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在基于地震波法的隧道探测中,高质量去噪是提升不良地质体成像精度关键。然而,传统方法依赖固定阈值或频域滤波去噪效果差,现有机器学习算法则受限于泛化能力与实时性,难以满足工程需求。因此本文通过建立联合优化去噪模型定向压制残余噪声,基于压缩感知进行联合优化去噪,通过融合震源扫描信号的确定性先验特征,建立了信号重构与噪声压制联合优化机制。通过多维度定量评价与频谱特征进行分析,与传统滤波方法、小波变换、曲波变换和TV正则化技术相比,本文所提方法在信噪比、结构相似性及均方根误差等指标上均优于上述方法,证实了所提方法在复杂地质结构保真度和噪声压制效果等方面的综合优势,在构造边缘锐化、薄层信号恢复及伪影控制等方面展现出显著优势,能够提升不良地质体探测识别精度。最后,通过江苏某地地铁隧道探测,模拟隧道前方存在的岩溶空洞类不良地质体,使用本文提出的方法对地震数据处理后实现了地铁隧道有效成像,效果优于常规成像方法,证实了该方法能够有效提高前方不良地质体成像效果。
Abstract：High-quality denoising is crucial for refining images of adverse geological bodies in tunnel seismic surveys. However, traditional methods relying on fixed thresholds or frequency-domain filtering yield suboptimal denoising results, while machine learning algorithms are constrained by limited generalization capability and realtime performance, rendering them inadequate for meeting engineering requirements. To address these issues, a joint optimization denoising model was constructed in this study to directionally suppress residual noise. Building upon the framework of compressed sensing, a joint optimization mechanism which couples signal reconstruction with noise suppression was established by incoporating the deterministic prior features inherent in the source scanning signals. Comprehensive evaluations involving multidimensional quantitative metrics and spectral analysis demonstrate that the proposed method consistently outperforms traditional filtering, wavelet transform,curvelet transform, and total variation(TV) regularization techniques in terms of signal-to-noise ratio(SNR),structural similarity index(SSIM), and root mean square error(RMSE). The method shows clear advantages in structural fidelity preservation in complex geological environments and noise suppression efficacy. It exhibits particular strength in structural edge sharpening, thin-layer signal recovery, and artifact suppression—key factors in improving the identification accuracy of adverse geological bodies. Finally, geophysical prospecting was conducted in a subway tunnel in Jiangsu Province, where karst cavities as adverse geological bodies were simulated ahead of the tunnel face. Seismic data processed by the proposed method yield high-quality imaging results of the tunnel structure, outperforming conventional imaging methods. Such results further verify that the proposed method can significantly enhance the imaging resolution of adverse geological structures ahead.
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基本信息:
DOI：10.13532/j.jmsce.cn10-1638/td.2025-1320
中图分类号:U452.11
引用信息:
[1]石少帅,邴纪晓,郭伟东,等.隧道不良地质体的稀疏基联合去噪方法研究[J].采矿与岩层控制工程学报,2026,8(03):153-168.DOI:10.13532/j.jmsce.cn10-1638/td.2025-1320.
基金信息:
国家重点研发计划资助项目（2023YFB2390200）; 山东省自然科学基金资助项目（ZR2024QE460）
2026-06-15
2026-06-15