基于新的模态单元滤波消除地震信号中的汽车噪声
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摘要
由于地震信号采集环境和采集仪器存在干扰,采集到的地震波形中通常包含很多噪声信号,严重影响了地震信号的应用。模态单元滤波(Mode Cell Filter:MCF)无需先验基函数,是一种自适应的消噪算法。将MCF引入到地震信号消噪中,提出了一种基于MCF的汽车干扰消除算法,并设计对仿真信号和实际信号的消噪试验。仿真试验中,MCF消噪性能优于最优小波基算法,而在实际信号消噪中,MCF算法性能优于IIR数字滤波器,与改进最优小波基方法相当。
As there are interference sources in surroundings and instruments collecting seismic wave signals,the seismic waves collected may always be influenced by many noises affecting the further applications of the seismic waves.The mode cell filter(MCF) is a novel adaptive denoising method with no need for pre-defined basic functions.Here,the MCF was introduced to the seismic wave denoising,and a MCF-based automobile noise eliminating method was proposed.Experiments on artificial and natural signals were designed for denoising.The results showed that the performances of the MCF-based denoising method are better than the selected optimal wavelet threshold method in the artificial case,while in the natural case,the MCF-based method is better than IIR digital filter and equivalent to the improved optimal wavelet threshold method.
As there are interference sources in surroundings and instruments collecting seismic wave signals,the seismic waves collected may always be influenced by many noises affecting the further applications of the seismic waves.The mode cell filter(MCF) is a novel adaptive denoising method with no need for pre-defined basic functions.Here,the MCF was introduced to the seismic wave denoising,and a MCF-based automobile noise eliminating method was proposed.Experiments on artificial and natural signals were designed for denoising.The results showed that the performances of the MCF-based denoising method are better than the selected optimal wavelet threshold method in the artificial case,while in the natural case,the MCF-based method is better than IIR digital filter and equivalent to the improved optimal wavelet threshold method.
引文
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[2]刘代志,张斌,李夕海,等.核爆地震模式识别中的特征相空间研究[J].地球物理学报,2006 49(3):871-876.
[3]万用革.数字信号处理的MATLAB实现[M].北京:科学出版社,2007.
[4]Donoho D L,Johnstone I M.Ideal spatial adaptation by wave-let shrinkage[J].Biometrika,1994,81:425-455.
[5]Donoho D L.De-noising by soft-thresholding[J].IEEETrans.on Inf.Theory,1995,41(3):613-627.
[6]Huang N E,Shen Z,Long S R,et al.The empirical modedecomposition and the Hilbert spectrum for nonlinear andnon-stationary time series analysis[J].Proc.Roy.Soc.Lon-don A,1998,454:903-995.
[7]Boudraa A O,Cexus J C.EMD-Based Signal Filtering[J].IEEE Transactions on Instrumentation and Measurement,2007,56(6):2196-2202.
[8]Adriano O A,Nasuto S,Kyberd P,et al.Van Kanijn,EMGsignal filtering based on Empirical Mode Decomposition[J].Biomedical Signal Processing and Control,2006,1(1):44-55.
[9]Manuel B V,Weng B W,Barner K E.ECG signal denoisingand baseline wander correction based on the empirical modedecomposition[J].Computers in Biology and Medicine,2008,38(1):1-13.
[10]Hadjileontiadis L J.A Novel Technique for Denoising Explo-sive Lung Sounds Empirical Mode Decomposition and FractalDimension Foilter[J].IEEE Engineering in Medicine and Bi-ology Magazine,2007,26(1):30-39.
[11]Flandrin P,Rilling G,Goncalves P.Empirical mode decom-position as a filter bank[J].IEEE Signal Processing Letters,2004,11(1):112-114.
[12]钱昌松,刘代志,刘志刚,等.基于模态振幅的模态单元滤波及其在高斯消噪中的应用[EB/OL],http://www.pa-per.edu.cn/,中国科技论文在线,2008.
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