Inversion of microtremor recordings dispersion curve based on geological unit
-
摘要: 微动勘探技术是一种利用环境本身微弱振动探测城市地下结构的方法。微动信号中面波占主导，分析微动信号可以提取面波频散曲线，通过反演频散曲线能够获得地层横波速度。但反演时地层划分规模与计算开销及求解稳定性之间存在矛盾，地层划分层数越多，反演过程中未知参数越多，会增大计算开销，加剧方程的欠定程度，使反演结果不准确。为了解决这一问题，本文在前人研究的基础上提出基于地质单元体的改进算法，当反演收敛速度较低时对横波速度差异较小的相邻两单元进行合并，以合并后的地层参与反演。通过数值理论模拟与实测数据分析，基于地质单元体的改进反演算法能够降低方程的欠定程度，得到更稳定的解并减少计算开销。Abstract: The urban microtremor exploration technology is a method that detects urban underground structures by using the weak vibration of environment.The surface wave is dominant in the microtremor recordings, and the surface wave dispersion curve can be extracted by further analysis of the fretting signal, and the shear wave velocity can be obtained by inverting the dispersion curve.There is a contradiction among the scale of stratigraphic division, the calculation cost and solution stability.The more stratigraphic division layers, the more unknown parameters in the inversion process.Too much stratigraphic division will increase the computational overhead, aggravate the uncertainty of the equation, and make the inversion results inaccurate.In order to solve this problem, this paper proposes an improved algorithm based on geological unit by drawing on previous studies.When the convergence rate of inversion is low, the two adjacent units with small s-wave velocity difference are combined and the combined stratum participates in inversion.Through the analysis of numerical simulation and measured data, the improved inversion algorithm based on geological unit can reduce the uncertainty of the equation, obtain more stable solutions and reduce the computational overhead.
-
Key words:
- microtremor recordings /
- dispersion curve /
- geological unit /
- mergeing stratum
-
表 1 含低速夹层模型参数
Table 1. Parameters of the model with low velocity interlayer
层序号 横波速度/(m·s-1) 纵波速度/(m·s-1) 密度/(g·cm-3) 层厚/m 1 300 743 2 037 12 2 400 950 2 161 6 3 300 743 2 037 12 4 500 1 150 2 263 12 5 700 1 533 2 425 半空间 
下载: 导出CSV
表 2 含高速夹层模型参数
Table 2. Parameters of the model with high velocity interlayer
层序号 横波速度/(m·s-1) 纵波速度/(m·s-1) 密度/(g·cm-3) 层厚/m 1 300 743 2 037 12 2 400 950 2 161 6 3 800 1 719 2 493 12 4 500 1 150 2 263 12 5 700 1 533 2 425 半空间
下载: 导出CSV
表 3 台站组合
Table 3. Station combination
台站组合间距 台站组合编号 r (1，2)，(1，3)，(1，4) 1.73r (2，3)，(3，4)，(4，2) 2r (1，5)，(1，6)，(1，7) 3r (3，6)，(2，7)，(4，5) 3.46r (5，6)，(6，7)，(7，5)
下载: 导出CSV
-
[1] 李雪燕. 城市微动高阶面波在浅层成像中的应用[D]. 合肥: 中国科学技术大学, 2019. [2] 彭刘亚, 任川, 冯伟栋. 多模式瑞雷波频散曲线的粒子群反演方法研究[J]. 地球物理学进展, 2018, 33(3): 1262-1268. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWJ201803050.htmPeng Liuya, Ren Chuan, Feng Weidong. Multimodal Rayleigh wave dispersion curve inversion by particle swarm optimization[J]. Progress in Geophysics, 2018, 33(3): 1262-1268. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWJ201803050.htm [3] Toksöz M N, Lacoss R T. Microseisms: mode structure and sources[J]. Science, 1968, 159(3817): 872-873. doi: 10.1126/science.159.3817.872 [4] 孙远, 杨峰, 郑晶, 等. 基于变分模态分解和小波能量熵的微震信号降噪[J]. 矿业科学学报, 2019, 4(6): 469-479. http://kykxxb.cumtb.edu.cn/article/id/248Sun Yuan, Yang Feng, Zheng Jing, et al. Research on microseismic signal denoising based on variational mode decomposition and wavelet energy entropy[J]. Journal of Mining Science and Technology, 2019, 4(6): 469-479. http://kykxxb.cumtb.edu.cn/article/id/248 [5] 夏江海, 高玲利, 潘雨迪, 等. 高频面波方法的若干新进展[J]. 地球物理学报, 2015, 58(8): 2591-2605. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201508002.htmXia Jianghai, Gao Lingli, Pan Yudi, et al. New findings in high-frequency surface wave method[J]. Chinese Journal of Geophysics, 2015, 58(8): 2591-2605. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201508002.htm [6] 刘云祯, 王振东. 瞬态面波法的数据采集处理系统及其应用实例[J]. 物探与化探, 1996, 20(1): 28-34. https://www.cnki.com.cn/Article/CJFDTOTAL-WTYH601.003.htmLiu Yunzhen, Wang Zhendong. Data collection and processing system of transient surface wave method and examples of its application[J]. Geophysical and Geochemical Exploration, 1996, 20(1): 28-34. https://www.cnki.com.cn/Article/CJFDTOTAL-WTYH601.003.htm [7] Xia J H, 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. doi: 10.1190/1.1444578 [8] 宋先海, 肖柏勋, 黄荣荣, 等. 用等厚薄层权重自适应迭代阻尼最小二乘法反演瑞雷波频散曲线[J]. 物探与化探, 2003, 27(3): 212-216. doi: 10.3969/j.issn.1000-8918.2003.03.013Song Xianhai, Xiao Boxun, Huang Rongrong, et al. The inversion of dispersion curves using self-adaptively iterative damping least square method by combining equal thinner layers with weighting matrix[J]. Geophysical and Geochemical Exploration, 2003, 27(3): 212-216. doi: 10.3969/j.issn.1000-8918.2003.03.013 [9] Yamanaka H, Ishida H. Application of genetic algorithms to an inversion of surface wave dispersion data[J]. Bulletin of the Seismological Society of America, 1996, 86(2): 436-444. [10] 蔡伟, 宋先海, 袁士川, 等. 基于萤火虫和蝙蝠群智能算法的瑞雷波频散曲线反演[J]. 地球物理学报, 2018, 61(6): 2409-2420. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201806021.htmCai Wei, Song Xianhai, Yuan Shichuan, et al. Inversion of Rayleigh wave dispersion curves based on firefly and bat algorithms[J]. Chinese Journal of Geophysics, 2018, 61(6): 2409-2420. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201806021.htm [11] Beaty K S, Schmitt D R, Sacchi M. Simulated annealing inversion of multimode Rayleigh wave dispersion curves for geological structure[J]. Geophysical Journal International, 2002, 151(2): 622-631. [12] Shirazi H, Abdallah I, Nazarian S. Developing artificial neural network models to automate spectral analysis of surface wave method in pavements[J]. Journal of Materials in Civil Engineering, 2009, 21(12): 722-729. [13] Song X H, Gu H M, Zhang X Q, et al. Pattern search algorithms for nonlinear inversion of high-frequency Rayleigh-wave dispersion curves[J]. Computers & Geosciences, 2008, 34(6): 611-624. [14] Song X H, Tang L, Lu X C, et al. Application of particle swarm optimization to interpret Rayleigh wave dispersion curves[J]. Journal of Applied Geophysics, 2012, 84: 1-13. [15] Wang S, He D K. A new method for Rayleigh wave inversion by geological unit merging[J]. E3S Web of Conferences, 2020, 165: 03008. [16] Lacoss R T, Kelly E J, Toksöz M N. Estimation of seismic noise structure using arrays[J]. GEOPHYSICS, 1969, 34(1): 21-38. [17] Aki K. Space and time spectra of stationary stochastic waves, with special reference to microtremors[J]. Bulletin, Earthquake Research Institute, 1957, 35: 415-456. [18] 鲁来玉. 基于平面波模型重访地震背景噪声互相关及空间自相关(SPAC)[J]. 地球与行星物理论评, 2021, 52(2): 123-163. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXP202102001.htmLu Laiyu. Revisiting the cross-correlation and spatial autocorrelation (SPAC) of the seismic ambient noise based on the plane wave model[J]. Reviews of Geophysics and Planetary Physics, 2021, 52(2): 123-163. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXP202102001.htm [19] 徐佩芬, 李世豪, 凌甦群, 等. 利用SPAC法估算地壳S波速度结构[J]. 地球物理学报, 2013, 56(11): 3846-3854. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201311026.htmXu Peifen, Li Shihao, Ling Suqun, et al. Application of SPAC method to estimate the crustal S-wave velocity structure[J]. Chinese Journal of Geophysics, 2013, 56(11): 3846-3854. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX201311026.htm [20] Asten M W. On bias and noise in passive seismic data from finite circular array data processed using SPAC methods[J]. GEOPHYSICS, 2006, 71(6): 153-162. [21] 夏江海. 高频面波方法[M]. 武汉: 中国地质大学出版社, 2015: 105-106. [22] 朱广生, 桂志先, 熊新斌, 等. 密度与纵横波速度关系[J]. 地球物理学报, 1995, 38(S1): 260-264. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX5S1.025.htmZhu Guangsheng, Gui Zhixian, Xiong Xinbin, et al. Relationships between density and p-wave, s-wave velocities[J]. Chinese Journal of Geophysics, 1995, 38(S1): 260-264. https://www.cnki.com.cn/Article/CJFDTOTAL-DQWX5S1.025.htm [23] 肖柏勋. 高模式瑞雷面波及其正反演研究[D]. 长沙: 中南大学, 2000. [24] 赵雪然, 刘四新, 张健桥, 等. 基于不同半径分段组合的微动探测方法研究[J]. 世界地质, 2020, 39(1): 150-158. https://www.cnki.com.cn/Article/CJFDTOTAL-SJDZ202001015.htmZhao Xueran, Liu Sixin, Zhang Jianqiao, et al. Research on microtremor survey method based on segmentation combination of different radii[J]. Global Geology, 2020, 39(1): 150-158. https://www.cnki.com.cn/Article/CJFDTOTAL-SJDZ202001015.htm [25] 刘庆华. 基于SPAC和NCF技术的被动源面波浅勘方法研究[D]. 北京: 中国地震局地球物理研究所, 2015. -
点击查看大图
图(9) / 表(3)
计量
- 文章访问数: 290
- HTML全文浏览量: 57
- PDF下载量: 39
- 被引次数: 0