Abstract:

China has the largest number of tailings ponds in the world. Failure of tailings dams can cause severe ecological damage, economic losses, and casualties in downstream areas. Conventional monitoring approaches, including surface deformation measurements such as GPS and InSAR, as well as borehole-based instruments such as pore-pressure sensors and deep displacement meters, are limited by high costs, sensor vulnerability, and their limited ability to directly characterize the internal mechanical properties of dam structures. Therefore, there is an increasing need for damage-sensitive and cost-effective monitoring techniques that can directly evaluate the mechanical properties of tailings dams during operation. In recent years, seismic ambient-noise-based methods have attracted growing attention in subsurface structure investigation. Among these methods, the characteristic resonant frequency derived from the horizontal-to-vertical spectral ratio(HVSR) of seismic ambient noise is sensitive to variations in subsurface velocity and impedance, making it particularly suitable for structural health monitoring of tailings dams.

In this study, we deployed three seismometers at different elevations of the Xiaodonggou tailings dam in Chengde, China, to analyze HVSR characteristics and infer the internal layering of the dam. Full-day seismic records were divided into 72 non-overlapping 20 min windows, and the Konno-Ohmachi algorithm was applied to smooth the HVSR curves. Steady-state data were then selected to obtain representative HVSR curves for each station. Furthermore, an evolutionary inversion algorithm based on covariance matrix adaptation evolution strategy (CMA-ES) was employed to invert the HVSR curves and characterize the layered structure of the tailings dam. The reliability of CMA-ES was evaluated by comparing its inversion results with those obtained using particle swarm optimization(PSO), grid search, and the open-source software HVSRInv.

The results show that the peak frequencies of the HVSR curves at the three stations increase from 1.4 to 3.7Hz from the crest to the toe of the tailings dam, indicating the presence of a well-defined stratified interface with substantial lateral variation in depth. The HVSR curve at the crest exhibits the largest standard deviation, likely due to interference from discharge activities, highlighting the influence of external disturbances on monitoring results. The CMA-ES inversion results indicate a strong S-wave velocity impedance contrast between unsaturated and saturated tailings sand. Moreover, the depth of the upper unsaturated tailings layer inferred from the inversion agrees best with the phreatic surface measured in the borehole profile at the dam crest. Compared with PSO and grid search, CMA-ES produces the smallest interface-depth error, with the interface depth corresponding to the HVSR peak frequency being closest to the phreatic-line depth observed in borehole data. In addition, compared with the open-source HVSRInv software, the CMA-ES inversion yields a better fit to the observed HVSR curves, further supporting its accuracy and reliability for structural inversion of tailings dams.

These findings demonstrate the strong potential of HVSR-based methods for structural health monitoring of tailings dams and confirm the reliability of CMA-ES for HVSR inversion. This study provides a scientific basis for assessing tailings-dam stability and mitigating potential geohazards. Future work should focus on improving the anti-interference capability of the HVSR method and conducting long-term observations to evaluate its applicability and stability under varying environmental conditions.

Key words: tailings dam, HVSR, resonance frequency, structure inversion

摘要：

中国尾矿库总量居世界第一。尾矿库坝体失稳导致的溃坝事故易造成下游区域的生态破坏、 经济损失及人员伤亡, 因此亟需发展经济高效的无损监测方法, 以保障尾矿库运行过程中坝体结构的健康安全。近年来, 基于地震背景噪声的一系列方法在地球浅地表结构探测应用研究中日益受到重视, 其中通过分析地震背景噪声中水平与垂直分量的谱比(HVSR)得到的峰值频率对地下介质波速阻抗敏感的特性, 适用于尾矿坝的结构健康监测。文中基于布设在尾矿坝表面的3台三分量地震仪采集的数据, 利用HVSR方法分析尾矿坝不同位置的共振频率, 并将基于协方差矩阵自适应演化策略(CMA-ES)的进化算法应用于HVSR曲线的反演, 刻画尾矿坝结构的分层特征。研究结果表明, 3个台站的HVSR曲线均清晰地表现出与各自坝体位置相关的峰值频率, 尾矿坝顶部到坝趾的峰值频率从1.4Hz增加到3.7Hz。此外, 基于CMA-ES反演HVSR曲线的结果显示, 尾矿坝体存在一个由干、 湿尾矿颗粒分层所形成的横波速度强阻抗界面, 该界面的深度与浸润线深度高度一致。最后, 通过对比CMA-ES、 PSO和网格搜索及HVSRInv的反演结果, 发现基于CMA-ES方法得到的尾矿坝体结构界面深度误差最小。文中研究成果表明, HVSR方法可有效识别尾矿坝共振频率及其内部的分层结构, 为评估坝体潜在风险提供新的监测手段。

关键词: 尾矿坝, HVSR, 共振频率, 结构反演