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现代矿业 ›› 2026, Vol. 42 ›› Issue (08): 1-8,15.

• 矿山控制爆破技术专栏 •    下一篇

爆破振动信号分析方法的演进与智能化展望

杨棚涛1 王 倩2 衡少华1 孙 泽3,4 张永利3,4 王 燕3,4   

  1. 1. 中铁北京工程局集团第一工程有限公司;2. 西安建筑科技大学信息与控制工程学院; 3. 西安建筑科技大学土木工程学院;4. 陕西省岩土与地下空间工程重点实验室
  • 出版日期:2026-08-25 发布日期:2026-08-20

Evolution and Outlook for Intelligence of Blasting Vibration Signal Analysis Methods

YANG Pengtao1 WANG Qian2 HENG Shaohua1 SUN Ze3,4 ZHANG Yongli3,4 WANG Yan3,4   

  1. 1. First Engineering Group,China Railway Beijing Engineering Bureau Group Co.,Ltd.; 2. College of In⁃ formation and Control Engineering,Xi'an University of Architecture and Technology; 3. College of Civil Engineering,Xi'an University of Architecture and Technology; 4. Shaanxi Provincial Key Laboratory of Geotechnical and Underground Space Engineering
  • Online:2026-08-25 Published:2026-08-20

摘要: 爆破振动信号分析对提升爆破作业精度与安全性至关重要,其核心矛盾在于信号的 非平稳特性与传统平稳信号分析方法的局限性。通过对该领域分析方法发展脉络的系统梳理发 现,早期基于平稳假设的傅里叶变换及短时傅里叶变换,因时频分辨率固定而应用受限。小波变 换等方法实现了对非平稳信号特征的有效捕捉;Hilbert-Huang 变换则凭借其自适应时频解析机 制,在处理复杂瞬变信号时展现出独特优势,但需结合 EEMD、EWT 等改进算法,以规避端点效应 与模态混叠。智能化信息处理与多源数据融合,是突破现有分析瓶颈、实现振动效应精准评估与 实时控制的关键。未来,基于深度学习的信号解析、多模态数据协同与动态反馈系统,将共同推动 爆破振动分析向智能化、精准化与实时化方向发展。

关键词: 爆破振动信号, 分析方法, 非平稳信号, 智能化信息处理

Abstract: The analysis of blasting vibration signals is of vital importance for enhancing the accuracy and safety of blasting operations. The core issue lies in the non-stationary nature of the signals and the limi⁃ tations of traditional stationary signal analysis methods. Through a systematic review of the development process of analysis methods in this field,it is found that the early Fourier transform and short-time Fourier transform based on the assumption of stationarity were limited by their fixed time-frequency resolution. Methods such as wavelet transform effectively capture the characteristics of non-stationary signals; the Hil⁃ bert-Huang transform,with its adaptive time-frequency analysis mechanism,demonstrates unique advan⁃ tages in handling complex transient signals,but requires the combination of improved algorithms such as EEMD and EWT to avoid end-point effects and mode aliasing. Intelligent information processing and multi-source data fusion are the key to breaking through the existing analysis bottlenecks and achieving precise assessment and real-time control of vibration effects. In the future,signal analysis based on deep learning,multi-modal data collaboration,and dynamic feedback systems will jointly drive the develop⁃ ment of blasting vibration analysis towards intelligence,precision,and real-time.

Key words: blasting vibration signals, analysis methods, non-stationary signals, intelligent informa? tion processing