基于数据驱动多尺度表征爆破信号特征提取研究

黄嘉瑞 ,  付晓强 ,  闫大洋 ,  苏洪 ,  霍艺强

六盘水师范学院学报 ›› 2026, Vol. 38 ›› Issue (3) : 12 -24.

PDF (2463KB)
六盘水师范学院学报 ›› 2026, Vol. 38 ›› Issue (3) : 12 -24. DOI: 10.16595/j.1671-055X.2026.03.002
富矿精开研究

基于数据驱动多尺度表征爆破信号特征提取研究

作者信息 +

Research on Feature Extraction of Blasting Signals based on Data Driven Multi-Scale Representation

Author information +
文章历史 +
PDF (2521K)

摘要

为消除爆破振动信号中噪声对信号特征提取的影响,利用变分模式分解(Variational mode decomposition,VMD)算法和Cramér von Mises(CVM)统计量,提出了一种基于数据驱动的爆破信号去噪方法,该方法使用统计距离的CVM度量来选择主要噪声模态,对其余模态局部使用CVM统计量来评估模态与估计噪声分布的关系,更接近噪声分布的模态被舍弃,从而获得更为真实的信号波动特性并进一步对消噪信号的时频分布、瞬时能量及边际能量分布进行了精细化特征提取。结果表明:CVM-VMD方法具有优越的数学和理论框架,使其对噪声和模态混叠具有很强的鲁棒性,使其在爆破信号去噪方面具有独特的优势。爆破信号时频谱表现出能量分布不均匀性、低频成分主导性和高频成分衰减性的显著特征。瞬时能量谱能够直观地展示信号能量在时域的实际变化情况,边际能量谱能够更准确地反映信号能量随频率的实际波动变化,为研究爆破机理和建筑物的受振影响评估提供了重要数据支持。

Abstract

To eliminate the influence of noise in blasting vibration signals on signal feature extraction, a data-driven blasting signal denoising method is proposed using Variational mode decomposition (VMD) algorithm and Cramér-von misse (CVM) statistic.This method uses the CVM metric of statistical distance to select the main noise modes, and locally uses the CVM statistic on the remaining modes to test the relationship between the modes and the estimated noise distribution. Modes closer to the noise distribution are discarded to obtain more realistic signal fluctuation characteristics. Subsequently, refined feature extraction is performed on time-frequency distribution, instantaneous energy, and marginal energy distribution of the denoised signal. The results indicate that the CVM-VMD method has superior mathematical and theoretical frameworks, making it highly robust to noise and mode mixing, and has unique advantages in blasting signals denoising. The time-frequency spectrum of blasting signals exhibits significant characteristics of uneven energy distribution, dominance of low-frequency components, and attenuation of high-frequency components. The instantaneous energy spectrum can intuitively display the changes in signal energy at different time points, while the marginal energy spectrum can more accurately reflect the fluctuation of signal energy with actual frequency, which provides important data support for studying blasting mechanisms and evaluating the vibration effects on buildings.

关键词

爆破信号 / 信号去噪 / CVM-VMD / 时频分析 / 能量分布

Key words

Blasting signals / Signals denoising / Cramér-von misse-variational mode decomposition / Time-frequency analysis / Energy distribution

引用本文

引用格式 ▾
黄嘉瑞,付晓强,闫大洋,苏洪,霍艺强. 基于数据驱动多尺度表征爆破信号特征提取研究[J]. 六盘水师范学院学报, 2026, 38(3): 12-24 DOI:10.16595/j.1671-055X.2026.03.002

登录浏览全文

4963

注册一个新账户 忘记密码

参考文献

[1]

王健, 何叶荣, 王向前. 基于 WSR 方法论分析煤矿安全问题[J]. 六盘水师范学院学报, 2022, 34(3): 106-112.

[2]

Tian Xinyun , Gong Siyuan , Tang Chen , et al. Research on the construction of three—dimensional longitudinal wave velocity model Based on underground—surface joint microseismic monitoring[J]. Rock Mechanics and Rock Engineering, 2025, 58(9): 10105-10120.

[3]

Kang Yize , Yao Yingkang , Dong Runlong , et al. Improved complete ensemble empirical mode decomposition with adaptive noise and composite multiscale permutation entropy for denoising blast vibration signal[J]. Heliyon, 2024, 10(18): 1-22.

[4]

刘玉洁, 刘启蒙, 刘瑜, . 断层采动效应及防隔水煤(岩)柱合理留设研究[J]. 六盘水师范学院学报, 2025, 37(3): 18-28.

[5]

周红敏, 赵事成, 赵文清, . 基于改进的 MEEMD 的隧道掘进爆破振动信号去噪优化分析[J]. 振动与冲击, 2023, 42(10): 74-81.

[6]

闫鹏, 张云鹏, 田婕, . 基于 CEEMDAN—K—means 算法的爆破振动信号去噪研究[J]. 爆破, 2023, 40(3): 184-190.

[7]

付晓强, 杨仁树, 刘纪峰, . 冻结立井爆破近区井壁振动信号基线漂移校正和消噪方法[J]. 爆炸与冲击, 2020, 40(9): 100-112.

[8]

Bai Weijun , Chang Yingjie . Denoising of Blasting Vibration Signals Based on Ceemdan—Ica Algorithm[J]. Scientific Reports, 2023, 13: 20928.

[9]

Dragomiretskiy K , Zosso D . Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.

[10]

Liu Ze , Peng Yaxiong . Study on denoising method of vibration signal induced by tunnel portal blasting based on WOA—VMD algorithm[J]. Applied Sciences 2023, 13(5): 1-12.

[11]

莫宏毅, 徐振洋, 刘鑫, . 基于 SSA—VMD 的爆破振动信号趋势项去除方法[J]. 振动与冲击, 2023, 42(11): 304-312.

[12]

彭亚雄, 刘广进, 苏莹, . 基于自适应 VMD—MPE 算法的矿山爆破地震波信号降噪方法研究[J]. 振动与冲击, 2022, 41(13): 135-141.

[13]

付晓强, 俞缙, 戴良玉, . 隧道爆破振动信号时频谱交叉项干扰抑制方法[J]. 振动与冲击, 2021, 40(19): 59-65.

[14]

王海龙, 柏皓博, 赵岩, . 基于傅里叶分解 — 小波包分析的爆破信号去噪方法[J]. 爆破, 2021, 38(2): 37-44.

[15]

易文华, 刘连生, 闫雷, . 基于 EMD 改进算法的爆破振动信号去噪[J]. 爆炸与冲击, 2020, 40(9): 77-87.

[16]

邵东辉. 基于 CEEMD 低通的隧道爆破振动信号去噪[J]. 工程爆破, 2017, 23(6): 5-10.

[17]

赵一聪, 韩风雷, 肖东辉, . 基于 CEEMD—HHT 的公路隧道爆破洞外空气噪声时频能量分析[J]. 振动与冲击, 2025, 44(4): 198-206.

[18]

赵立财. 隧道水压爆破中不同轴向装药结构的地表振动响应规律研究[J]. 振动工程学报, 2025, 38(1): 172-179.

[19]

叶海旺, 张鹏辉, 蒙云琪, . 基于爆破振动与松动圈分析的水封洞库爆破方案比选[J]. 爆破, 2025, 42(1): 44-55.

[20]

付晓强, 戴良玉, 俞缙, . 气爆破岩振动信号优化分解与相关特征分析[J]. 中国安全科学学报, 2025, 35(5): 64-72.

基金资助

国家级大学生创新训练项目“新型化能瞬态气胀致裂破岩振动效应与控制技术研究”(202511311015)

福建省自然科学基金联合资助项目计划“新型破岩气体发生器振动效应与灾害评估研究”(2024J01905)

鞍钢矿业爆破有限公司企业委托项目“基于可视化的台阶微差控制爆破技术研究”(HX20250204)

AI Summary AI Mindmap
PDF (2463KB)

78

访问

0

被引

详细

导航
相关文章

AI思维导图

/