强噪声环境下基于信噪比的地震P波到时自动提取方法
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摘要
大数据量、强噪声环境给地震P波到时的自动提取带来很大挑战.针对此问题,本文通过构建特殊的特征函数,建立SNR与STA/LTA的内在联系,提出两种基于SNR的地震P波到时自动提取方法,即基于SNR的STA/LTA方法与基于SNR的综合方法.这两种方法分别是运用SNR概念对传统STA/LTA方法和STA/LTA与AIC综合方法的改进.仿真分析结果表明:对于弱噪声环境(10dB)和一般噪声环境(6dB),本文方法较传统STA/LTA方法对地震P波到时提取的准确度更高;而对于强噪声环境(3dB),本文方法仍能准确提取地震P波到时,而传统STA/LTA方法则出现了较大的误判率(10%)与漏判率(65%).本文方法为STA/LTA赋予了明确的物理意义,使其阈值的选取建立在严密的数学推导之上.另外,本文方法在进行地震P波到时自动提取的同时,兼具数据预处理功能,无需额外的基线校正或高通滤波,因而具有较好的实时性.
Big data and strong noise environments bring great challenges to pick up P waves automatically.In order to solve this problem,a special characteristic function is constructed with the conception of Signal-to-Noise Ratios(SNR).By using this function,an internal relationship between the SNR and the Short-Term Average and Long-Term Average ratio(STA/LTA)is built.And two novel SNR-based P waves′picking methods are proposed,namely the SNR-based STA/LTA method and the SNR-based comprehensive method which are respectively the improvements of the traditional STA/LTA method and the comprehensive method of STA/LTA and Akaike Information Criteria(AIC)by using the SNR conception.The simulation analysis shows that under a weak noise circumstance(10 dB)and normal noise circumstance(6 dB)the two proposed methods have higher accuracy than the traditional STA/LTA method.And under a strong noise circumstance(3 dB),both the methods can accurately pick up the seismic P waves without any regulations,whereas the traditional STA/LTA method has a big error ratio(10%)and a large missing ratio(65%).The proposed methods give STA/LTA a clear physical meaning,and their thresholds can be obtained based on rigorous mathematical derivation.In addition,the proposed methods have favorable real-time performances because they can process data without requirement of additional baseline correction or high-pass filtering.
Big data and strong noise environments bring great challenges to pick up P waves automatically.In order to solve this problem,a special characteristic function is constructed with the conception of Signal-to-Noise Ratios(SNR).By using this function,an internal relationship between the SNR and the Short-Term Average and Long-Term Average ratio(STA/LTA)is built.And two novel SNR-based P waves′picking methods are proposed,namely the SNR-based STA/LTA method and the SNR-based comprehensive method which are respectively the improvements of the traditional STA/LTA method and the comprehensive method of STA/LTA and Akaike Information Criteria(AIC)by using the SNR conception.The simulation analysis shows that under a weak noise circumstance(10 dB)and normal noise circumstance(6 dB)the two proposed methods have higher accuracy than the traditional STA/LTA method.And under a strong noise circumstance(3 dB),both the methods can accurately pick up the seismic P waves without any regulations,whereas the traditional STA/LTA method has a big error ratio(10%)and a large missing ratio(65%).The proposed methods give STA/LTA a clear physical meaning,and their thresholds can be obtained based on rigorous mathematical derivation.In addition,the proposed methods have favorable real-time performances because they can process data without requirement of additional baseline correction or high-pass filtering.
引文
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Allen R M,Kanamori H.2004.The potential for earthquake early warning in southern California.Science,300(5620):786-789.
Allen R V.1978.Automatic earthquake recognition and timing from single trace.Bulletin of the Seismological Society of America,68(5):1521-1532.
Bataille K,Chiu J M.1991.Polarization analysis of high-frequency,three-component seismic data.Bulletin of the Seismological Society of America,81(2):622-642.
Chen H Z,Hou Y Y,He J Y,et al.2011.The well development of earthquake warning system of Japan.Recent Developments in World Seismology(in Chinese),(4):10-15.
Evans J R,Allen R M,Chung A I,et al.2014.Performance of several low-Cost accelerometers.Seismological Research Letters,85(1):147-158.
Han L J,Wong J,Bancroft J C.2009.Time picking on noisy microseismograms.CREWES Research Report,21:30.1-30.13.
Kanamori H,Hauksson E,Heaton T.1997.Real-time seismology and earthquake hazard mitigation.Nature,390(6659):461-464.
Liu J H,Liu F T,Xu Y.2006.Polarization Analysis of Threecomponent Seismic Data.Process in Geophysics(in Chinese),21(1):6-10,doi:10.3969/j.issn.1004-2903.2006.01.002.
Ma Q,Jin X,Li Y S,et al.2013.Automatic P-arrival detection for earthquake early warning.Chinese Journal of Geophysics(in Chinese),56(7):2313-2321,doi:10.6038/cjg20130718.
Munro K A.2004.Automatic event detection and picking of P-wave arrivals.CREWES Research Report,18:12.1-12.10.
Tosi P,Barba S,De Rubeis V,et al.1999.Seismic signal detection by fractal dimension analysis.Bulletin of the Seismological Society of America,89(4):970-977.
Wang X,Fu J H,Tang C P,et al.2018.A P waves′automatic picking by detecting the changes of seismic signals′stationary random process through similarity analysis.Soil Dynamics and Earthquake Engineering,115:225-231.
Yin H T,Liu X Q,Li J,et al.2012.Current Earthquake Early Warning technology and it's development in China.Earthquake Research in China(in Chinese),28(1):1-9.
Zhang H C,Jin X,Li J,et al.2013.Progress of research and application of earthquake early warning system(EEWs).Progress in Geophysics(in Chinese),28(2):706-719,doi:10.6038/pg20130219.
陈会忠,侯燕燕,何加勇等.2011.日本地震预警系统日趋完善.国际地震动态,(4):10-15.
刘建华,刘福田,胥頤.2006.三分量地震资料的偏振分析.地球物理学进展,21(1):6-10,doi:10.3969/j.issn.1004-2903.2006.01.002.
马强,金星,李山有等.2013.用于地震预警的P波震相到时自动拾取.地球物理学报,56(7):2313-2321,doi:10.6038/cjg20130718.
殷海涛,刘希强,李杰等.2012.现今地震预警技术及其在国内发展状况的探讨.中国地震,28(1):1-9.
张红才,金星,李军等.2013.地震预警系统研究及应用进展.地球物理学进展,28(2):706-719,doi:10.6038/pg20130219.
Allen R M,Kanamori H.2004.The potential for earthquake early warning in southern California.Science,300(5620):786-789.
Allen R V.1978.Automatic earthquake recognition and timing from single trace.Bulletin of the Seismological Society of America,68(5):1521-1532.
Bataille K,Chiu J M.1991.Polarization analysis of high-frequency,three-component seismic data.Bulletin of the Seismological Society of America,81(2):622-642.
Chen H Z,Hou Y Y,He J Y,et al.2011.The well development of earthquake warning system of Japan.Recent Developments in World Seismology(in Chinese),(4):10-15.
Evans J R,Allen R M,Chung A I,et al.2014.Performance of several low-Cost accelerometers.Seismological Research Letters,85(1):147-158.
Han L J,Wong J,Bancroft J C.2009.Time picking on noisy microseismograms.CREWES Research Report,21:30.1-30.13.
Kanamori H,Hauksson E,Heaton T.1997.Real-time seismology and earthquake hazard mitigation.Nature,390(6659):461-464.
Liu J H,Liu F T,Xu Y.2006.Polarization Analysis of Threecomponent Seismic Data.Process in Geophysics(in Chinese),21(1):6-10,doi:10.3969/j.issn.1004-2903.2006.01.002.
Ma Q,Jin X,Li Y S,et al.2013.Automatic P-arrival detection for earthquake early warning.Chinese Journal of Geophysics(in Chinese),56(7):2313-2321,doi:10.6038/cjg20130718.
Munro K A.2004.Automatic event detection and picking of P-wave arrivals.CREWES Research Report,18:12.1-12.10.
Tosi P,Barba S,De Rubeis V,et al.1999.Seismic signal detection by fractal dimension analysis.Bulletin of the Seismological Society of America,89(4):970-977.
Wang X,Fu J H,Tang C P,et al.2018.A P waves′automatic picking by detecting the changes of seismic signals′stationary random process through similarity analysis.Soil Dynamics and Earthquake Engineering,115:225-231.
Yin H T,Liu X Q,Li J,et al.2012.Current Earthquake Early Warning technology and it's development in China.Earthquake Research in China(in Chinese),28(1):1-9.
Zhang H C,Jin X,Li J,et al.2013.Progress of research and application of earthquake early warning system(EEWs).Progress in Geophysics(in Chinese),28(2):706-719,doi:10.6038/pg20130219.
陈会忠,侯燕燕,何加勇等.2011.日本地震预警系统日趋完善.国际地震动态,(4):10-15.
刘建华,刘福田,胥頤.2006.三分量地震资料的偏振分析.地球物理学进展,21(1):6-10,doi:10.3969/j.issn.1004-2903.2006.01.002.
马强,金星,李山有等.2013.用于地震预警的P波震相到时自动拾取.地球物理学报,56(7):2313-2321,doi:10.6038/cjg20130718.
殷海涛,刘希强,李杰等.2012.现今地震预警技术及其在国内发展状况的探讨.中国地震,28(1):1-9.
张红才,金星,李军等.2013.地震预警系统研究及应用进展.地球物理学进展,28(2):706-719,doi:10.6038/pg20130219.