基于互补集合经验模态分解的近场脉冲地震信号降噪算法
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摘要
针对近断层地震速度脉冲信号非线性非平稳的特点,建立了以互补集合经验模态分解(Coplementary Ensemble Empirical Mode Decomposition,CEEMD)为基础的一种降噪算法。该算法首先对仿真信号进行CEEMD操作,获得从高频到低频的固有模态函数(Intrinsic Mode Function,IMF),然后对IMF筛选叠加,得到多组含不同阶数的重构信号;通过算法相关度和逼近度组成综合评价指标,对多组重构信号进行筛选,得到全局最优重构组合Rec3,实现信号的有效去噪。然后采用以所选最优重构组合为基础的降噪算法分析实测速度脉冲信号。结果表明:该降噪算法具有很好的降噪效果,所得结果曲线较原信号曲线光滑平整,主脉冲信号清晰可辨,且由于算法本身没有人工加窗这类操作,使得其自适应良好,具有一定的实用性和可靠性。
Aiming at the nonlinear and non-stationary characteristics of near-fault pulse-like earthquake signals,a denoise algorithm based on Complementary Ensemble Empirical Mode Decomposition( CEEMD) was established.Firstly,the algorithm performs CEEMD on the simulated signal to obtain the Intrinsic Mode Function( IMF)components ranging from high frequency to low frequency.The IMFs are superimposed to obtain multiple sets of reconstructed signals with different orders.A comprehensive evaluation index composed of algorithm correlation and approximation degree is used to screen multiple sets of reconstructed signals,and the global optimal reconstruction combination Rec3 is obtained to achieve effective denoising of signals.The selected excellent noise reduction algorithm is applied to analyze the measured velocity pulse signal.The results show that the noise reduction algorithm has a good noise reduction effect,and the obtained result curve is smoother than the original signal curve.The main pulse signal is also clearly identifiable. The algorithm itself has no manual windowing operation,contributing better selfadaptability.Therefore,the algorithm is practicable and reliable.
Aiming at the nonlinear and non-stationary characteristics of near-fault pulse-like earthquake signals,a denoise algorithm based on Complementary Ensemble Empirical Mode Decomposition( CEEMD) was established.Firstly,the algorithm performs CEEMD on the simulated signal to obtain the Intrinsic Mode Function( IMF)components ranging from high frequency to low frequency.The IMFs are superimposed to obtain multiple sets of reconstructed signals with different orders.A comprehensive evaluation index composed of algorithm correlation and approximation degree is used to screen multiple sets of reconstructed signals,and the global optimal reconstruction combination Rec3 is obtained to achieve effective denoising of signals.The selected excellent noise reduction algorithm is applied to analyze the measured velocity pulse signal.The results show that the noise reduction algorithm has a good noise reduction effect,and the obtained result curve is smoother than the original signal curve.The main pulse signal is also clearly identifiable. The algorithm itself has no manual windowing operation,contributing better selfadaptability.Therefore,the algorithm is practicable and reliable.
引文
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[6]叶昆.近断层脉冲型地震作用下LRB基础隔震结构地震响应研究[D].武汉:华中科技大学,2008.
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[10]GACI S.The Use of Wavelet-based Denoising Techniques to Enhance the First-arrival Picking on Seismic Traces[J].IEEE Transactions on Geoscience&Remote Sensing,2014,52(8):4558-4563.
[11]秦晅,蔡建超,刘少勇,等.基于经验模态分解互信息熵与同步压缩变换的微地震信号去噪方法研究[J].石油物探,2017,56(5):658-666.
[12]刘霞,黄阳,黄敬,等.基于经验模态分解(EMD)的小波熵阈值地震信号去噪[J].吉林大学学报(地球科学版),2016,46(1):262-269.
[13]颜中辉,栾锡武,王赟,等.基于经验模态分解的分数维地震随机噪声衰减方法[J].地球物理学报,2017,60(7):2845-2857.
[14]杨成,唐泽楠,常志旺,等.基于经验模态分解的速度脉冲型地震动量化识别[J].工程力学,2017,34(4):206-212.
[15]薛雅娟,曹俊兴.聚合经验模态分解和小波变换相结合的地震信号衰减分析[J].石油地球物理勘探,2016,51(6):1148-1155.
[16]YEH J R,SHIEH J S,HUANG N E.Complementary Ensemble Empirical Mode Decomposition:A Novel Noise Enhanced Data Analysis Method[J]. Advances in Adaptive Data Analysis,2010,2(2):135-156.
[17]王姣,李振春,王德营.基于CEEMD的地震数据小波阈值去噪方法研究[J].石油物探,2014,53(2):164-172.
[18]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 Mathematical Physical&Engineering Sciences,1998,454(5):903-995.
[19]HUANG G,SU Y,KAREEM A,et al.Time-frequency Analysis of Nonstationary Process Based on Multivariate Empirical Mode Decomposition[J]. Journal of Engineering Mechanics,2016,142(1):04015065.
[20]BOUDRAA A O,CEXUS J C. EMD-based Signal Filtering[J].IEEE Transactions on Instrumentation&Measurement,2007,56(6):2196-2202.
[21]王婷.EMD算法研究及其在信号去噪中的应用[D].哈尔滨:哈尔滨工程大学,2010.
[22]赵肖宇.基于EMD和EEMD的自适应光谱预处理方法及其应用研究[D].秦皇岛:燕山大学,2015.
[23]郑一,孙晓峰,陈健,等.基于集合经验模态的随钻脉冲信号优良降噪算法[J].石油勘探与开发,2012,39(6):750-753.
[24]杨永锋,吴亚锋.经验模态分解在振动分析中的应用[M].北京:国防工业出版社,2013.
[2]潘毅,季晨龙,王超,等.日本地震中钢筋混凝土框架结构震害及分析[J].工程抗震与加固改造,2012,34(4):122-127.
[3]潘毅,王忠凯,时胜杰,等.尼泊尔8. 1级地震加德满都—樟木沿线民居震害调查与分析[J].湖南大学学报(自然科学版),2017,44(3):35-44.
[4]潘毅,谢丹,袁双,等.尼泊尔8. 1级地震文化遗产建筑震害调查与分析[J].西南交通大学学报,2015,50(6):1039-1046.
[5]潘毅,张弛,高宪,等.台湾美浓6. 7级地震框架结构震害调查与分析[J].土木工程学报,2016,49(增1):13-18.
[6]叶昆.近断层脉冲型地震作用下LRB基础隔震结构地震响应研究[D].武汉:华中科技大学,2008.
[7]李明,刘勇,孙哲哲,等.不同类型近断层脉冲型地震动对框架-剪力墙结构的影响[J].工程力学,2015,32(增1):294-297,303.
[8]邱志刚,罗奇峰.脉冲型地震动的潜在破坏能分析[J].土木工程学报,2014,47(12):56-63.
[9]ALVANITOPOULOS P F,PAPAVASILEIOU M,ANDREADIS I,et al. Seismic Intensity Feature Construction Based on the Hilbert-Huang Transform[J]. IEEE Transactions on Instrumentation&Measurement,2012,61(2):326-337.
[10]GACI S.The Use of Wavelet-based Denoising Techniques to Enhance the First-arrival Picking on Seismic Traces[J].IEEE Transactions on Geoscience&Remote Sensing,2014,52(8):4558-4563.
[11]秦晅,蔡建超,刘少勇,等.基于经验模态分解互信息熵与同步压缩变换的微地震信号去噪方法研究[J].石油物探,2017,56(5):658-666.
[12]刘霞,黄阳,黄敬,等.基于经验模态分解(EMD)的小波熵阈值地震信号去噪[J].吉林大学学报(地球科学版),2016,46(1):262-269.
[13]颜中辉,栾锡武,王赟,等.基于经验模态分解的分数维地震随机噪声衰减方法[J].地球物理学报,2017,60(7):2845-2857.
[14]杨成,唐泽楠,常志旺,等.基于经验模态分解的速度脉冲型地震动量化识别[J].工程力学,2017,34(4):206-212.
[15]薛雅娟,曹俊兴.聚合经验模态分解和小波变换相结合的地震信号衰减分析[J].石油地球物理勘探,2016,51(6):1148-1155.
[16]YEH J R,SHIEH J S,HUANG N E.Complementary Ensemble Empirical Mode Decomposition:A Novel Noise Enhanced Data Analysis Method[J]. Advances in Adaptive Data Analysis,2010,2(2):135-156.
[17]王姣,李振春,王德营.基于CEEMD的地震数据小波阈值去噪方法研究[J].石油物探,2014,53(2):164-172.
[18]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 Mathematical Physical&Engineering Sciences,1998,454(5):903-995.
[19]HUANG G,SU Y,KAREEM A,et al.Time-frequency Analysis of Nonstationary Process Based on Multivariate Empirical Mode Decomposition[J]. Journal of Engineering Mechanics,2016,142(1):04015065.
[20]BOUDRAA A O,CEXUS J C. EMD-based Signal Filtering[J].IEEE Transactions on Instrumentation&Measurement,2007,56(6):2196-2202.
[21]王婷.EMD算法研究及其在信号去噪中的应用[D].哈尔滨:哈尔滨工程大学,2010.
[22]赵肖宇.基于EMD和EEMD的自适应光谱预处理方法及其应用研究[D].秦皇岛:燕山大学,2015.
[23]郑一,孙晓峰,陈健,等.基于集合经验模态的随钻脉冲信号优良降噪算法[J].石油勘探与开发,2012,39(6):750-753.
[24]杨永锋,吴亚锋.经验模态分解在振动分析中的应用[M].北京:国防工业出版社,2013.