基于ST时频分析的地下厂房微震信号识别研究

doi:10.13722/j.cnki.jrme.2015.0702

摘要
图/表
参考文献(32)
相关文章 (15)

全文: PDF (1535 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要利用S变换(S Transform，ST)分析地下厂房岩石破裂、爆破振动信号的频率特征，通过频带能量分布比例对其进行量化研究，并作为信号特征，建立基于遗传算法优化的BP神经网络模型，实现信号的准确分类。在白鹤滩水电站左岸地下厂房微震监测资料的基础上，通过MATLAB编制相应程序，对信号进行ST时频分解，并求解各子频段的能量分布比例。通过信号的频带能量分布特征，结合遗传算法优化的BP神经网络对400组地下厂房2类拾取信号进行训练，建立分类识别模型，并对100组测试信号进行分类预测。研究结果表明：岩石破裂与爆破振动信号表现出明显的频带能量分布差异；建立的基于遗传算法优化的BP神经网络模型能对2类信号进行有效识别，识别精度达98%，研究方法可为类似工程的微震监测波形识别模型的建立提供参考。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	姜鹏1
	戴峰1
	徐奴文1
	樊义林2
	李彪1
	郭亮1
	徐剑2

关键词 ：地下工程, 地下厂房, 微震监测, 波形识别, S变换, 频率特征, BP神经网络, 遗传算法

Abstract：The classification of microseismic events is one of the key technologies in microseismic monitoring. The frequency characteristics of rock fracture，blasting vibration signal in underground powerhouse are studied through S transform(ST)，the energy distribution ratio in different frequency bands were used to quantify the frequency characteristics and a model for classification of microseismic events based on the BP neural network optimized by genetic algorithm(GA) was established. Based on the monitoring data in the left bank underground powerhouse of Baihetan hydropower station，the microseismic signals were decomposed in time-frequency domain to calculate the energy distribution ratio in different frequency bands using MATLAB software. Then the feature of microseismic signal can be established，combined with the GA-BP method，the classification model was built by training 400 sets of sample data. Finally，100 sets of test data were used to verify the accuracy of GA-BP network model. The results show that the rock fracture and blasting vibration events show difference in energy distribution， the GA-BP network model presented excellent performance for identifying the two types of signals，the correct rate of identification is 98%，the research method can provide reference for the establishment of events classification model in microseismic monitoring of similar projects.

Key words： underground engineering underground powerhouse microseismic monitoring waveform identification S transform(ST) frequency characteristics BP neural network genetic algorithm(GA)

引用本文:

姜鹏1，戴峰1，徐奴文1，樊义林2，李彪1，郭亮1，徐剑2. 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(S2): 4071-4079.
JIANG Peng1，DAI Feng1，XU Nuwen1，FAN Yilin2，LI Biao1，GUO Liang1，XU Jian2. IDENTIFICATION OF MICROSEISMIC SIGNAL IN UNDERGROUND POWERHOUSE BASED ON ST TIME-FREQUENCY ANALYSIS. , 2015, 34(S2): 4071-4079.

链接本文:

https://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme.2015.0702 或 https://rockmech.whrsm.ac.cn/CN/Y2015/V34/IS2/4071

[1]	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，2005，24(12)：2 048-2 053. (LI Shulin，YIN Xiangang，ZHENG Wenda，et al.Researchofmulti-channelmicroseismicmonitoring system andits application to fankou lead-zing mine[J]. Chinese Journal of Rock Mechanics and Engineering，2005，24(12)：2 048-2 053.(in Chinese))
[2]	唐礼忠，潘长良，杨承祥，等. 冬瓜山铜矿微震监测系统及其应用研究[J]. 金属矿山，2006，(10)：41-44.(TANG Lizhong，PANChangliang，YANGChengxiang，et al.Establishment and application of microseismicity monitoring system in Dongguashancopper mine[J]. Metal Mine，2006，(10)：41-44.(in Chinese))
[3]	赵周能，冯夏庭，陈炳瑞，等.深埋隧洞微震活动区与岩爆的相关性研究[J]. 岩土力学，2013，34(2)：491-497.(ZHAO Zhouneng，FENG Xiating，CHEN Bingrui，et al.Study of relativity between rockburst and microseismicactivity zone in deep tunnel[J].Rock and Soil Mechanics，2013，34(2)：491-497.(in Chinese))
[4]	陈炳瑞，冯夏庭，曾雄辉，等. 深埋隧洞TBM掘进微震实时监测与特征分析[J]. 岩石力学与工程学报，2011，30(2)：275-283.(CHEN Bingrui，FENG Xiating，ZENG Xionghui，et al. Real-time microseismic monitoring and its characteristic analysis during TBM tunneling in deep-buried tunnel[J]. Chinese Journal of Rock Mechanics and Engineering，2011，30(2)：275-283.(in Chinese))
[5]	李彪，戴峰，徐奴文，等. 深埋地下厂房微震监测系统及其工程应用[J]. 岩石力学与工程学报，2014，33(增1)：3375-3383.(LI Biao，DAI Feng，XU Nuwen，et al. Microseismic monitoring system and its engineeringapplications of deep-buried underground powerhouse[J]. Chinese Journal of Rock Mechanics and Engineering，2014，33(Supp.1)：3375-3383.(in Chinese))
[6]	张伯虎，邓建辉，高明忠，等. 基于微震监测的水电站地下厂房安全性评价研究[J]. 岩石力学与工程学报，2012，31(5)：937-944.(ZHANG Bohu，DENG Jianhui，GAO Mingzhong，et al.Safety evaluation research based on microseismic monitoring in underground powerhouse of hydropower station[J]. Chinese Journal of Rock Mechanics and Engineering，2012，31(5)：937-944.(in Chinese))
[7]	XU N W，LI T B，DAI F，et al. Microseismic monitoring and stability evaluation for the large scale underground caverns atthe Houziyan hydropower stationin Southwest China[J]. Engineering Geology，2015，188(1)：48-67.
[8]	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.(in Chinese))
[9]	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]. 采矿与安全工程报，2007，24(2)：146-149.(CAO Anye，DOU Linming，QIN Yuhong，et al.Characteristic of micro seismic monitoring signalin high stressedzone[J].Journal of Mining andSafety Engineering，2007，24(2)：146-149.(in Chinese))
[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 FSWTtime-frequencyanalysis[J]. Chinese Journal of Geotechnical Engineering，2015，37(2)：306-312.(in Chinese))
[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 Quanjie，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.(in Chinese))
[12]	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-fractalmethod and pattern recognition[J]. Chinese Journal of Geotechnical Engineering，2012，34(11)：2036-2042.(in Chinese))
[13]	姜福兴，尹永明，朱权洁，等.单事件多通道微震波形的特征提取与联合识别研究[J]. 煤炭学报，2014，39(2)：229-237.(JIANG Fuxing，YIN Yongming，ZHU Quanjie，et al. Feature extraction and classification of mining microseismic waveforms via multi-channels analysis[J]. Journal of China Coal Society，2014，39(2)：229-237.(in Chinese))
[14]	TAN J F，STEWART R R，WONG J. Classification of microseismic events via principal component analysis of trace statistics[J]. CSEG Recorder，2010，(1)：34-38.
[15]	VALLEJOSJA，MCKINNONSD. Logistic regression and neural network classification of seismic records[J]. International Journal of Rock Mechanics andMining Sciences，2013，62(1)：86-95.
[16]	何满潮，赵菲，张昱，等.瞬时应变型岩爆模拟试验中花岗岩主频特征演化规律分析[J]. 岩土力学，2015，36(1)：1-8.(HE Manchao，ZHAO Fei，ZHANG Yu，et al. Feature evolution of dominant frequency components in acoustic emissions of instantaneous strain-type granitic rockburst simulation tests[J]. Rock and Soil Mechanics，2015，36(1)：1-8.(in Chinese))
[17]	肖亚勋，冯夏庭，陈炳瑞，等. 深埋隧洞即时型岩爆孕育过程的频谱演化特征[J]. 岩土力学，2015，36(4)：1 127-1 134.(XIAO Ya-xun，FENG Xiating，CHEN Bingrui，et al. Evolution of frequency spectrum during instantrockbursts in deep inoculation tunnel[J].Rock and Soil Mechanics，2015，36(4)：1 127-1 134.(in Chinese))
[18]	STOCKWELL R G，MANSINHA L，LOWE R P，et al. Localization of the complex spectrum：the S transform[J]. IEEE Transactions onSignal Processing，1996，44(4)：998-1001.
[19]	李刚. 基于ST的地震信号相干噪声压制研究[硕士学位论文][D]. 成都：西南交通大学，2011.(LI Gang. The research on coherent noise suppressionof seismic signal based on S transform[M. S. Thesis][D]. Chengdu：SouthwestJiaotongUniversity，2011.(in Chinese))
[20]	武国宁，曹思远，马宁，等. S变换的时频分析特性及其改进[J]. 地球物理学进展，2011，26(5)：1 661-1 667.(WU Guoning，CAO Siyuan，MA Ning，et al. The time frequency property of S transform and its generalization[J]. Progress in Geophys，2011，26(5)：1 661- 1667.(in Chinese))
[21]	中国水电顾问集团华东勘测设计研究院. 金沙江白鹤滩水电站左岸引水发电系统土建及金属结构安装工程施工招标文件参考资料：工程地质[R]. 杭州：中国水电顾问集团华东勘测设计研究院，2013.(Hydro China Engineering Consulting Group Co..Bidding documents reference of civil engineering and installation of metal structuresof the left bank water diversion and power generation system in Baihetan hydropower station[R]. Hangzhou：Hydro China Engineering Consulting Group Co.，2013.(in Chinese))
[22]	李孟源，尚振东，蔡海潮，等. 声发射监测及信号处理[M]. 北京：科学出版社，2010：90-100.(LI Mengyuan，SHANG Zhendong，CAI Haichao，et al. Acoustic emission monitoring and signal processing[M].Beijing：Science Press，2010：90-100.(in Chinese))
[23]	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)：1 117-1 122.(LING Tonghua，LI Xibing. Analysis of energy distribution of millisecond blast vibration signal using the wavelet packet method[J]. Chinese Journal of Rock Mechanics and Engineering，2005，24(7)：1 117-1 122. (in Chinese))
[24]	HE M C，MIAO J L，FENG J L. Rock burst process of limestone and its acoustic emission characteristics under true-triaxial unloading conditions[J]. International Journal of Rock Mechanics and Mining Sciences，2010，47(2)：286-298.
[25]	HE M C，SOUSA L R E，MIRANDA T，et al. Rockburst laboratory tests database—application of datamining techniques[J]. Engineering Geology，2015，185(1)：116-130.
[26]	张明，卢裕杰，毕忠伟，等. 利用神经网络的反馈分析方法及其在地下厂房中的应用[J]. 岩石力学与工程学报，2010，29(11)：2211-2220.(ZHANG Ming，LU Yujie，BI Zhongwei，et al. Feedback analysis method using artificial neural network and its application to underground powerhouse[J]. Chinese Journal of Rock Mechanics and Engineering，2010，29(11)：2 211-2 220.(in Chinese))
[27]	冯夏庭，周辉，李邵军，等. 岩石力学与工程综合集成智能反馈分析方法及应用[J].岩石力学与工程学报，2007，26(9)：1737-1 744. (FENG Xiating，ZHOU Hui，LI Shaojun，et al. Integrated intelligent feedback analysis of rock mechanics and engineering problems and its applications[J]. Chinese Journal of Rock Mechanics and Engineering 2007，26(9)：1 737-1 744.(in Chinese))
[28]	刘刚. 基于BP 神经网络的隧道围岩稳定性分类的研究与工程应用[硕士学位论文][D]. 合肥：合肥工业大学，2007.(LIU Gang. ThesafetyclassificationofsurroundingrocksintunnelsanditsengineeringapplicationbasedonBPneuralnetwork[M. S. Thesis][D]. Hefei：Hefei University of Technology，2007.(in Chinese))
[29]	王小川，史峰，郁磊，等. MATLAB神经网络43个案例分析[M]. 北京：北京航空航天大学出版社，2013：1-33.(WANG Xiaochuan，SHI Feng，YU Lei，et al. 43 cases based on MATLAB neural network[M]. Beijing：Beihang University Press，2013：1-33.(in Chinese))
[30]	王雪青，喻刚，孟海涛. 基于GA改进BP神经网络的建设工程投标报价研究[J]. 土木工程学报，2007，40(7)：93-98.(WANG Xueqing，YU Gang，MENG Haitao. Study of construction project bidding based on the BP neural network improved by GA[J]. China Civil Engineering Journal，2007，40(7)：93-98.(in Chinese))
[31]	刘冰，郭海霞. MATLAB神经网络超级学习手册[M]. 北京：人民邮电出版社，2014：159-168.(LIUBing，GUO Haixia. MATLAB neural network learning manual[M]. Beijing：Posts and TelecomPress，2014：159-168.(in Chinese))
[32]	戚德虎，康继昌. BP 神经网络的设计[J]. 计算机工程与设计，1998，19(2)：48-50.(QIDehu，KANGJichang. On Design of the BP neural network[J]. Computer Engineering and Design，1998，19(2)：48-50.(in Chinese))