防灾与环境

爆破振动信号的CPO-ICEEMDAN-小波阈值联合降噪

  • 杜密立 ,
  • 李祥龙 ,
  • 王建国 ,
  • 徐杰
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  • 1.昆明理工大学 国土资源工程学院,云南 昆明 650093;
    2.云南省教育厅爆破新技术工程研究中心,云南 昆明 650093
杜密立(2000—),男,云南昭通人,硕士,主要从事工程爆破研究工作。E-mail:dumili6311@163.com
李祥龙(1981—),男,安徽淮北人,博士,教授,博士生导师,主要从事岩石破碎及工程爆破等方面的研究工作。E-mail:lxl00014002@163.com

收稿日期: 2025-11-15

  网络出版日期: 2026-06-23

基金资助

国家自然科学基金面上项目(52274083);云南省基础研究计划面上项目(202201AT070178);云南省重大科技专项计划项目(202202AG050014)

Blasting Vibration Signal Denoising via Combined CPO-ICEEMDAN-Wavelet Thresholding

  • Du Mili ,
  • Li Xianglong ,
  • Wang Jianguo ,
  • Xu Jie
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  • 1. Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, P. R. China;
    2. Advanced Blasting Technology Engineering Research Center of Yunnan Province Education Department, Kunming 650093, P. R. China

Received date: 2025-11-15

  Online published: 2026-06-23

摘要

为解决矿山爆破振动信号中的噪声干扰问题,提出了一种基于冠豪猪优化算法(CPO)、改进自适应噪声完全集合经验模态分解(ICEEMDAN)、改进小波阈值的联合降噪算法。首先运用CPO对ICEEMDAN的关键参数进行全局寻优,将某露天矿山实测爆破振动信号自适应分解为一系列本征模态函数(IMF),利用多尺度排列熵(MPE)构建噪声识别阈值,筛选出含噪高频IMF分量,通过改进小波阈值处理后,将其与小于设定阈值的分量重新组合以完成降噪。通过与CEEMDAN-MPE及ICEEMDAN-传统小波阈值方法进行比较,3组振动信号降噪结果显示,本方法平均信噪比分别提升39.33%和19.93%,均方根误差降低2~3倍。同时,基于三维时频能量分析发现,去噪前后信号主频能量分布未发生变化,表明该方法在有效消除噪声干扰的同时,能完整保留信号主频能量特征。

本文引用格式

杜密立 , 李祥龙 , 王建国 , 徐杰 . 爆破振动信号的CPO-ICEEMDAN-小波阈值联合降噪[J]. 地下空间与工程学报, 2026 , 22(3) : 1115 -1126 . DOI: 10.20174/j.JUSE.2026.03.36

Abstract

To address the issue of noise interference in mining blasting vibration signals, a joint denoising algorithm combining the Crested Porcupine Optimizer (CPO), Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), and an enhanced wavelet thresholding method is proposed. First, the CPO algorithm was employed to globally optimize the key parameters of ICEEMDAN, adaptively decomposing the measured blasting vibration signals from an open-pit mine into a series of intrinsic mode functions (IMFs). A noise identification threshold was then constructed using multiscale permutation entropy (MPE) to screen out high-frequency noisy IMF components. These components were processed with the improved wavelet thresholding method and subsequently recombined with IMFs below the threshold to achieve denoising. Comparative experiments with CEEMDAN-MPE and ICEEMDAN-traditional wavelet thresholding methods demonstrate that the proposed method improves the average signal-to-noise ratio (SNR) by 39.33% and 19.93%, respectively, and reduces the root mean square error (RMSE) by 2~3 times across three sets of vibration signals. Furthermore, three-dimensional time-frequency energy analysis reveals that the main frequency energy distribution remains unchanged before and after denoising. These results indicate that the proposed method not only effectively eliminates noise interference but also fully preserves the main frequency energy characteristics of the original signal, demonstrating superior performance and engineering applicability.

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