|本期目录/Table of Contents|

[1]何正祥,彭平安,廖智勤.基于梅尔倒谱系数的矿山复杂微震信号自动识别分类方法[J].中国安全生产科学技术,2018,14(12):41-47.[doi:10.11731/j.issn.1673-193x.2018.12.006]
 HE Zhengxiang,PENG Pingan,LIAO Zhiqin.An automatic identification and classification method of complex microseismic signals in mines based on Mel-frequency cepstral coefficients[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2018,14(12):41-47.[doi:10.11731/j.issn.1673-193x.2018.12.006]
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基于梅尔倒谱系数的矿山复杂微震信号自动识别分类方法
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
14
期数:
2018年12期
页码:
41-47
栏目:
学术论著
出版日期:
2018-12-31

文章信息/Info

Title:
An automatic identification and classification method of complex microseismic signals in mines based on Mel-frequency cepstral coefficients
文章编号:
1673-193X(2018)-12-0041-07
作者:
何正祥彭平安廖智勤
(中南大学 资源与安全工程学院,湖南 长沙 410083)
Author(s):
HE Zhengxiang PENG Ping’an LIAO Zhiqin
(School of Resources and Safety Engineering, Central South University, Changsha Hunan 410083, China)
关键词:
微震信号梅尔倒谱系数混合高斯隐马尔科夫识别分类
Keywords:
microseismic signal Mel-frequency cepstral coefficients gaussian mixture hidden markov model identification and classification
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2018.12.006
文献标志码:
A
摘要:
为了实现矿山复杂微震信号的自动高效识别与分类,保证后续微震分析的时效性和准确性,运用梅尔倒谱系数法,将原始的4种微震信号(岩体破裂、爆破振动、电磁干扰和钻机凿岩)转化为梅尔标度上的非线性频谱,再转换到倒谱域上,结合其在时域上的差分得到1组24维的特征参数向量,利用这些特征参数向量训练构建各类事件对应的混合高斯隐马尔可夫识别模型,进而实现对微震信号的自动识别分类。研究结果表明:运用基于梅尔倒谱系数的微震信号识别分类方法对矿山实际微震数据进行测试,微震事件的识别分类准确率达到92.46%,具有较高的准确性,为实现微震监测系统的实时性分析提供了技术支持。
Abstract:
In order to realize the automatic and efficient identification and classification of complex microseismic signals in mines and ensure the timeliness and accuracy of subsequent microseismic analysis, the Melfrequency cepstral coefficients method was applied to convert four types of original microseismic signals (rock burst, blasting vibration, electromagnetic interference and rig drilling) into the nonlinear frequency spectrum on the MEL scale and then switch to the cepstrum domain, and a set of characteristic parameter vector with 24 dimensions was obtained combining with the difference in the time domain. The Gaussian mixture hidden Markov identification model corresponding to various events was constructed and trained by using these characteristics parameter vector, thus the automatic identification and classification of microseismic signals was realized. The experimental results showed that when testing the actual microseismic data of the mine by using the identification and classification method of microseismic signals based on the Melfrequency cepstral coefficients, the accuracy of identification and classification of microseismic events reached 92.46%, with a relatively high accuracy. It provides the technical support for realizing the realtime analysis of microseismic monitoring system.

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备注/Memo

备注/Memo:
收稿日期: 2018-11-21
基金项目: 国家重点研发计划项目(2017YFC0602905)
作者简介: 何正祥,硕士研究生,主要研究方向为矿山微震监测技术。
更新日期/Last Update: 2019-01-03