连续重力观测异常模式的多分辨率识别算法
|
摘要
在小波多孔算法的基础上,提出了一种综合信号频率信息和幅值信息的连续重力观测数据多分辨率异常模式识别算法,利用小波分解得到高频区域的能量作为频率指标,与幅值相结合,对信号及其多孔小波分解结果进行多分辨率异常模式识别。利用模拟数据和实际超导重力观测数据对算法的有效性进行了验证,结果表明,该算法能够准确地在带有噪声的信号中识别模拟数据的异常模式,可应用于连续重力观测台网数据分析处理,对于提升台网观测数据质量以及地震预测等实际应用都具有重要意义。用此方法分析拉萨和武汉的3台超导重力仪2015-04-25尼泊尔地震前一天的秒采样数据后,得到一段27min的在频率指标上有超过90%相似性的异常模式,这一结果的更深层次物理解释仍需要进一步研究。
Based onàtrous algorithm,we propose a multiresolution algorithm,using the energy of high frequency domain of wavelet decomposition as the frequency index,combining with amplitude,to search for the abnormal pattern in the signal and its wavelet decomposition results.Simulated data and actual superconducting gravimeter(SG)results verify the effectiveness of this algorithm.Results show that the algorithm can accurately identify anomalies in the simulated data with noise.Using this algorithm when processing of continuous gravity observation data can improving the quality of China gravity network observation data with significance in earthquake prediction and other applications.We found 27-minute anomalies after analyzing three SG datasets for Lhasa and Wuhan after the 2015Nepal earthquake,with a similarity of over 90%.The reason of this phenomenon still needs further research in future.
Based onàtrous algorithm,we propose a multiresolution algorithm,using the energy of high frequency domain of wavelet decomposition as the frequency index,combining with amplitude,to search for the abnormal pattern in the signal and its wavelet decomposition results.Simulated data and actual superconducting gravimeter(SG)results verify the effectiveness of this algorithm.Results show that the algorithm can accurately identify anomalies in the simulated data with noise.Using this algorithm when processing of continuous gravity observation data can improving the quality of China gravity network observation data with significance in earthquake prediction and other applications.We found 27-minute anomalies after analyzing three SG datasets for Lhasa and Wuhan after the 2015Nepal earthquake,with a similarity of over 90%.The reason of this phenomenon still needs further research in future.
引文
[1]Liu Ziwei,Li Hui,Xu Zhonghua,et al.Improvement in Performance of Data Acquisition System of gPhone Gravitymeters[J].Journal of Geodesy and Geodynamics,2010,30(B11):102-104(刘子维,李辉,徐中华,等.gPhone重力仪数据采集系统性能的改进[J].大地测量与地球动力学,2010,30(B11):102-104)
[2]Sun Heping,Chen Xiaodong,Liu Ming,et al.Analysis and Comparison of the Tidal Gravity Observations Obtained with LCR-ET20Spring Gravimeter[J].Acta Seismologica Sinica,2002,15(5):533-539
[3]Banka D,Crossley D.Noise Levels of Superconducting Gravimeters at Seismic Frequencies[J].Geophysical Journal International,1999,139(1):87-97
[4]Tang K W,An D.A New Framework for Pattern Recognition of Time-series Data[D].New York:Stony Brook University,2004
[5]Duda R O,Hart P E,Stork D G.Pattern Classification[M].New York:Wiley,1973
[6]Duda R O,Hart P E,Stork D G.Pattern Classification[M].2nd ed.Hoboken,NJ:Wiley,2001
[7]Kaufman L,Rousseeuw P J.Finding Groups in Data:An Introduction to Cluster Analysis[M].Hoboken,NJ:Wiley,1990
[8]Yang Yanlin,Ye Feng,Lv Xin,et al.DTW Clustering-based Similarity Mining Method for Hydrological Time Series[J].Computer Science,2016,43(2):245-249(杨艳林,叶枫,吕鑫,等.一种基于DTW聚类的水文时间序列相似性挖掘方法[J].计算机科学,2016,43(2):245-249)
[9]Carpenter G A,Grossberg S.ART 2:Stable Selforganization of Pattern Recognition Codes for Analog Input Patterns[J].Applied Optics,1987,26(23):4 919-4 930
[10]Vargas F,Lettnin D,Felippettod C M C,et al.Electrocardiogram Pattern Recognition by Means of MLP Network and PCA:A Case Study on Equal Amount of Input Signal Types[C].SBRN 2002Proceedings VII Brazilian Symposium on IEEE,Pernambuco,2002
[11]Vedam H,Venkatasubramanian V.A Wavelet Theory-based Adaptive Trend Analysis System for Process Monitoring and Diagnosis[C].American Control Conference,IEEE,Albuquerque,1997
[12]Zheng Cheng,Cai Qingsheng.A Muti-scale Similar Pattern Match Approach for Times Series Databases[J].Mini-Micro System,2003,24(3):546-549(郑诚,蔡庆生.一种多尺度的时间序列相似模式匹配算法[J].小型微型计算机系统,2003,24(3):546-549)
[13]Zhang Haiqin,Cai Qingsheng.Time Series Similar Pattern Matching Based on Wavelet Transform[J].Chinese Journal of Computers,2003,26(3):373-377(张海勤,蔡庆生.基于小波变换的时间序列相似模式匹配[J].计算机学报,2003,26(3):373-377)
[14]Carpenter G A,Grossberg S,Rosen D B.Fuzzy ART:Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System[J].Neural Networks,1991,4(6):759-771
[15]Wong S K M.The Relational Structure of Belief Networks[J].Journal of Intelligent Information Systems,2001,16(2):117-148
[16]Looney C G.Pattern Recognition Using Neural Networks:Theory and Algorithms for Engineers and Scientists[M].New York:Oxford University Press,1997
[17]Dutilleux P.An Implementation of the“algorithmeàtrous”to Compute the Wavelet Transform[M].Berlin Heidelberg:Springer,1989
[18]Percival D B,Walden A T.Wavelet Methods for Time Series Analysis[M].Cambridge Cambridge University Press,2002
[19]Mou L,Chen Z X.The Optimal Choice of Wavelet Bases in Gravity Data Multi-scale Analysis[J].Geophysical and Geochemical Exploration,2015,39(5):1 013-1 019(牟力,陈召曦.重力资料多尺度分析最优小波基的选择[J].物探与化探,2015,39(5):1 013-1 019)
[20]Xu Huajun,Liu Lintao,Xu Houze,et al.Wavelet Approach to Study Gravity Pole Tide[J].Geomatics and Information Science of Wuhan University,2008,33(11):1 114-1 117(徐华君,柳林涛,许厚泽,等.重力极潮的小波分析[J].武汉大学学报·信息科学版,2008,33(11):1 114-1 117)
[21]Xu Chuang,Luo Zhicai,Lin Xu,et al.Automatic Preprocessing of Tidal Gravity Observation Data[J].Geomatics and Information Science of Wuhan University,2013,38(2):157-161(许闯,罗志才,林旭,等.重力固体潮观测数据的自动化预处理[J].武汉大学学报·信息科学版,2013,38(2):157-161)
[22]Xu Huajun,Liu Lintao,Xu Houze,et al.Wavelet Approach to Study the Secular Gravity Variation[J].Chinese Journal of Geophysics,2008,51(3):735-742(徐华君,柳林涛,许厚泽,等.利用小波分析重力的长期变化[J].地球物理学报,2008,51(3):735-742)
[2]Sun Heping,Chen Xiaodong,Liu Ming,et al.Analysis and Comparison of the Tidal Gravity Observations Obtained with LCR-ET20Spring Gravimeter[J].Acta Seismologica Sinica,2002,15(5):533-539
[3]Banka D,Crossley D.Noise Levels of Superconducting Gravimeters at Seismic Frequencies[J].Geophysical Journal International,1999,139(1):87-97
[4]Tang K W,An D.A New Framework for Pattern Recognition of Time-series Data[D].New York:Stony Brook University,2004
[5]Duda R O,Hart P E,Stork D G.Pattern Classification[M].New York:Wiley,1973
[6]Duda R O,Hart P E,Stork D G.Pattern Classification[M].2nd ed.Hoboken,NJ:Wiley,2001
[7]Kaufman L,Rousseeuw P J.Finding Groups in Data:An Introduction to Cluster Analysis[M].Hoboken,NJ:Wiley,1990
[8]Yang Yanlin,Ye Feng,Lv Xin,et al.DTW Clustering-based Similarity Mining Method for Hydrological Time Series[J].Computer Science,2016,43(2):245-249(杨艳林,叶枫,吕鑫,等.一种基于DTW聚类的水文时间序列相似性挖掘方法[J].计算机科学,2016,43(2):245-249)
[9]Carpenter G A,Grossberg S.ART 2:Stable Selforganization of Pattern Recognition Codes for Analog Input Patterns[J].Applied Optics,1987,26(23):4 919-4 930
[10]Vargas F,Lettnin D,Felippettod C M C,et al.Electrocardiogram Pattern Recognition by Means of MLP Network and PCA:A Case Study on Equal Amount of Input Signal Types[C].SBRN 2002Proceedings VII Brazilian Symposium on IEEE,Pernambuco,2002
[11]Vedam H,Venkatasubramanian V.A Wavelet Theory-based Adaptive Trend Analysis System for Process Monitoring and Diagnosis[C].American Control Conference,IEEE,Albuquerque,1997
[12]Zheng Cheng,Cai Qingsheng.A Muti-scale Similar Pattern Match Approach for Times Series Databases[J].Mini-Micro System,2003,24(3):546-549(郑诚,蔡庆生.一种多尺度的时间序列相似模式匹配算法[J].小型微型计算机系统,2003,24(3):546-549)
[13]Zhang Haiqin,Cai Qingsheng.Time Series Similar Pattern Matching Based on Wavelet Transform[J].Chinese Journal of Computers,2003,26(3):373-377(张海勤,蔡庆生.基于小波变换的时间序列相似模式匹配[J].计算机学报,2003,26(3):373-377)
[14]Carpenter G A,Grossberg S,Rosen D B.Fuzzy ART:Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System[J].Neural Networks,1991,4(6):759-771
[15]Wong S K M.The Relational Structure of Belief Networks[J].Journal of Intelligent Information Systems,2001,16(2):117-148
[16]Looney C G.Pattern Recognition Using Neural Networks:Theory and Algorithms for Engineers and Scientists[M].New York:Oxford University Press,1997
[17]Dutilleux P.An Implementation of the“algorithmeàtrous”to Compute the Wavelet Transform[M].Berlin Heidelberg:Springer,1989
[18]Percival D B,Walden A T.Wavelet Methods for Time Series Analysis[M].Cambridge Cambridge University Press,2002
[19]Mou L,Chen Z X.The Optimal Choice of Wavelet Bases in Gravity Data Multi-scale Analysis[J].Geophysical and Geochemical Exploration,2015,39(5):1 013-1 019(牟力,陈召曦.重力资料多尺度分析最优小波基的选择[J].物探与化探,2015,39(5):1 013-1 019)
[20]Xu Huajun,Liu Lintao,Xu Houze,et al.Wavelet Approach to Study Gravity Pole Tide[J].Geomatics and Information Science of Wuhan University,2008,33(11):1 114-1 117(徐华君,柳林涛,许厚泽,等.重力极潮的小波分析[J].武汉大学学报·信息科学版,2008,33(11):1 114-1 117)
[21]Xu Chuang,Luo Zhicai,Lin Xu,et al.Automatic Preprocessing of Tidal Gravity Observation Data[J].Geomatics and Information Science of Wuhan University,2013,38(2):157-161(许闯,罗志才,林旭,等.重力固体潮观测数据的自动化预处理[J].武汉大学学报·信息科学版,2013,38(2):157-161)
[22]Xu Huajun,Liu Lintao,Xu Houze,et al.Wavelet Approach to Study the Secular Gravity Variation[J].Chinese Journal of Geophysics,2008,51(3):735-742(徐华君,柳林涛,许厚泽,等.利用小波分析重力的长期变化[J].地球物理学报,2008,51(3):735-742)