[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]
点击复制
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
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]
《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
- Keywords:
- microseismic signal; Mel-frequency cepstral coefficients; gaussian mixture; hidden markov model; identification and classification
- 分类号:
- X936
- 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 Melfrequency 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 Melfrequency 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 realtime analysis of microseismic monitoring system.
参考文献/References:
［1］ALBER M, FRITSCHEN R, BISCHOFF M, et al. Rock mechanical investigations of seismic events in a deep longwall coal mine ［J］. International Journal of Rock Mechanics & Mining Sciences, 2009, 46(2): 408-420.
［2］GE M. Efficient mine microseismic monitoring ［J］. International Journal of Coal Geology, 2005, 64(1): 44-56.
［3］朱梦博, 王李管, 彭平安, 等. 微震P波到时拾取的PAI-k-MFV算法改进及应用 ［J］. 煤炭学报, 2017, 42(10): 2698-2705.
ZHU Mengbo, WANG Liguan, PENG Pingan,et al. Modified PAI-k-MFV Picker of Picking Microseismic P-wave Arrival Time and Its Application ［J］. Journal of China Coal Society, 2017, 42(10): 2698-2705.
［4］VALLEJOS J A, MCKINNON S D. Logistic regression and neural network classification of seismic records ［J］. International Journal of Rock Mechanics & Mining Sciences, 2013, 62(9): 86-95.
［5］董陇军, 孙道元, 李夕兵, 等. 微震与爆破事件统计识别方法及工程应用 ［J］. 岩石力学与工程学报, 2016, 35(7): 1423-1433.
DONG Longjun, SUN Daoyuan, LI Xibing, et al. A statistical method to identify blasts and microseismic events and its engineering application ［J］. Chinese Journal of Rock Mechanics and Engineering, 2016, 35(7): 1423-1433.
［6］曹安业, 窦林名, 秦玉红, 等. 高应力区微震监测信号特征分析 ［J］. 采矿与安全工程学报, 2007, 24(2): 146-149.
CAO Anye, DOU Linming, QIN Yuhong, et al. Characteristic of microseismic m onito ring signal in high stressed zone ［J］. Journal of Mining and Safety Engineering, 2007, 24(2): 146-149.
［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, 27(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, 27(7): 772-775.
［8］郭涛, 方向, 谢全民, 等. 频率切片小波变换在爆破振动信号时频特征精确提取中应用 ［J］. 振动与冲击, 2013, 32(22): 73-78.
GUO Tao, FANG Xiang, XIE Quanmin, et al. Application of FSWT in accurate extraction of time-frequency features for blasting vibration signals ［J］. Journal of Vibration and Shock, 2013, 32(22): 73-78.
［9］唐礼忠, 陈资南, 张君, 等. 矿山微震信号小波分析与研究 ［J］. 科技导报, 2013, 31(32): 29-33.
TANG Lizhong, CHEN Zinan, ZHANG Jun, et al. Research and analysis on wavelet of mine microseismic signals ［J］. Science & Technology Review, 2013, 31(32): 29-33.
［10］赵国彦, 邓青林, 马举. 基于FSWT时频分析的矿山微震信号分析与识别 ［J］. 岩土工程学报, 2015, 37(2): 306-312.
ZHAO Guoyan, DENG Qinglin, MA Ju. Recognition of mine microseismic signals based on FSWT time-frequency analysis ［J］. Chinese Journal of Geotechnical Engineering, 2015, 37(2): 306-312.
［11］朱权洁, 姜福兴, 于正兴, 等. 爆破震动与岩石破裂微震信号能量分布特征研究 ［J］. 岩石力学与工程学报, 2012, 31(4): 723-730.
ZHU Qaunjie, JIANG Fuxing, YU Zhengxing, et al. Study on energy distribution characters about blasting vibration and rock fracture microseismic signal ［J］. Chinese Journal of Rock Mechanics and Engineering, 2012, 31(4): 723-730.
［12］IEEE S B D M. Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences ［J］. Readings in Speech Recognition, 1990, 28(4): 65-74.
［13］ZULFIQAR A, MUHAMMAD A, MARTINEZ E A M. A speaker identification system using MFCC features with VQ technique［C］// The Workshop on Intelligent Information Technology Applications, 2009: 115-118.
［14］JIANFENG W U, QIN H B, HUA Y Z, et al. Pitch estimation and voicing classification using reconstructed spectrum from MFCC ［J］. Leice Transactions on Information & Systems, 2018, 101(2): 556-559.
［15］MILNER B, DARCH J. Robust acoustic speech feature prediction from noisy mel-frequency cepstral coefficients ［J］. IEEE Transactions on Audio Speech & Language Processing, 2010, 19(2): 338-347.
［16］GOODARZI M M, ALMASGANJ F. A GMM/HMM model for reconstruction of missing speech spectral components for continuous speech recognition ［J］. International Journal of Speech Technology, 2016, 1-9.
［17］唐礼忠, 潘长良, 谢学斌, 等. 冬瓜山铜矿深井开采岩爆危险区分析与预测 ［J］. 中南工业大学学报（自然科学版）, 2002, 33(4): 335-338.
TANG Lizhong, PAN Changliang, XIE Xuebin, et al. Analysis and prediction of rock burst dangerous areas in Dongguashan Copper Mine under deep well mining ［J］. Journal of Central South University(Science and Technology), 2002, 33(4): 335-338.
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 microseismic signals of coal and rock fracture based on HHT[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(12):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(12):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(12):27.[doi:10.11731/j.issn.1673-193X.2016.02.005]
[4]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.[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(12):13.[doi:10.11731/j.issn.1673-193x.2023.10.002]
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。
