|本期目录/Table of Contents|

[1]熊璐伟,钟晓阳,李庶林,等.基于改进的HHT-SVM微震信号特征提取及分类识别研究*[J].中国安全生产科学技术,2023,19(10):13-20.[doi:10.11731/j.issn.1673-193x.2023.10.002]
　XIONG Luwei,ZHONG Xiaoyang,LI Shulin,et al.Research on feature extraction and classification and identification of microseismic signals based on improved HHT-SVM[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2023,19(10):13-20.[doi:10.11731/j.issn.1673-193x.2023.10.002]
点击复制

基于改进的HHT-SVM微震信号特征提取及分类识别研究*
分享到：

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
19
期数:
2023年10期
页码:
13-20
栏目:
学术论著
出版日期:
2023-10-31

文章信息/Info

Title:
Research on feature extraction and classification and identification of microseismic signals based on improved HHT-SVM
文章编号:
1673-193X(2023)-10-0013-08
作者:
熊璐伟; 钟晓阳; 李庶林; 叶龙珍; 方鑫; 郑宗槟
（1.厦门大学 建筑与土木工程学院，福建 厦门 361005；
2.江西铁山垅钨业有限公司，江西 赣州 341000；
3.自然资源部丘陵山地地质灾害防治重点实验室，福建 福州 350002）
Author(s):
XIONG Luwei;  ZHONG Xiaoyang;  LI Shulin;  YE Longzhen;  FANG Xin;  ZHENG Zongbin
(1.School of Architecture and Civil Engineering,Xiamen University,Xiamen Fujian 361005,China;
2.Jiangxi Tieshanlong Tungsten Industry Co.,Ganzhou Jiangxi 341000,China;
3.Key Laboratory of Geohazard Prevention of Hill Mountains of Ministry of Nature Resources,Fuzhou Fujian 350002, China)
关键词:
微震信号; 爆破信号; 支持向量机; 希尔伯特-黄变换; 信号识别
Keywords:
microseismic signal;  blasting signal;  support vector machine (SVM);  hilbert-huang transform (HHT);  signal identification
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2023.10.002
文献标志码:
A
摘要:
为了解决实际工程中微震信号与爆破信号在波形上具有较强相似性，难以识别的问题，提出基于改进HHT-SVM的微震信号识别方法。分析2种信号的瞬时能量变化规律，发现微震信号具有孕育速度慢、持续时间短及峰后衰减速度慢的特点，对其进行量化处理并提取特征，构建多维特征向量。对输入的特征向量经功率谱熵特征加权算法赋予权重，并使用GSWOA算法对SVM进行参数寻优，建立信号识别网络对微震信号、爆破信号进行识别。研究结果表明：方法的综合识别成功率达到96.250%，能够有效识别微震、爆破信号。研究结果可为后续利用微震信号进行灾害预警提供有力支撑。
Abstract:
To solve the problem that the microseismic signals and blasting signals in actual engineering have strong similarity in waveforms and are difficult to identify,an identification method of microseismic signals based on improved HHT-SVM was proposed.The instantaneous energy change laws of the two signals were analyzed,and it was found that the microseismic signals had the characteristics of slow gestation speed,short duration and slow decay speed after the peak,then the microseismic signals were quantized and the features were extracted to construct a multi-dimensional feature vector.The input feature vectors were given weights by the power spectrum entropy feature weighting algorithm,then the parameters of SVM were optimized by using GSWOA algorithm,and a signal identification network was established to identify the microseismic signals and blasting signals.The results show that the comprehensive identification success rate of this method reaches 96.250%,which can effectively identify the microseismic and blasting signals.The research results can provide strong support for the subsequent use of microseismic signals for disaster warning.

参考文献/References:

［1］姜鹏,戴峰,徐奴文,等.基于ST时频分析的地下厂房微震信号识别研究［J］.岩石力学与工程学报,2015,34（增刊2）:4071-4079. JIANG Peng,DAI Feng,XU Nuwen,et al.Identification of microseismic signal in underground powerhouse based on ST timefrequency analysis［J］.Chinese Journal of Rock Mechanics and Engineering,2015,34（Supplement 2）:4071-4079.
［2］李庶林,尹贤刚,郑文达,等.凡口铅锌矿多通道微震监测系统及其应用研究［J］.岩石力学与工程学报,2005（12）:2048-2053. LI Shulin,YIN Xian’gang,ZHENG Wenda,et al.Research of multi-channel microseismic monitoring system and its application to Fankou leadzinc mine［J］.Chinese Journal of Rock Mechanics and Engineering,2005（12）:2048-2053.
［3］张楚旋,李夕兵,董陇军,等.基于微震监测的岩体失稳智能预报［J］.中国安全生产科学技术,2016,12（3）:5-9. ZHANG Chuxuan,LI Xibing,DONG Longjun,et al.Intelligent prediction of rock mass instability based on microseismic monitoring ［J］.Journal of Safety Science and Technology,2016,12（3）:5-9.
［4］XIE Q M,LONG K,LI Z M,et al.Stability evaluation for steep bank slope with microseismic monitoring in Three Gorges Reservoir area［J］.Journal of Mountain Science,2022,19（6）:1588-1601.
［5］裴琳.微地震监测技术在地下工程中应用研究［J］.工程地球物理学报,2008（5）:554-559. PEI Lin.Application of microearthquake monitoring technology in subsurface engineering［J］.Chinese Journal of Engineering Geophysics,2008（5）:554-559.
［6］朱权洁,姜福兴,尹永明,等.基于小波分形特征与模式识别的矿山微震波形识别研究［J］.岩土工程学报,2012,34（11）:2036-2042. ZHU Quanjie,JIANG Fuxing,YIN Yongming,et al.Classification of mine microseismic events based on wavelet-fractal method and pattern recogniton［J］.Chinese Journal of Geotechnical Engineering,2012,34（11）:2036-2042.
［7］陆菜平,窦林名,吴兴荣,等.岩体微震监测的频谱分析与信号识别［J］.岩土工程学报,2005（7）:772-775. LU Caiping,DOU Linming,WU Xingrong,et al.Frequency spectrum analysis on microseismic monitoring and signal differentiation of rock material［J］.Chinese Journal of Geotechnical Engineering,2005（7）:772-775.
［8］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. HE Zhengxiang,PENG Ping’an,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.
［9］LI X L,LI Z H,WANG E Y,et al.Pattern recognition of mine microseismic and blasting events based on wave fractal features ［J］.Fractals,2018,26（3）：1-18.
［10］CHENG Y,XIAO B,PENG R,et al.Band weighting via maximizing interclass distance for hyperspectral image classification［J］.IEEE Geosci.Remote Sensing Lett,2016,13（7）：922-925. ［11］SEYEDALI M,ANDDREW L.The whale optimization algorithm［J］.Advances in Engineering Software,2016,95：51-67.
［12］刘磊,白克强,但志宏,等.一种全局搜索策略的鲸鱼优化算法［J］.小型微型计算机系统,2020,41（9）:1820-1825. LIU Lei,BAI Keqiang,DAN Zhihong,et al.Whale optimization algorithm with global search strategy［J］.Journal of Chinese Computer Systems,2020,41（9）:1820-1825.
［13］MARCELO A，COLOMINAS,GASTON S,et al.Improved complete ensemble EMD:a suitable tool for biomedical signal processing［J］.Biomedical Signal Processing and Control,2014,14：19-29.
［14］凌同华，李夕兵.多段微差爆破振动信号频带能量分布特征的小波包分析［J］.岩石力学与工程学报，2005，24（7）：1117-1122. LING Tonghua,LI Xibing.Analysis of energy distributions of millisecond blast vibration signals using the wavelet packet method［J］.Chinese Journal of Rock Mechanics and Engineering,2005，24（7）：1117-1122.
［15］张法全,王海飞,王国富,等.基于RST-NMF模型的微震信号时频分析和识别［J］.振动与冲击,2019,38（17）:1-7. ZHANG Faquan,WANG Haifei,WANG Guofu,et al.Time-frequency analysis and identification for micro-seismic signals based on RST-NMF model［J］.Journal of Vibration and Shock,2019,38（17）:1-7.

相似文献/References:

[1]商 鹏,雷文杰,冷军发.基于HHT的煤岩破裂微震信号分析[J].中国安全生产科学技术,2014,10(2):33.[doi:10.11731/j.issn.1673-193x.2014.02.006]
　SHANG Peng,LEI Wen jie,LENG Jun fa.Analysis on microseismic signals of coal and rock fracture based on HHT[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(10):33.[doi:10.11731/j.issn.1673-193x.2014.02.006]
[2]吕垒,张教福,朱权洁,等.矿山微震时间序列的相空间构建与混沌吸引子维数确立[J].中国安全生产科学技术,2016,12(2):27.[doi:10.11731/j.issn.1673-193x.2016.02.005]
　LYU Lei,ZHANG Jiaofu,ZHU Quanjie,et al.Construction of phase space and determination of chaotic attractor dimension for time series of mine microseismic[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2016,12(10):27.[doi:10.11731/j.issn.1673-193x.2016.02.005]
[3]吕垒,张教福,朱权洁,等.矿山微震时间序列的相空间构建与混沌吸引子维数确立[J].中国安全生产科学技术,2016,12(2):27.[doi:10.11731/j.issn.1673-193X.2016.02.005]
　LYU Lei,ZHANG Jiaofu,ZHU Quanjie,et al.Construction of phase space and determination of chaotic attractor dimension for time series of mine microseismic[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2016,12(10):27.[doi:10.11731/j.issn.1673-193X.2016.02.005]
[4]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.[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(10):41.[doi:10.11731/j.issn.1673-193x.2018.12.006]

备注/Memo

备注/Memo:
收稿日期： 2023-06-08
* 基金项目： 自然资源部丘陵山地地质灾害防治重点实验室（福建省地质灾害重点实验室）开放基金资助项目（FJKLGH2020K005）；自然资源部丘陵山地地质灾害防治重点实验室（福建省地质灾害重点实验室）自主课题资助项目（KLGHZ202104,KY-07000-04-2022-020）
作者简介： 熊璐伟，硕士研究生，主要研究方向为矿山微震监测预警。
通信作者： 李庶林，博士，教授，主要研究方向为微震与声发射技术。
更新日期/Last Update: 2023-11-06