transient Electromagnetic to Detecting Coal Mining Goaf and Inspect Grouting Effect[J]. Progress in Geophysics, 2014, 29(1): 362-369.

[15] 王善勋, 杨文锋, 张卫敏, 等. 瞬变电磁法在煤矿采空区探测中的应用研究[J]. 工程地球物理学报, 2012, 9(4): 400-405.

[16] Wang Shanxun, Yang Wenfeng, Zhang Weimin, et al. The Application of TEM to Detecting Coal Mining Goaf[J]. Chinese Journal of Engineering Geophysics, 2012, 9(4): 400-405.

[17] 石刚, 屈战辉, 唐汉平, 等. 探地雷达技术在煤矿采空区探测中的应用[J]. 煤田地质与勘探, 2012, 40(5): 82-85.

[18] Shi Gang, Qu Zhanhui, Tang Hanping, et al. Gob Detection Based on Ground Penetrating Radar Technique[J]. Coal Geology & Exploration, 2012, 40(5): 82-85.

[19] 汪利民, 夏江海, 罗银河, 等. 浅地表面波成像技术研究进展与展望[J]. 地球与行星物理论评, 2022, 53(6): 613-655.

[20] Wang Limin, Xia Jianghai, Luo Yinhe, et al. Progress and Prospect of Surface-Wave Imaging Techniques in Near-Surface Applications[J]. Reviews of Geophysics and Planetary Physics, 2022, 53(6): 613-655.

[21] Pan L, Chen X, Wang J, et al. Sensitivity Analysis of Dispersion Curves of Rayleigh Waves with Fundamental and Higher Modes[J]. Geophysical Journal International, 2019, 216(2): 1276-1303.

[22] Lamuraglia S, Stucchi E, Aleardi M. Application of a Global-Local Full-Waveform Inversion of Rayleigh Wave to Estimate the Near-Surface Shear Wave Velocity Model[J]. Near Surface Geophysics, 2023, 21(1): 21-38.

[23] Wang J, Wu G, Chen X. Frequency-Bessel Transform Method for Effective Imaging of Higher-Mode Rayleigh Dispersion Curves from Ambient Seismic Noise Data[J]. Journal of Geophysical Research: Solid Earth, 2019, 124(4): 3708-3723.

[24] Yan Y, Chen X, Huai N, et al. Modern Inversion Workflow of the Multimodal Surface Wave Dispersion Curves: Staging Strategy and Pattern Search with Embedded Kuhn-Munkres Algorithm[J]. Geophysical Journal International, 2022, 231(1): 47-71.

[25] Zhan W, Pan L, Chen X. A Widespread Mid-Crustal Low-Velocity Layer Beneath Northeast China Revealed by the Multimodal Inversion of Rayleigh Waves from Ambient Seismic Noise[J]. Journal of Asian Earth Sciences, 2020, 196: 104372.

[26] 李雪燕, 陈晓非, 杨振涛, 等. 城市微动高阶面波在浅层勘探中的应用:以苏州河地区为例[J]. 地球物理学报, 2020, 63(1): 247-255.

[27] Li Xueyan, Chen Xiaofei, Yang Zhentao, et al. Application of High-Order Surface Waves in Shallow Exploration: An Example of the Suzhou River, Shanghai[J]. Chinese Journal of Geophysics, 2020, 63(1): 247-255.

[28] 李海艳, 陈晓非, 陈举庆, 等. 背景噪声频率贝塞尔变换法研究厦门岛浅层S波速度结构[J]. 地球物理学报, 2024, 67(7): 2654-2667.

[29] Li Haiyan, Chen Xiaofei, Chen Juqing, et al. Study on Shallow S-Wave Velocity Structure of Xiamen Island with the Frequency-Bessel Transform Method from Seismic Ambient Noise[J]. Chinese Journal of Geophysics, 2024, 67(7): 2654-2667.

[30] Yan Y, Chen X, Li J, et al. Inversion of Multimodal Dispersion Curves from Distributed Acoustic Sensing Measurements for Subsurface Imaging: A Field Case of Garner Valley, California[J]. Journal of Applied Geophysics, 2023, 214: 105070.

[31] Song X, Li D, Gu H, et al. Insights into Performance of Pattern Search Algorithms for High-Frequency Surface Wave Analysis[J]. Computers & Geosciences, 2009, 35(8): 1603-1619.

[32] 闫英伟, 王者江, 韩飞, 等. 多模式面波非线性反演[J]. 吉林大学学报(地球科学版), 2019, 49(6): 1768-1779.

[33] Yan Yingwei, Wang Zhejiang, Han Fei, et al. Nonlinear Inversion of Multi-Mode Surface Waves[J]. Journal of Jilin University (Earth Science Edition), 2019, 49(6): 1768-1779.

[34] Kuhn H. The Hungarian Method for the Assignment Problem[J]. Naval Research Logistics Quarterly, 1955, 2: 83-97.

[35] Munkres J. Algorithms of the Assignment and Transportation Problems[J]. Journal of the Society for Industrial and Applied Mathematics, 1957, 5(1): 32-38.

[36] Aki K, Richards P G. Quantitative Seismology[M].2nd ed.Sausalito: University Science Books, 2002.

[37] Zywicki D J, Rix G J. Mitigation of the Near-Field Effects for Seismic Surface Wave Velocity Estimation with Cylindrical Beamformers[J]. Journal of Geotechnical and Geoenvironmental Engineering, 2005, 131(8): 970-977.

[38] Haney M M, Nakahara H. Surface-Wave Green’s Tensors in the Near Field[J]. Bulletin of the Seismological Society of America, 2014, 104(3): 1578-1586.

[39] Aster R, Borchers B, Thurber C. Parameter Estimation and Inverse Problems[M].3rd ed.Amsterdam: Elsevier, 2018.

[40] Xia J, Miller R D, Park C B. Estimation of Near-Surface Shear-Wave Velocity by Inversion of Rayleigh Waves[J]. Geophysics, 1999, 64(3): 691-700.

[41] Conn A, Gould N, Toint P. A Globally Convergent Augmented Lagrangian Algorithm for Optimization with General Constraints and Simple Bound[J]. SIAM Journal on Numerical Analysis, 1991, 28(2): 545-572.

[42] 凡友华, 陈晓非, 刘雪峰, 等. Rayleigh波的频散方程高频近似分解和多模式激发数目[J]. 地球物理学报, 2007, 50(1): 233-239.

[43] Fan Youhua, Chen Xiaofei, Liu Xuefeng, et al. Approximate Decomposition of the Dispersion Equation at High Frequencies and the Number of Multimodes for Rayleigh Waves[J]. Chinese Journal of Geophysics, 2007, 50(1): 233-239.

[44] 于涵, 刘财, 王典, 等. 面波频散能量谱计算方法[J]. 吉林大学学报(地球科学版), 2022, 52(2): 602-612.

[45] Yu Han, Liu Cai, Wang Dian, et al. Energy Spectrum Calculation of Surface Wave Dispersion[J]. Journal of Jilin University (Earth Science Edition), 2022, 52(2): 602-612.

[46] Kausel E. Generalized Stiffness Matrix Method for Layer Soils[J]. Soil Dynamics and Earthquake Engineering, 2018, 115: 663-672.

[47] 孙敬雯, 吕子强, 孔庆翰, 等. 基于背景噪声成像的沂沭断裂带及邻区波速变化[J]. 吉林大学学报(地球科学版), 2025, 55(4): 1361-1371.

[48] Sun Jingwen, Lü Ziqiang, Kong Qinghan, et al. Crustal Velocity Variations in the Yishu Fault Zone and Surrounding Regions Based on Ambient Noise Tomography[J]. Journal of Jilin University (Earth Science Edition), 2025, 55(4): 1361-1371.

[49] Schimmel M, Paulssen H. Noise Reduction and Detection of Weak, Coherent Signals Through Phase-Weighted Stacks[J]. Geophysical Journal International, 1997, 130(2): 497-505.

基金资助

国家科技重大专项(2025ZD1008400)

吉林省全国重点实验室重大专项(SKL202502020JC)

教育部基础学科和交互学科突破计划项目(JYB2025XDXM803)