f-x域共偏移距道集的EEMD随机噪声压制方法
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摘要
f-x域随机噪声压制方法面临着2个问题:叠前共炮点道集或CMP道集反射波同相轴为双曲线型,去噪同时会损害有效波;地震信号为复杂的非平稳信号,要求去噪方法具有自适应性。基于f-xEEMD的共偏移距道集随机噪声压制方法利用了共偏移距道集反射波同相轴为水平满足f-x域去噪假设条件和EEMD算法对非平稳信号的良好适应性,对f-x域每一个等频率切片做EEMD分解,并去除以高频随机噪声为主的第一个IMF分量,最后将f-x域数据反变换回t-x域,实现噪声分离。正演模拟和实际地震数据试算结果表明:该方法在压制随机噪声的同时,能够保持有效信号。
There are two problems in the random noise attenuation in f-x domain:(1) Reflected waves as hyperbolic events in pre-stack CMP gather or shot gathers, de-noising will damage the effective wave;(2) Seismic signals are complex non-stationary signals, requiring that the de-noising method has the adaptivity. Aiming at these two problems, a random noise suppression method based on f-x EEMD was proposed. The method utilizes the reflected waves which are horizontal events in common offset gathers, satisfying the f-x domain de-noising assumption, and the good adaptability of EEMD algorithm to non-stationary signals. For each frequency slices in the f-x domain,signal is decomposed into a series of IMFs by EEMD, and the first IMF component which is noise dominant is removed. Finally, f-x domain data is inversely transformed back to t-x domain to realize noise separation. The theoretical trial and practical application indicate that the proposed method can suppress the random noise while maintaining the desired signal.
There are two problems in the random noise attenuation in f-x domain:(1) Reflected waves as hyperbolic events in pre-stack CMP gather or shot gathers, de-noising will damage the effective wave;(2) Seismic signals are complex non-stationary signals, requiring that the de-noising method has the adaptivity. Aiming at these two problems, a random noise suppression method based on f-x EEMD was proposed. The method utilizes the reflected waves which are horizontal events in common offset gathers, satisfying the f-x domain de-noising assumption, and the good adaptability of EEMD algorithm to non-stationary signals. For each frequency slices in the f-x domain,signal is decomposed into a series of IMFs by EEMD, and the first IMF component which is noise dominant is removed. Finally, f-x domain data is inversely transformed back to t-x domain to realize noise separation. The theoretical trial and practical application indicate that the proposed method can suppress the random noise while maintaining the desired signal.
引文
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[15]FU Yanxiao,JIA Limin,QIN Yong,et al.Fast EEMD based AM-correntropy matrix and its application on roller bearing fault diagnosis[J].Entropy,2016,18(7):242.
[16]黄海波,黄晓蓉,苏瑞强,等.基于EEMD与GA-小波神经网络的传动系声品质预测[J].振动与冲击,2017,36(9):130-137.HUANG Haibo,HUANG Xiaorong,SU Ruiqiang,et al.Prediction of power train sound metric based on ensemble empirical mode decomposition and GA-Wavelet neural network[J].Journal of Vibration and Shock,2017,36(9):130-137.
[17]GóMEZ J L,VELIS D R.A fast empirical mode decomposition for noise attenuation of seismic data[C]//Seg Technical Program Expanded.New Orleans:Society of Exploration Geophysicists,2015.
[18]WU W,PENG H.Application of EMD denoising approach in noisy blind source separation[J].Journal of Communications,2014,9(6):506-514.
[2]CANALES L L.Random noise reduction[C]//Seg Technical Program Expanded Abstracts.Atlanta:SEG,1984.
[3]国九英,周兴元,杨慧珠.三维f-x,y域随机噪音衰减[J].石油地球物理勘探,1995,30(2):207-215.GUO Jiuying,ZHOU Xingyuan,YANG Huizhu.Attenuation of random noise in(f-x,y)domain[J].Oil Geophysical Prospecting,1995,30(2):207-215.
[4]CHEN Y,MA J.Random noise attenuation by f-x empirical-mode decomposition predictive filtering[J].Geophysics,2013,79(3):81-91.
[5]苏贵士,周兴元,李承楚.频率空间(三维)F-XYZ域预测去噪技术[J].石油地球物理勘探,1998,33(1):95-104.SU Guishi,ZHOU Xingyuan,LI Chengchu.Prediction of noise elimination in F-XYZ domain[J].Oil Geophysical Prospecting,1998,33(1):95-104.
[6]康冶,于承业,贾卧,等.F-x域去噪方法研究[J].石油地球物理勘探,2003,38(2):136-138.KANG Ye,YU Chengye,JIA Wo,et al.A study on noise-suppression method in f-x domain[J].Oil Geophysical Prospecting,2003,38(2):136-138.
[7]BEKARA M,VAN DER BAAN M.Random and coherent noise attenuation by empirical mode decomposition[J].Geophysics,2008,74(5):89-98.
[8]CAI Hanpeng,HE Zhenhua,HUANG Deji.Seismic data denoising based on mixed time-frequency methods[J].Applied Geophysics,2011,8(4):319-327.
[9]CHEN Yangkang,GAN Shuwei,LIU Tingting,et al.Random noise attenuation by a selective hybrid approach using f-x empirical mode decomposition[J].Journal of Geophysics&Engineering,2015,12(1):12-25.
[10]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 A:Mathematical Physical&Engineering Sciences,1998,454:903-995.
[11]RILLING G,FLANDRIN P,GONCALVES P.On empirical mode decomposition and its algorithms[C]//In Proceedings of IEEE-EURASIPWorkshop on Nonlinear Signal and Image Processing NSIP-03,Grado,2003.
[12]LIU Yanping,LI Yue,LIN Hongbo,et al.An amplitude-preserved time-frequency peak filtering based on empirical mode decomposition for seismic random noise reduction[J].IEEE Geoscience&Remote Sensing Letters,2014,11(5):896-900.
[13]WU Zhaohua,HUANG Norden E.Ensemble empirical mode decomposition:A noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,1(1):1-41.
[14]WANG Y H,YEH C H,VINCENT YOUNG H W,et al.On the computational complexity of the empirical mode decomposition algorithm[J].Physica A:Statistical Mechanics&Its Applications,2014,400:159-167.
[15]FU Yanxiao,JIA Limin,QIN Yong,et al.Fast EEMD based AM-correntropy matrix and its application on roller bearing fault diagnosis[J].Entropy,2016,18(7):242.
[16]黄海波,黄晓蓉,苏瑞强,等.基于EEMD与GA-小波神经网络的传动系声品质预测[J].振动与冲击,2017,36(9):130-137.HUANG Haibo,HUANG Xiaorong,SU Ruiqiang,et al.Prediction of power train sound metric based on ensemble empirical mode decomposition and GA-Wavelet neural network[J].Journal of Vibration and Shock,2017,36(9):130-137.
[17]GóMEZ J L,VELIS D R.A fast empirical mode decomposition for noise attenuation of seismic data[C]//Seg Technical Program Expanded.New Orleans:Society of Exploration Geophysicists,2015.
[18]WU W,PENG H.Application of EMD denoising approach in noisy blind source separation[J].Journal of Communications,2014,9(6):506-514.