微动HVSR(horizontal-to-vertical spectral ratio)法是一种高效、非侵入式的地球物理探测手段,广泛用于城市地质调查与工程勘探。然而,行人、车辆等瞬态干扰会导致HVSR曲线畸变。现有瞬态干扰剔除方法存在局限:STA/LTA(short-term-aver...微动HVSR(horizontal-to-vertical spectral ratio)法是一种高效、非侵入式的地球物理探测手段,广泛用于城市地质调查与工程勘探。然而,行人、车辆等瞬态干扰会导致HVSR曲线畸变。现有瞬态干扰剔除方法存在局限:STA/LTA(short-term-average over long-term-average)法易误判且调参复杂,手动剔除法效率低,频域窗剔除法仅关注峰值频率信息。为此,本文提出一种基于机器学习的微动HVSR数据干扰信号压制方法。首先通过提取曲线形态特征训练曲线剔除模型,用于剔除HVSR数据中显著偏离平均趋势的离群HVSR曲线;随后提取峰值特征训练峰值识别模型,用于识别曲线中的有效共振峰值;最后结合DBSCAN(density-based spatial clustering of applications with noise)聚类算法对识别出的有效峰值进行聚类与二次剔除,去除频率和振幅异常的峰值所在曲线。曲线剔除模型和峰值识别模在测试集上的F_(1)分数分别为0.967和0.985,均表现出优异的分类性能。实际算例结果表明,本文方法在异常曲线剔除方面相较于STA/LTA法和频域窗剔除法具有更高的稳定性与准确性,处理后的曲线频谱分布更加集中,标准差曲线更加收敛,峰值更加清晰稳定。本文方法在实现高效自动处理的同时,剔除效果与手动剔除法高度一致。展开更多
The exploration of urban underground spaces is of great significance to urban planning,geological disaster prevention,resource exploration and environmental monitoring.However,due to the existing of severe interferenc...The exploration of urban underground spaces is of great significance to urban planning,geological disaster prevention,resource exploration and environmental monitoring.However,due to the existing of severe interferences,conventional seismic methods cannot adapt to the complex urban environment well.Since adopting the single-node data acquisition method and taking the seismic ambient noise as the signal,the microtremor horizontal-to-vertical spectral ratio(HVSR)method can effectively avoid the strong interference problems caused by the complex urban environment,which could obtain information such as S-wave velocity and thickness of underground formations by fitting the microtremor HVSR curve.Nevertheless,HVSR curve inversion is a multi-parameter curve fitting process.And conventional inversion methods can easily converge to the local minimum,which will directly affect the reliability of the inversion results.Thus,the authors propose a HVSR inversion method based on the multimodal forest optimization algorithm,which uses the efficient clustering technique and locates the global optimum quickly.Tests on synthetic data show that the inversion results of the proposed method are consistent with the forward model.Both the adaption and stability to the abnormal layer velocity model are demonstrated.The results of the real field data are also verified by the drilling information.展开更多
微动HVSR(Horizontal to Vertical Spectral Ratio)谱比法是分辨第四系覆盖层、松散层、尾矿堆积物厚度较为有效的方法,且工作方便快捷,利于施工。柳州市融水县多个矿区急需查明矿区内尾矿堆场储量,为更加清楚地获得矿区内尾矿堆场储量...微动HVSR(Horizontal to Vertical Spectral Ratio)谱比法是分辨第四系覆盖层、松散层、尾矿堆积物厚度较为有效的方法,且工作方便快捷,利于施工。柳州市融水县多个矿区急需查明矿区内尾矿堆场储量,为更加清楚地获得矿区内尾矿堆场储量情况,本文开展微动HVSR谱比法测量,结合矿区相关资料,划分了尾矿堆积物与基岩的分界线并分析其起伏情况,对尾矿堆积场厚度进行推断,并估算其储量。通过多个钻孔验证,钻孔揭露的尾矿堆积物厚度与物探异常推断的成果基本一致,取得了比较良好的物探勘查效果,为下一步尾矿库的处理及下一步工作提供了物探依据,该法可为划分尾矿库堆积物厚度工作提供参考。展开更多
文摘微动HVSR(horizontal-to-vertical spectral ratio)法是一种高效、非侵入式的地球物理探测手段,广泛用于城市地质调查与工程勘探。然而,行人、车辆等瞬态干扰会导致HVSR曲线畸变。现有瞬态干扰剔除方法存在局限:STA/LTA(short-term-average over long-term-average)法易误判且调参复杂,手动剔除法效率低,频域窗剔除法仅关注峰值频率信息。为此,本文提出一种基于机器学习的微动HVSR数据干扰信号压制方法。首先通过提取曲线形态特征训练曲线剔除模型,用于剔除HVSR数据中显著偏离平均趋势的离群HVSR曲线;随后提取峰值特征训练峰值识别模型,用于识别曲线中的有效共振峰值;最后结合DBSCAN(density-based spatial clustering of applications with noise)聚类算法对识别出的有效峰值进行聚类与二次剔除,去除频率和振幅异常的峰值所在曲线。曲线剔除模型和峰值识别模在测试集上的F_(1)分数分别为0.967和0.985,均表现出优异的分类性能。实际算例结果表明,本文方法在异常曲线剔除方面相较于STA/LTA法和频域窗剔除法具有更高的稳定性与准确性,处理后的曲线频谱分布更加集中,标准差曲线更加收敛,峰值更加清晰稳定。本文方法在实现高效自动处理的同时,剔除效果与手动剔除法高度一致。
基金Supported by projects of National Natural Science Foundation of China(No.42074150)National Key Research and Development Program of China(No.2023YFC3707901)Futian District Integrated Ground Collapse Monitoring and Early Warning System Construction Project(No.FTCG2023000209).
文摘The exploration of urban underground spaces is of great significance to urban planning,geological disaster prevention,resource exploration and environmental monitoring.However,due to the existing of severe interferences,conventional seismic methods cannot adapt to the complex urban environment well.Since adopting the single-node data acquisition method and taking the seismic ambient noise as the signal,the microtremor horizontal-to-vertical spectral ratio(HVSR)method can effectively avoid the strong interference problems caused by the complex urban environment,which could obtain information such as S-wave velocity and thickness of underground formations by fitting the microtremor HVSR curve.Nevertheless,HVSR curve inversion is a multi-parameter curve fitting process.And conventional inversion methods can easily converge to the local minimum,which will directly affect the reliability of the inversion results.Thus,the authors propose a HVSR inversion method based on the multimodal forest optimization algorithm,which uses the efficient clustering technique and locates the global optimum quickly.Tests on synthetic data show that the inversion results of the proposed method are consistent with the forward model.Both the adaption and stability to the abnormal layer velocity model are demonstrated.The results of the real field data are also verified by the drilling information.
文摘微动HVSR(Horizontal to Vertical Spectral Ratio)谱比法是分辨第四系覆盖层、松散层、尾矿堆积物厚度较为有效的方法,且工作方便快捷,利于施工。柳州市融水县多个矿区急需查明矿区内尾矿堆场储量,为更加清楚地获得矿区内尾矿堆场储量情况,本文开展微动HVSR谱比法测量,结合矿区相关资料,划分了尾矿堆积物与基岩的分界线并分析其起伏情况,对尾矿堆积场厚度进行推断,并估算其储量。通过多个钻孔验证,钻孔揭露的尾矿堆积物厚度与物探异常推断的成果基本一致,取得了比较良好的物探勘查效果,为下一步尾矿库的处理及下一步工作提供了物探依据,该法可为划分尾矿库堆积物厚度工作提供参考。
