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Article(id=1241046467687797499, tenantId=1146029695717560320, journalId=1240670690148397066, issueId=1241046461174043350, articleNumber=null, orderNo=null, doi=10.3963/j.issn.1001-487X.2025.01.021, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1721491200000, receivedDateStr=2024-07-21, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773818135425, onlineDateStr=2026-03-18, pubDate=1741968000000, pubDateStr=2025-03-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773818135425, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773818135425, creator=13701087609, updateTime=1773818135425, updator=13701087609, issue=Issue{id=1241046461174043350, tenantId=1146029695717560320, journalId=1240670690148397066, year='2025', volume='42', issue='1', pageStart='1', pageEnd='198', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=0, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773818133871, creator=13701087609, updateTime=1773820872662, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241057948554817923, tenantId=1146029695717560320, journalId=1240670690148397066, issueId=1241046461174043350, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241057948554817924, tenantId=1146029695717560320, journalId=1240670690148397066, issueId=1241046461174043350, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=175, endPage=182, ext={EN=ArticleExt(id=1241046467985593093, articleId=1241046467687797499, tenantId=1146029695717560320, journalId=1240670690148397066, language=EN, title=A Noise Reduction Method for Tunnel Blasting Vibration Signals based on OOA-VMD, columnId=1240702076553065119, journalTitle=Blasting, columnName=BLASTING SAFETY, runingTitle=null, highlight=null, articleAbstract=

Accurate acquisition of blasting vibration signals is essential for analyzing the harmful effects of blasting operations. However, geological conditions, electromagnetic interference, and instrument errors can introduce significant high-frequency noise into the collected signals, leading to distortion and inaccurate data interpretation. To address this issue, a signal decomposition algorithm based on Ospley Optimization Algorithm (OOA) is proposed to optimize Variational Mode Decomposition (VMD). Multiscale Permutation Entropy (MPE) is also employed to construct a noise reduction model for tunnel blasting vibration signals. OOA is iteratively applied to determine the optimal VMD parameters (K & α) and obtain the intrinsic mode formula (IMF) using the maximum information coefficient as the fitness function. The MPE values of each decomposed signal are then used to identify the noise components, which are removed to reconstruct the denoised signal. This coupled algorithm was applied to analyze the blasting effects in Dashan Tunnel, Yunnan Province. The results demonstrate that the proposed optimization algorithm effectively decomposes the signal and eliminates noise without significantly affecting the low-frequency energy. The OOA-VMD denoising method's performance is superior to the complete ensemble empirical mode decomposition (CEEMD) and conventional VMD algorithm, thereby verifying its reliability.

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WU Li (1963-), male, born in Zhuzhou, Hunan province, professor, mainly engaged in blasting and rock dynamics research, (E-mail) .
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爆破工程中现场爆破监测获取准确的爆破振动信号是分析有害效应的基础，由于地质条件、电磁干扰和仪器误差等因素对隧道爆破振动信号采集的不利影响，实测信号往往存在大量高频噪声，可能导致信号曲线畸变和数据失真。本研究提出了一种基于鱼鹰优化算法（OOA）优化变分模态分解（VMD）的信号分解算法，并利用多尺度排列熵（MPE）构建了适用于隧道爆破振动信号的降噪模型。采用最大信息系数作为适应度函数，OOA迭代计算最优VMD参数（K & α），并得到本征模态函数（IMF），利用每个分解信号的MPE值来识别噪声，通过去除噪声分量并重构得到降噪信号。采用该优化算法对云南省大山隧道实测爆破振动信号进行降噪处理，结果表明：新的优化算法能够分解出最优信号并消除噪声，且对信号的低频能量影响较小。该方法的降噪效果优于完全集成经验模态分解（CEEMD）和传统VMD算法，从而验证了OOA-VMD降噪算法的可靠性。该研究对隧道工程爆破振动信号的处理和噪声干扰问题的处理具有重要工程应用价值。

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吴立（1963-），男，湖南株洲人，教授，主要研究爆破与岩石动力学，（E-mail）。
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马荣增（1974-），男，福建永定人，大学本科，主要从事公路、市政、房建等专业施工及设计管理工作，（E-mail）。

MA Rong-zeng (1974-), male, born in Yongding, Fujian province, bachelor degree, senior engineer, mainly engaged in highway, municipal, housing construction and other professional construction and design management, (E-mail) .

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马荣增（1974-），男，福建永定人，大学本科，主要从事公路、市政、房建等专业施工及设计管理工作，（E-mail）。

MA Rong-zeng (1974-), male, born in Yongding, Fujian province, bachelor degree, senior engineer, mainly engaged in highway, municipal, housing construction and other professional construction and design management, (E-mail) .

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马荣增（1974-），男，福建永定人，大学本科，主要从事公路、市政、房建等专业施工及设计管理工作，（E-mail）。

MA Rong-zeng (1974-), male, born in Yongding, Fujian province, bachelor degree, senior engineer, mainly engaged in highway, municipal, housing construction and other professional construction and design management, (E-mail) .

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tableContent=null), ArticleFig(id=1241057561437327846, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241046467687797499, language=CN, label=图5, caption=原始信号和降噪信号比较, figureFileSmall=Sc0sFty7AVeCrIURj7pzXA==, figureFileBig=1Ts9WRq9JcjtTjEJzhgUAA==, tableContent=null), ArticleFig(id=1241057562951471594, tenantId=1146029695717560320, journalId=1240670690148397066, articleId=1241046467687797499, language=EN, label=Table 1, caption=

Mean value of MPE

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  IMF1 IMF2 IMF3 IMF4 IMF5 IMF6 IMF7 IMF8 IMF9 IMF10
MPE0.31520.35520.38520.41810.46790.50370.56370.62940.68020.7254
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MPE的平均值

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  IMF1 IMF2 IMF3 IMF4 IMF5 IMF6 IMF7 IMF8 IMF9 IMF10
MPE0.31520.35520.38520.41810.46790.50370.56370.62940.68020.7254
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Denoising index

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原始信号降噪算法分解数K RMSE SNR/dB
s1CEEEMD90.1099.242
VMD90.06713.262
OOA－VMD100.05814.401
s2CEEEMD80.0919.320
VMD90.07215.025
OOA－VMD90.07215.541
s3CEEEMD80.1159.210
VMD90.08115.625
OOA－VMD100.06916.658
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降噪指数

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原始信号降噪算法分解数K RMSE SNR/dB
s1CEEEMD90.1099.242
VMD90.06713.262
OOA－VMD100.05814.401
s2CEEEMD80.0919.320
VMD90.07215.025
OOA－VMD90.07215.541
s3CEEEMD80.1159.210
VMD90.08115.625
OOA－VMD100.06916.658
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基于OOA-VMD的隧道爆破振动信号降噪方法
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马荣增 1 , 杜泽辉 2, 3 , 李文 1, 3 , 彭亚雄 4 , 吴立 3
爆破 | 安全与管理 2025,42(1): 175-182
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爆破 | 安全与管理 2025, 42(1): 175-182
基于OOA-VMD的隧道爆破振动信号降噪方法
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马荣增1 , 杜泽辉2, 3, 李文1, 3, 彭亚雄4, 吴立3
作者信息
  • 1.中交二航局（昆明）建设有限公司，昆明　650200
  • 2.中交武汉港湾工程设计研究院有限公司　海工结构新材料及维护加固湖北省重点实验室，武汉　430040
  • 3.中国地质大学（武汉）　岩土钻掘与防护教育部工程研究中心，武汉　430074
  • 4.湖南科技大学　岩土工程稳定控制与健康监测湖南省重点实验室，湘潭　411201

    • 马荣增（1974-），男，福建永定人，大学本科，主要从事公路、市政、房建等专业施工及设计管理工作，（E-mail）。

    MA Rong-zeng (1974-), male, born in Yongding, Fujian province, bachelor degree, senior engineer, mainly engaged in highway, municipal, housing construction and other professional construction and design management, (E-mail) .

通讯作者:

吴立（1963-），男，湖南株洲人，教授，主要研究爆破与岩石动力学，（E-mail）。
A Noise Reduction Method for Tunnel Blasting Vibration Signals based on OOA-VMD
Rong-zeng MA1 , Ze-hui DU2, 3, Wen LI1, 3, Ya-xiong PENG4, Li WU3
Affiliations
  • 1.CCCC SHEC (Kunming) Construction Co., Ltd., Kunming 650200, China
  • 2.Hubei Key Laboratory of Advanced Materials & Reinforcement Technology Research for Marine Environment Structures, CCCC Wuhan Harbour Engineering Design and Research Co., Ltd., Wuhan 430040, China
  • 3.Engineering Research Center of Rock-Soil Drilling & Excavation and Protection, China University of Geoscieneces, Wuhan 430074, China
  • 4.Hunan Provincial Key Laboratory of Geotechnical Engineering for Stability Control and Health Monitoring, Hunan University of Science and Technology, Xiangtan 411201, China
出版时间: 2025-03-15 doi: 10.3963/j.issn.1001-487X.2025.01.021
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摘要
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爆破工程中现场爆破监测获取准确的爆破振动信号是分析有害效应的基础，由于地质条件、电磁干扰和仪器误差等因素对隧道爆破振动信号采集的不利影响，实测信号往往存在大量高频噪声，可能导致信号曲线畸变和数据失真。本研究提出了一种基于鱼鹰优化算法（OOA）优化变分模态分解（VMD）的信号分解算法，并利用多尺度排列熵（MPE）构建了适用于隧道爆破振动信号的降噪模型。采用最大信息系数作为适应度函数，OOA迭代计算最优VMD参数（K & α），并得到本征模态函数（IMF），利用每个分解信号的MPE值来识别噪声，通过去除噪声分量并重构得到降噪信号。采用该优化算法对云南省大山隧道实测爆破振动信号进行降噪处理，结果表明：新的优化算法能够分解出最优信号并消除噪声，且对信号的低频能量影响较小。该方法的降噪效果优于完全集成经验模态分解（CEEMD）和传统VMD算法，从而验证了OOA-VMD降噪算法的可靠性。该研究对隧道工程爆破振动信号的处理和噪声干扰问题的处理具有重要工程应用价值。

爆破振动信号  /  降噪算法  /  隧道  /  Osprey优化算法  /  VMD分解算法

Accurate acquisition of blasting vibration signals is essential for analyzing the harmful effects of blasting operations. However, geological conditions, electromagnetic interference, and instrument errors can introduce significant high-frequency noise into the collected signals, leading to distortion and inaccurate data interpretation. To address this issue, a signal decomposition algorithm based on Ospley Optimization Algorithm (OOA) is proposed to optimize Variational Mode Decomposition (VMD). Multiscale Permutation Entropy (MPE) is also employed to construct a noise reduction model for tunnel blasting vibration signals. OOA is iteratively applied to determine the optimal VMD parameters (K & α) and obtain the intrinsic mode formula (IMF) using the maximum information coefficient as the fitness function. The MPE values of each decomposed signal are then used to identify the noise components, which are removed to reconstruct the denoised signal. This coupled algorithm was applied to analyze the blasting effects in Dashan Tunnel, Yunnan Province. The results demonstrate that the proposed optimization algorithm effectively decomposes the signal and eliminates noise without significantly affecting the low-frequency energy. The OOA-VMD denoising method's performance is superior to the complete ensemble empirical mode decomposition (CEEMD) and conventional VMD algorithm, thereby verifying its reliability.

blasting vibration signal  /  denoising algorithm  /  tunnel  /  osprey optimization algorithm  /  variational mode decomposition algorithm
马荣增, 杜泽辉, 李文, 彭亚雄, 吴立. 基于OOA-VMD的隧道爆破振动信号降噪方法. 爆破, 2025 , 42 (1) : 175 -182 . DOI: 10.3963/j.issn.1001-487X.2025.01.021
Rong-zeng MA, Ze-hui DU, Wen LI, Ya-xiong PENG, Li WU. A Noise Reduction Method for Tunnel Blasting Vibration Signals based on OOA-VMD[J]. Blasting, 2025 , 42 (1) : 175 -182 . DOI: 10.3963/j.issn.1001-487X.2025.01.021
正文
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爆破是目前隧道开挖最有效的方法，炸药爆炸势必引起隧道围岩和结构振动，对施工安全造成极大威胁。掌握隧道爆破有害效应的前提是能够获取准确的爆破振动信号[1]，由于采爆破振动信号常受到地层地质、电磁环境、仪器误差等因素的影响，使得在采集真实信号的同时也会包含大量噪声信号，导致实测信号发生畸变。因此，在分析爆破信号特征前进行降噪处理至关重要。
爆破振动信号具有非平稳随机特性，针对此类信号的降噪技术包括小波算法[2，3]、EMD算法和VMD算法[4－7]。谢全民等构建了爆破振动信号降噪和特征分析的SGWP小波包方法[8]，验证了该方法的降噪效果并应用于实际工程。彭亚雄等提出了一种具有自适应能力的修正EMD算法[9]，实现了爆破振动信号平滑降噪。然而，小波分解需要人工确定基函数，具有较大随机性和人为主观性；EMD类算法具有多分辨率特性，但分解过程会产生模态混叠。
VMD可以解决端点畸变、模式混乱等问题，且不需要选取核函数，该算法广泛应用于各类非平稳信号的分析中。因此，该方法在最新信号处理方面得到广泛应用。一些学者已经在分析爆破信号特征基础上[10，11]，构建了基于VMD算法的爆破振动信号降噪技术，并取得了较好的降噪效果。同时，也发现VMD参数[K，α]的值较难选取，往往依赖于工程师和研究人员的经验，导致主观性对分析结果影响极大。鱼鹰优化算法(OOA)是2023年由Mohammad Dehghani和Pavel Trojovsky提出的一种新型智能优化算法，该算法具有收敛速度快、精度高的特点[12]。本文采用鱼鹰优化算法自适应确定VMD参数，避免人工选择带来的误差，并与适合于爆破振动信号的MPE噪声识别方法相结合[13]，构建了适用于隧道爆破振动信号的降噪处理方法，进行了工程应用并取得了较好效果。
1　VMD和OOA算法
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1.1　VMD算法
VMD方法是一种处理非递归模态信号的分解技术，认为信号由给定中间频率和有限带宽的子信号组成。通过求解变分问题，识别中频和带宽约束，提取频域中的频率分量，推导出模态方程。该方法可以将复杂的非平稳随机的爆破振动信号分解为不同的频率分量，从而实现有效的降噪和分析[14]。
约束变分公式如下所示
式中：x(t)是原始信号；{uk}是IMF；{ωk}是中频；δ(t)是脉冲方程。
采用补偿因子α和拉格朗日算子求解式(1)，利用参数，和λn＋1进行交替更新
原始和重构信号x(t)、可以表示为
1.2　鱼鹰优化算法
鱼鹰优化算法受到鱼鹰捕食行为的启发，分两个阶段解决复杂的优化问题。首先，确定位置并寻找目标(全局探索)[15]。然后，将目标运送到合适的位置(局部探索)。这种方法通过整合全局和局部探索技术，提供了解决优化问题的综合策略，反映了鱼鹰探索效率。
(1)种群初始化
鱼鹰种群可以用公式(4)中的矩阵来构建，鱼鹰在搜寻区域中的位置通过公式(5)随机初始化。
式中：X为鱼鹰位置矩阵；Xi为鱼鹰位置(可能的解)；xi，j为其jit维度；N为鱼鹰数量；m为问题变量数量；ri，j为0~1之间的随机数；lbj、ubj分别为变量的下限和上限。
位置初始化后，根据优化问题计算适应度值。
式中：F是目标函数；Fi是第i个鱼鹰值。
(2)位置确定和搜索目标
鱼鹰以其出色的视力而闻名，采用潜水战术进行水下捕鱼。这种行为显著改变了鱼鹰在搜索空间中的位置，增强了算法的探索能力。鱼鹰狩猎策略作为算法探索的模型，模拟捕食者与猎物之间的相互作用，以优化搜索工作并有效地操纵搜索空间，从而获得更好的优化结果。
式中：FPi为第i个的鱼鹰位置集合；Xbest为最佳位置。
该算法模拟了鱼鹰狩猎行为，它会随机检测目标并捕获目标。公式(7)根据此移动计算出一个新的位置，如果新位置更优，则利用公式(8)替代原来的位置。
(3)把目标带到正确位置
成功捕获目标后，鱼鹰会将目标带到合适位置。该策略在局部探索阶段有效地利用了面向对象的方法，从而加快了寻找最优解决方案的进程。建模的运动有助于有效导航搜索空间，增强算法的搜索能力并提高改进解决方案的接近度。
在模拟鱼鹰行为时，鱼鹰群体中的每个个体，通过公式(9)确定一个随机生成的新“进食”位置。如果目标函数值在这个新位置有所改善，它将通过公式(10)取代鱼鹰之前的位置。这种方法模仿了自然鱼鹰的行为并增强了算法的优化过程。
第i个鱼鹰新位置及其第j维度和由[0，1]中的随机数确定。变量t和T表示迭代的次数和总迭代次数。
2　OOA－VMD降噪
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2.1　参数优化
VMD算法主要依赖于两个关键参数：分解数(K)和补偿因子(α)。VMD方法区分不同频率模态响应的准确度取决于K的值。较低的K值可能无法区分有效的模态响应分量，而较高的K值可能会错误地将同一频率的模态响应分离为不同的分量。同样，过大的α值会导致信号成分的丢失，过小的α值又会产生冗余信息。人工确定参数对于处理大规模数据或准确分离复杂信号的模式效率不高。本研究将OOA算法应用于VMD分解数(K)和补偿因子(α)的自适应确定，具体流程如下：对VMD参数[K，α]初始化，采用合适的适应度函数评估VMD参数的适应度值，持续进行迭代更新和收敛因子评估过程，直到满足终止条件，此时输出最优的VMD参数。
其中，合适的适应度函数至关重要，最大信息系数是一种用于量化两个变量之间依赖关系强度的统计量，能够在不同类型的关联中(如线性、非线性、周期性等)提供一致的度量[16]。通过比较x和y之间的互信息与它们各自的信息熵来得到的最大值，如公式(11)所示。针对爆破振动信号特点，通过比较选择了最大信息系数作为适应度函数构建OOA－VMD分解算法。
式中：MIC()为最大信息系数；MI()为两个参数的互信息；Entropy()为信息熵。
2.2　降噪处理
MPE算法可以检测信号的随机性和动态突变性，是处理爆破振动信号中噪声的有效手段[13]。计算过程为：将时间序列x＝{x1，x2，…，xL}在多个尺度上进行粗粒化，然后计算PE值。具体计算过程如下：
①多尺度粗化和重构
为了获得粗重构的时间序列()，将时间序列分割成各个尺度，并计算每个段的平均值。粗粒度重构公式如下
式中：为时间序列；τ为延迟；m为嵌入维数；为重构序列；s为比例因子。
②重建信号排序
升序得到m阶排列，并计算每个排列的出现频率N1和概率。
③MPE计算
计算不同尺度因子的的PE值，并对其进行归一化，得到MPE。
OOA－VMD算法自适应地将信号分解为K个IMF分量。采用MPE对爆破振动信号进行处理，阈值设定为不大于0.6，可以辨别出噪声成分，通过重构获得降噪信号。
2.3　降噪效果
通过应用前后的RMSE和信噪比SNR来评价降噪算法的有效性，评价指标定义
式中：为降噪后信号的幅度；SNR衡量的是噪声与信号能量的关系；RMSE反映的是噪声能量的平均值。RMSE越小，表示降噪效果越好；SNR越大，表示对信号特征的影响越小。
3　工程应用
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3.1　信号降噪
云南省天保至猴桥高速公路天保至文山段(天麻段)大山隧道位于云南省麻栗坡县，进口端在大岩前村，出口端在香色坡，为穿越北东向山体和南北向岭峰而修建。施工场地地层岩性主要为第四系全新统残坡粘土、砾石、块石，泥盆系中强—中风化白云质灰岩，节理裂隙发育，厚度较大。
在隧道开挖过程中进行现场爆破振动监测，选取典型实测信号作为研究对象(图1)。根据奈奎斯特采样定理，采集采样点3200个，采样时间为1 s，采样频率为3200 sps，信号实测频率为1600 Hz。
由于复杂岩层和地质结构的影响，现场测得的信号图显示其中有大量高频噪声，给信号特征识别和能量计算带来很大困难。为了降低测量信号的噪声，利用OOA对VMD参数[K，α]进行优化，并结合MPE算法进行降噪。计算时设定种群数为30，迭代次数为50，K取值为[2，50]，α取值为[10，3000]。通过迭代计算输出最优参数为[K，α]＝(10，982)，计算得到的各IMF分量如图2所示。
为了准确识别噪声成分，计算每个IMF的平均MPE。经过试验计算，确定MPE相关参数为m＝6、τ＝1、s＝5，所得平均MPE值如表1所示。
由于MPE阈值为0.6，所以IMF8~IMF10被判定为噪声。重构其余IMF，得到降噪信号。降噪前后信号对比如图3所示。降噪过程证明了OOA算法动态获取VMD分解参数的能力，从而产生高质量的分解信号。这种OOA－VMD方法有助于减少由于人为因素造成的计算误差。由图3可知，降噪前后信号特征保持一致，噪声明显降低，振动速度幅值的影响可以忽略。结果证明了OOA－VMD降噪技术在保持信号纯度方面的有效性。
3.2　能量特征分析
AOK时频分析方法能很好地提取测得的信号特征[17]。同时，对OOA－VMD降噪算法在信号降噪效果方面的有效性进行了进一步的验证，并利用AOK分析法对原始信号和降噪后的信号能量进行了对比。得到对应的时频能量谱图，如图4所示。其中X表示时频能量，Y表示主频率。
图4表明，振动峰值能量集中在持续时间0~0.5 s、频率0~300 Hz范围内。对比图5(a)、(b)可知，降噪前后信号的主频率均为139 Hz，峰值能量分别为233.4~229.1 J，峰值能量仅降低了2.4 J。原始信号的信号频谱中有明显的高频噪声点，但降噪后的信号中这部分振动能量被明显消除，如红色框所示。因此，研究结果表明，OOA－VMD降噪算法可以有效去除高频噪声，同时对低频振动的影响最小。
3.3　比较分析
为了进一步讨论OOA－VMD算法的降噪效果，我们将两种常用的信号分解方法CEEMD算法和传统VMD算法与OOA－VMD算法进行了对比分析。除上面分析的信号s1外，随机选取两组测量信号进行比较，如图5所示。在以上三种方法的比较中，采用MPE算法作为噪声信号识别，可以保证分析的一致性。各方法得到的降噪信号与原始信号对比如图5所示，表2给出了模态数K、信号的SNR和RMSE计算结果。
如图5所示，采用3种降噪算法CEEMD、VMD和OOA－VMD对某隧道群测得的3个爆破振动信号进行处理。各种方法都可以在一定程度上降低噪声，并有效地消除高频噪声。在测量信号的分解与重构过程中，原始信号与降噪后信号的RMSE介于0.052~0.115之间，表明得到的3种模型在信号处理上都有良好的保真性。其中RMSE得到的信号是3组信号最大的，最小值却展现出了不同性，这说明CEEMD、VMD及OOA－VMD 3种方法处理不同的测量信号的保真度不同。另外，CEEMD方法降噪后的SNR在三组测量信号中最小。OOA－VMD方法稍大于VMD方法，但是它们的SNR却远远优于CEEMD方法，大大高于CEEMD方法，说明VMD类算法在降噪效果上明显优于EMD类算法。
在对比OOA－VMD和VMD算法的降噪效果时，可以发现两种方法分解信号的模态数K不同。虽然两种方法得到的信号s2的模态数相同，但OOA－VMD分解的模态函数比VMD算法得到的模态函数多1。这种精细的分解带来了更好的降噪效果，凸显了OOA－VMD方法在捕捉信号特征和去除噪声方面的卓越性能。从波形图和评价指标来看，OOA－VMD降噪方法有效地去除了高频噪声信号，保留了原始信号波形。该方法在降低隧道群爆破振动信号噪声和保留有效信息方面具有优势，在实际应用中显示出降低噪声和保持信号完整性的潜力。
4　结论
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由于地质环境复杂，隧道爆破开挖过程中产生的振动信号，将会受到极多的高频噪声污染，致使测量信号被这些高频信号所掩。针对这一问题，本文提出了基于OOA－VMD算法的爆破振动信号降噪方法。
(1)采用VMD算法分解采集的振动信号，得到频率分布不一致的IMF分量，以此建立低通滤波算法。OOA算法可以通过迭代计算自适应地获得VMD优化参数[K，α]，避免了人为判断的随机性和误差。
(2)爆破信号的降噪过程体现了OOA－VMD算法的精确分解和MPE的高频噪声识别在增强降噪方面的功效。利用AOK技术分析了降噪前后的时频能量谱，结果表明OOA－VMD方法能对500 Hz以上高频区的噪声能量进行有效消除，并且对低频区信号能量的影响也较小。
(3)CEEMD、VMD、OOA－VMD等降噪方法均表现出了较高的精度和较好的降噪效果，其中OOA－VMD方法相比CEEMD和VMD表现出了更优的降噪指标，证明了新方法的有效性。新算法为今后研究爆炸引起的振动传播及其在隧道入口处的有害影响提供了技术支持。
基金
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  • 岩土钻掘与防护教育部工程研究中心开放研究基金(202410)
  • 江汉大学省部共建精细爆破国家重点实验室、江汉大学爆破工程湖北省重点实验室联合开放基金资助(PBSKL2022D08)
  • 湖南省教育厅科学研究项目(22B0486; 22B0516)
文献
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2025年第42卷第1期
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doi: 10.3963/j.issn.1001-487X.2025.01.021
  • 接收时间：2024-07-21
  • 首发时间：2026-03-18
  • 出版时间：2025-03-15
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  • 收稿日期：2024-07-21
基金
State Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering(202410)
岩土钻掘与防护教育部工程研究中心开放研究基金(202410)
Jianghan University(PBSKL2022D08)
江汉大学省部共建精细爆破国家重点实验室、江汉大学爆破工程湖北省重点实验室联合开放基金资助(PBSKL2022D08)
Scientific research project of Hunan Provincial Department of Education(22B0486; 22B0516)
湖南省教育厅科学研究项目(22B0486; 22B0516)
作者信息
    1.中交二航局（昆明）建设有限公司，昆明　650200
    2.中交武汉港湾工程设计研究院有限公司　海工结构新材料及维护加固湖北省重点实验室，武汉　430040
    3.中国地质大学（武汉）　岩土钻掘与防护教育部工程研究中心，武汉　430074
    4.湖南科技大学　岩土工程稳定控制与健康监测湖南省重点实验室，湘潭　411201

通讯作者:

吴立（1963-），男，湖南株洲人，教授，主要研究爆破与岩石动力学，（E-mail）。
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