地震预警中基于HHT的P波震相识别方法研究
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- 英文论文题名：A P wave identification algorithm based on HHT in Earthquake Early Warning
- 论文作者：唐雅蕾 ; 李鸿杰 ; 陈逸民 ; 张建经
- 英文论文作者：Tang Yalei ; Li Hongjie ; Chen Yimin ; Zhang Jianjing ; Southwest Jiaotong University
- 年：2016
- 作者机构：西南交通大学;
- 论文关键词：震相自动识别 ; 经验模态分解 ; Hilbert-Huang变换
- 英文论文关键词：seismic wave identification ; empirical mode decomposition ; Hilbert-Huang transform
- 会议召开时间：2016-10-27
- 会议录名称：第九届全国防震减灾工程学术研讨会论文集
- 语种：中文
- 分类号：P315.7
- 学会代码：OGTY
- 会议名称：第九届全国防震减灾工程学术研讨会
- 会议地点：中国安徽合肥
- 主办单位：中国土木工程学会、中国工程院土木、水利与建筑工程学部、中国土木工程学会防震减灾工程技术推广委员会、合肥工业大学、安徽省住房和城乡建设厅
- 学会名称：中国土木工程学会
- 页数：11
- 文件大小：519k
- 原文格式：O
- 会议级别：全国
摘要
针对地震信号非线性,非平稳性等特征,引入HHT方法对地震信号进行预处理,通过经验模态分解得到IMF分量,采用过零尺度阈值算法实现IMF分量的自适应选择,重构阈值范围内的IMF分量达到降噪效果。对有效信号的IMF分量作Hilbert变换得到瞬时频率和瞬时幅值,基于时频分析构建识别P波STA/LTA法的新特征函数。为提高震相识别精度,对STA/LTA法长短时窗内的幅值信号求标准差进一步放大地震到达后的特征函数比值,通过联合AIC准则,开展了P波震相自动识别。为了验证所建议P波震相自动识别算法,以人工捡拾为基准,应用KIK—NET数据,评价了本文提出方法的精度。结果表明,在处理信噪比较高的地震记录时,三种P博震相识别方法的捡拾精度基本一致;对于信噪比较低的地震信号,与传统STA/LTA—AIC算法相比,基于HHT的P波震相自动识别加权算法有更高的识别精度,同时具有较强抗噪性。
In view of nonlinear and non—stationary seismogram,HHT was introduced to preprocess the seismogram.A series of IMFs are obtained by empirical mode decomposition and are adaptively selected by zero — crossing scale thresholding adaptive denoising algorithm.The IMFs within the threshold range are reconstructed to reduce noise.Calculate the instantaneous amplitude and instantaneous frequency in a new STA/LTA characteristics function.The new STA/LTA method was applied jointly with AIC method in the identification of P wave.At the same time,a weighting factor was used to improve the STA/LTA algorithms.Considering the manual picking results as a benchmark,apply the seismic data from Japanese KIK—NET,verify the proposed algorithm in this paper.Results show that,compared with the traditional method,the P wave identification algorithm based on HHT has higher picking accuracy and better noise resistance.
In view of nonlinear and non—stationary seismogram,HHT was introduced to preprocess the seismogram.A series of IMFs are obtained by empirical mode decomposition and are adaptively selected by zero — crossing scale thresholding adaptive denoising algorithm.The IMFs within the threshold range are reconstructed to reduce noise.Calculate the instantaneous amplitude and instantaneous frequency in a new STA/LTA characteristics function.The new STA/LTA method was applied jointly with AIC method in the identification of P wave.At the same time,a weighting factor was used to improve the STA/LTA algorithms.Considering the manual picking results as a benchmark,apply the seismic data from Japanese KIK—NET,verify the proposed algorithm in this paper.Results show that,compared with the traditional method,the P wave identification algorithm based on HHT has higher picking accuracy and better noise resistance.
引文
[1]Rex Allen.Automatic phase pickers:their present use and future prospects[J].Bull.Seism.Soc.Am,1982,72(6):S225-S242.
[2]Paul S.Earle.Peter M.shear.Characterization of global seismograms using an automatic-picking algorithm[J].Bull.Seism.Soc.Am,1994,84(2):S366-S376.
[3]Rex Allen.Automatic earthquake recognition and timing from single trace C J].Bull.Seism.Soc.Am,1978,68(5):1521-1532.
[4]高淑芳,李山有,武东坡,等.一种改进的STA/LTA震相自动识别方法[J].世界地震工程,2008,24(2):37-41.(Gao Shufang,Li Shanyou,Wu Dongpo etc.,An automatic identifcation method of seismic phase based on modified STA/LTA algorithm[J].World Earthquake Engineering,2008,24(2):37-41).
[5]Tom Goforth,Eugene Herrin.An automatic seismic signal detection algorithm based on the walsh transform[J].Bull.Seism.Soc.Am,1981,71(4):1351-1360.
[6]刘希强,周慧兰,曹文海.高斯线调频小波变换及其在地震震相识别中的应用[J].地震学报,2002,24(6):607-616.
[7]Akaike,H.Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average processes[J].Ann.Inst.Stat.Math,1974,26,363-387.
[8]武安绪,吴培稚,兰从欣.Hilbert-Huang变换与地震信号的时频分析[J1.中国地震,2005,21(2):207-215.
[9]Kanwaldip S,Farid U.Dowla.Wavelet transform methods for phase identification in threecomponent seismograms[J],Bull.Seism.Soc.Am,1997,87:1598-1612.
[10]刘希强,周慧兰,等.基于小波变换的地震数据时频分析方法[J].西北地震学报,2000,22(2):143-146.
[11]Yomogid,K.Detection of anomalous seismic phases by the wavelet transform[J].Geophys.J.Int,1994,116:119-130.
[12]Chakraborty and D.Okaya.Frequency time decomposition of seismic data using wavelet based methods[J].Geophysics,1995,60:1906-1916.
[13]刘希强.基于小波包变换的弱震相识别方法[[J].地震学报,1998a,20(4):373-380.
[14]刘希强,郑治真.基于小波变换的信号突变检测、滤波和瞬态谱研究[J1.国际地震动态,1998b,1(4):373-380.
[15]Wang J.,Teng T.L.Artificial neural network-based seismic detector[J].Bull.Seism.Soc.Am,1995,85(1):308-319.
[16]张范民,李清河.利用人工神经网络理论对地震信号及地震震相进行识别[J].西北地震学报,1998,20(4):43-49.
[17]王娟,刘俊民,范万春.神经网络在震相识别中的应用[J].现代电子技术,2004,8(4):35-37.(Wang Juan,Liu Junmin,Fan Wanchun.Application of Neural Network in the Discrimination of Seismical Signal[J].Modern Electronic Technique,2004,8(4):35-37).
[18]Cheng Tong,B.L.N.Kennett.Auto seismic event recognition and later phase identification for broadband seismograms[J].Bull.Seism.Soc.Am,1996 86(6):1896-1909.
[19]Huang N E,Shen Z,Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings of the Royal Society of London.Series A:Mathematical,Physical and Engineering Sciences,1998,454(1971):903-995.
[20]Huang N E,Shen Z,Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings of the Royal Society of London.Series A:Mathematical,Physical and Engineering Sciences,1998,454(1971):903-995.
[21]Kopsinis Y,McLaughlin S.Development of EMD-based denoising methods inspiredby wavelet thresholding[J].IEEE Transactions on Signal Processing,2009,57(4):1351-1362.
[22]Vivo-Truyols G,Schoenmakers P J.Automatic selection of optimal Savitzky-Golay smoothing[J].Analytical chemistry,2006,78(13):4598-4608.
[23]Schafer R W.What is a Savitzky-Golay filter?[J].IEEE Signal Processing Magazine,2011,28(4):111-117.
[2]Paul S.Earle.Peter M.shear.Characterization of global seismograms using an automatic-picking algorithm[J].Bull.Seism.Soc.Am,1994,84(2):S366-S376.
[3]Rex Allen.Automatic earthquake recognition and timing from single trace C J].Bull.Seism.Soc.Am,1978,68(5):1521-1532.
[4]高淑芳,李山有,武东坡,等.一种改进的STA/LTA震相自动识别方法[J].世界地震工程,2008,24(2):37-41.(Gao Shufang,Li Shanyou,Wu Dongpo etc.,An automatic identifcation method of seismic phase based on modified STA/LTA algorithm[J].World Earthquake Engineering,2008,24(2):37-41).
[5]Tom Goforth,Eugene Herrin.An automatic seismic signal detection algorithm based on the walsh transform[J].Bull.Seism.Soc.Am,1981,71(4):1351-1360.
[6]刘希强,周慧兰,曹文海.高斯线调频小波变换及其在地震震相识别中的应用[J].地震学报,2002,24(6):607-616.
[7]Akaike,H.Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average processes[J].Ann.Inst.Stat.Math,1974,26,363-387.
[8]武安绪,吴培稚,兰从欣.Hilbert-Huang变换与地震信号的时频分析[J1.中国地震,2005,21(2):207-215.
[9]Kanwaldip S,Farid U.Dowla.Wavelet transform methods for phase identification in threecomponent seismograms[J],Bull.Seism.Soc.Am,1997,87:1598-1612.
[10]刘希强,周慧兰,等.基于小波变换的地震数据时频分析方法[J].西北地震学报,2000,22(2):143-146.
[11]Yomogid,K.Detection of anomalous seismic phases by the wavelet transform[J].Geophys.J.Int,1994,116:119-130.
[12]Chakraborty and D.Okaya.Frequency time decomposition of seismic data using wavelet based methods[J].Geophysics,1995,60:1906-1916.
[13]刘希强.基于小波包变换的弱震相识别方法[[J].地震学报,1998a,20(4):373-380.
[14]刘希强,郑治真.基于小波变换的信号突变检测、滤波和瞬态谱研究[J1.国际地震动态,1998b,1(4):373-380.
[15]Wang J.,Teng T.L.Artificial neural network-based seismic detector[J].Bull.Seism.Soc.Am,1995,85(1):308-319.
[16]张范民,李清河.利用人工神经网络理论对地震信号及地震震相进行识别[J].西北地震学报,1998,20(4):43-49.
[17]王娟,刘俊民,范万春.神经网络在震相识别中的应用[J].现代电子技术,2004,8(4):35-37.(Wang Juan,Liu Junmin,Fan Wanchun.Application of Neural Network in the Discrimination of Seismical Signal[J].Modern Electronic Technique,2004,8(4):35-37).
[18]Cheng Tong,B.L.N.Kennett.Auto seismic event recognition and later phase identification for broadband seismograms[J].Bull.Seism.Soc.Am,1996 86(6):1896-1909.
[19]Huang N E,Shen Z,Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings of the Royal Society of London.Series A:Mathematical,Physical and Engineering Sciences,1998,454(1971):903-995.
[20]Huang N E,Shen Z,Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings of the Royal Society of London.Series A:Mathematical,Physical and Engineering Sciences,1998,454(1971):903-995.
[21]Kopsinis Y,McLaughlin S.Development of EMD-based denoising methods inspiredby wavelet thresholding[J].IEEE Transactions on Signal Processing,2009,57(4):1351-1362.
[22]Vivo-Truyols G,Schoenmakers P J.Automatic selection of optimal Savitzky-Golay smoothing[J].Analytical chemistry,2006,78(13):4598-4608.
[23]Schafer R W.What is a Savitzky-Golay filter?[J].IEEE Signal Processing Magazine,2011,28(4):111-117.