自相关和褶积法单频干扰识别与消除
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摘要
在地震数据采集过程中会出现从浅层到深层频率、相位和振幅基本保持不变的50 Hz单频干扰。常规时间域压制噪声的方法虽然能够压制这种干扰,但在实际海量地震数据的运算中,耗费时间长,计算效率低。基于正、余弦函数的褶积和自相关运算结果是同频率的正、余弦函数,则单频干扰的褶积和自相关运算结果也是同频率的正、余弦函数。因此,可以通过褶积和自相关运算来确定单频干扰。自相关和褶积法利用地震数据的自相关函数和褶积函数,计算单频干扰的自相关函数和褶积函数,运用正、余弦函数自适应减算法快速估算单频干扰。由于频率和相位与单频干扰呈非线性函数关系,因此使用任何估算方法都非常耗时。运用自相关和褶积法识别与消除单频干扰不需要估算单频干扰的频率、相位和振幅,因此可以高效、快速地估算出单频干扰,达到消除单频干扰的目的。该方法的最大优点是在快速、高效地消除单频干扰的同时不损害单频干扰频率分量附近有效信号的频率成分,提高了单频干扰频率分量附近数据的信噪比。合成数据和实际数据试算结果表明,该方法有效且可行。
During field seismic data acquisition,there is a strong monofrequency interference(MFI) around 50Hz in seismic records.The frequency,phase and amplitude of MFI can be considered basically invariable from shallow to deep layer.Traditional time-domain suppression methods can suppress this interference,but their computational efficiency is very low,and operational cost is very high,so it is not very effective for the real huge seismic data.The autocorrelation and convolution operation results of a sine-cosine function are also the sine-cosine functions with the same frequencies,so the autocorrelation and convolution operation results of a MFI are also the sine-cosine functions of the same frequencies with the MFI. Thus,the MFI can be estimated by the autocorrelation and convolution operations for seismic records.For the autocorrelation and convolution algorithm(ACA),invoking the autocorrelation and the convolution function of a seismic record,those of the MFI are computed, and the MFI is quickly estimated by a sine-cosine function self-adaptive subtraction algorithm(SCFASA).Since a MFI has a nonlinear function relation with the frequency and phase,the computation of any parameter estimation algorithms takes long time. However,the identification and elimination of the MFI by the ACA would not need to estimate its amplitude,frequency and phase,so which can be utilized to estimate an MFI quickly and efficiently for the purpose of eliminating it.The greatest advantage of the ACA is that only interference with MFI component is quickly and effectively eliminated and its neighboring useful signal will not be damaged, so the signal-to-noise ratio has been improved in the vicinity of a MFI frequency component.The synthetic and real data examples illustrate that the method is feasible and effective.
During field seismic data acquisition,there is a strong monofrequency interference(MFI) around 50Hz in seismic records.The frequency,phase and amplitude of MFI can be considered basically invariable from shallow to deep layer.Traditional time-domain suppression methods can suppress this interference,but their computational efficiency is very low,and operational cost is very high,so it is not very effective for the real huge seismic data.The autocorrelation and convolution operation results of a sine-cosine function are also the sine-cosine functions with the same frequencies,so the autocorrelation and convolution operation results of a MFI are also the sine-cosine functions of the same frequencies with the MFI. Thus,the MFI can be estimated by the autocorrelation and convolution operations for seismic records.For the autocorrelation and convolution algorithm(ACA),invoking the autocorrelation and the convolution function of a seismic record,those of the MFI are computed, and the MFI is quickly estimated by a sine-cosine function self-adaptive subtraction algorithm(SCFASA).Since a MFI has a nonlinear function relation with the frequency and phase,the computation of any parameter estimation algorithms takes long time. However,the identification and elimination of the MFI by the ACA would not need to estimate its amplitude,frequency and phase,so which can be utilized to estimate an MFI quickly and efficiently for the purpose of eliminating it.The greatest advantage of the ACA is that only interference with MFI component is quickly and effectively eliminated and its neighboring useful signal will not be damaged, so the signal-to-noise ratio has been improved in the vicinity of a MFI frequency component.The synthetic and real data examples illustrate that the method is feasible and effective.
引文
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[10]Sauicer A,Marchant M,Chouteau M.A fast and accurate frequency estimation method for canceling harmonic noise in geophysical records[J].Geophysics, 2006,71(1):V7-V18
[11]Nyman D C,Gaiser J E.Adaptive rejection of highline contamination[J].Expanded Abstracts of 53~(rd) Annual Internat SEG Mtg,1983,321-323
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[13]高少武,贺振华,赵波,等.时间域单频干扰波消除方法的改进[J].石油地球物理勘探,2008,43(3): 270-274
[14]高少武,贺振华,赵波,等.自适应单频干扰波识别与消除方法研究[J].石油物探,2008,47(4):352-356
[15]罗国安,高少武,魏庚雨,等.Chirp-Z变换谱分析压制地震记录单频干扰[J].石油地球物理勘探,2009, 44(2):270-274
[2]石油物探研究修配厂.自动频率跟踪陷波器[J].石油物探,1978,17(2):17-30
[3]宋祈真.输电线对地震勘探的干扰及排除[J].石油地球物理勘探,1979,14(2):54-60
[4]孙文森.无相位畸变的50 Hz干扰滤波器[J].电测与仪表,1990(4):24 25
[5]王本吉,狄邦让.窄带自动跟踪陷波器[J].石油地球物理勘探,1985,20(4):411-414
[6]郭其贵.一种50 Hz陷波器的设计及效果[J].石油地球物理勘探,1988,23(5):626-629
[7]凌云,郭向宇.非地表一致性噪声的压制[J].石油地球物理勘探,1992,27(1):13-28
[8]高少武,周兴元,蔡加铭.时间域单频干扰波压制[J].石油地球物理勘探,2001,36(1):51-55
[9]刘洋.强工频干扰波的提取与消除方法[J].石油物探,2003,42(2):154-159
[10]Sauicer A,Marchant M,Chouteau M.A fast and accurate frequency estimation method for canceling harmonic noise in geophysical records[J].Geophysics, 2006,71(1):V7-V18
[11]Nyman D C,Gaiser J E.Adaptive rejection of highline contamination[J].Expanded Abstracts of 53~(rd) Annual Internat SEG Mtg,1983,321-323
[12]Butler K E,Don R R.Subtraction of powerline harmonics from geophysical records[J].Geophysics, 1993,58(6):898-903
[13]高少武,贺振华,赵波,等.时间域单频干扰波消除方法的改进[J].石油地球物理勘探,2008,43(3): 270-274
[14]高少武,贺振华,赵波,等.自适应单频干扰波识别与消除方法研究[J].石油物探,2008,47(4):352-356
[15]罗国安,高少武,魏庚雨,等.Chirp-Z变换谱分析压制地震记录单频干扰[J].石油地球物理勘探,2009, 44(2):270-274
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