基于非稳态多项式拟合的地震噪声衰减方法研究(英文)
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摘要
基于非稳态多项式拟合理论,针对地震数据中同相轴振幅变化这一特征,我们提出了一种地震噪声衰减的新方法。非稳态多项式拟合系数是时变的,通过整形正则化约束多项式拟和系数的光滑性,自适应的估计地震数据的相干分量。基于动校正后的共中心点道集(CMP)中地震信号的相干性,利用非稳态多项式拟合估计有效信号,从而衰减随机噪声。对于线性相干噪声,如地滚波,首先利用径向道变换(RadialTraceTransform,RTT)将地震数据变换到时间一视速度域,在时间—视速度域利用非稳态多项式拟合估计出相干噪声,然后减去相干噪声。该方法可以有效的估计振幅变化的相干分量,不需要相干分量振幅为常量的假设。模拟和实际资料处理结果表明,与传统的稳态多项式拟合和低切滤波相比,该方法可以更为有效的衰减地震噪声,同时保真了地震有效信号。
We propose a novel method for seismic noise attenuation by applying nonstationary polynomial fitting (NPF), which can estimate coherent components with amplitude variation along the event. The NPF with time-varying coefficients can adaptively estimate the coherent components. The smoothness of the polynomial coefficients is controlled by shaping regularization. The signal is coherent along the offset axis in a common midpoint (CMP) gather after normal moveout (NMO). We use NPF to estimate the effective signal and thereby to attenuate the random noise. For radial events-like noise such as ground roll, we first employ a radial trace (RT) transform to transform the data to the time-velocity domain. Then the NPF is used to estimate coherent noise in the RT domain. Finally, the coherent noise is adaptively subtracted from the noisy dataset. The proposed method can effectively estimate coherent noise with amplitude variations along the event and there is no need to propose that noise amplitude is constant. Results of synthetic and field data examples show that, compared with conventional methods such as stationary polynomial fitting and low cut filters, the proposed method can effectively suppress seismic noise and preserve the signals.
We propose a novel method for seismic noise attenuation by applying nonstationary polynomial fitting (NPF), which can estimate coherent components with amplitude variation along the event. The NPF with time-varying coefficients can adaptively estimate the coherent components. The smoothness of the polynomial coefficients is controlled by shaping regularization. The signal is coherent along the offset axis in a common midpoint (CMP) gather after normal moveout (NMO). We use NPF to estimate the effective signal and thereby to attenuate the random noise. For radial events-like noise such as ground roll, we first employ a radial trace (RT) transform to transform the data to the time-velocity domain. Then the NPF is used to estimate coherent noise in the RT domain. Finally, the coherent noise is adaptively subtracted from the noisy dataset. The proposed method can effectively estimate coherent noise with amplitude variations along the event and there is no need to propose that noise amplitude is constant. Results of synthetic and field data examples show that, compared with conventional methods such as stationary polynomial fitting and low cut filters, the proposed method can effectively suppress seismic noise and preserve the signals.
引文
Bekara, M., and Baan, M., 2009, Random and coherentnoise attenuation by empirical mode decomposition:Geophysics, 74, V89 – V98.
Brown, M., and Claerbout, J., 2000, A pseudo-unitaryimplementation of the radial trace transform: 70th Ann.Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts,2115 – 2118.
Claerbout, J., 2009, Slant-stacks and radial traces: StanfordExpl. Project Report, SEP-5, 1 – 12.
Fomel, S., 2001, Three-dimensional seismic dataregularization: PhD dissertation, Stanford University.
Fomel, S., 2006, Towards the seislet transform: 76th Ann.Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts,2847 – 2851.
Fomel, S., 2007, Shaping regularization in geophysical-estimation problems: Geophysics, 72, R29 – R36.
Fomel, S., 2009, Adaptive multiple subtraction usingregularized nonstationary regression: Geophysics, 74,V25 – V33.
Fomel, S., and Liu, Y., 2010, Seislet transform and seisletframe: Geophysics, 75, V25-V38.
Guitton, A., 2002, Coherent noise attenuation using inverseproblems and prediction-error filters: First Break, 20, 161– 167.
Henley, D., 2003, Coherent noise attenuation in the radialtrace domain: Geophysics, 68, 1408 – 1416.
Karsli, H., Dondurur, D., and if i, G., 2006, Applicationof complex-trace analysis to seismic data for random-noise suppression and temporal resolution improvement:Geophysics, 71, V79 – V86.
Liu, G., Fomel, S., Jin, L., and Chen, X., 2009a, Stackingseismic data using local correlation: Geophysics, 74, V43– V48.
Liu, G., Fomel, S., and Chen, X., 2009b, Time-frequencycharacterization of seismic data using local attributes:79th Ann. Internat. Mtg., Soc. Expl. Geophys., ExpandedAbstracts, 1825 – 1829.
Liu, Y., Fomel, S., Liu, C., Wang, D., Liu, G., and Feng, X.,2009c, High-order seislet transform and its application ofrandom noise attenuation: Chinese Journal of Geophysics(in Chinese), 52, 2142 – 2151.
Liu, Z. P., Chen, X. H., and Li, J. Y., 2008, Study oncoherent noise attenuation using radial trace transformfiltering: Progress in Geophysics (in Chinese),23(4), 1199 – 1204.
Lu, W. K., Zhang, W., and Liu, D., 2006, Local linearcoherent noise attenuation based on local polynomialapproximation: Geophysics, 71, V163 – V169.
Lu, Y., and Lu, W., 2009, Edge-preserving polynomialfitting method to suppress random seismic noise:Geophysics, 74, V69 – V73.
Mayne, W. H., 1962, Common reflection point horizontaldata stacking techniques: Geophysics, 27, 927 – 938.
Neelamani, R., Baumstein, A. I., Gillard, D. G., Hadidi, M.T., and Soroka, W. I., 2008, Coherent and random noiseattenuation using the curvelet transform: The LeadingEdge, 27, 240 – 248.
Ristau, J. P., and Moon, W. M., 2001, Adaptive filtering of random noise in 2-D geophysical data: Geophysics, 66,342 – 349.
Tikhonov, A. N., 1963, Solution of incorrectly formulatedproblems and the regularization method: SovietMathematics–Doklady, 1035 – 1038.
Xue, Y., and Chen, X., 2009, Random noise attenuationbased on orthogonal polynomials transform: 79th Ann.Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts,3377 – 3380.
Ursin, B., and Dahl, T., 1990, Least-square estimation ofreflectivity polynomials: 60th Ann. Internat. Mtg., Soc.Expl. Geophys., Expanded Abstracts, 1069 – 1071.
Yilmaz, O., 2001, Seismic data analysis: Processing,inversion, and interpretation of seismic data: SEG.
Zhang, R., and Ulrych, T. J., 2003, Physical wavelet framedenoising: Geophysics, 68, 225 – 231.
Brown, M., and Claerbout, J., 2000, A pseudo-unitaryimplementation of the radial trace transform: 70th Ann.Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts,2115 – 2118.
Claerbout, J., 2009, Slant-stacks and radial traces: StanfordExpl. Project Report, SEP-5, 1 – 12.
Fomel, S., 2001, Three-dimensional seismic dataregularization: PhD dissertation, Stanford University.
Fomel, S., 2006, Towards the seislet transform: 76th Ann.Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts,2847 – 2851.
Fomel, S., 2007, Shaping regularization in geophysical-estimation problems: Geophysics, 72, R29 – R36.
Fomel, S., 2009, Adaptive multiple subtraction usingregularized nonstationary regression: Geophysics, 74,V25 – V33.
Fomel, S., and Liu, Y., 2010, Seislet transform and seisletframe: Geophysics, 75, V25-V38.
Guitton, A., 2002, Coherent noise attenuation using inverseproblems and prediction-error filters: First Break, 20, 161– 167.
Henley, D., 2003, Coherent noise attenuation in the radialtrace domain: Geophysics, 68, 1408 – 1416.
Karsli, H., Dondurur, D., and if i, G., 2006, Applicationof complex-trace analysis to seismic data for random-noise suppression and temporal resolution improvement:Geophysics, 71, V79 – V86.
Liu, G., Fomel, S., Jin, L., and Chen, X., 2009a, Stackingseismic data using local correlation: Geophysics, 74, V43– V48.
Liu, G., Fomel, S., and Chen, X., 2009b, Time-frequencycharacterization of seismic data using local attributes:79th Ann. Internat. Mtg., Soc. Expl. Geophys., ExpandedAbstracts, 1825 – 1829.
Liu, Y., Fomel, S., Liu, C., Wang, D., Liu, G., and Feng, X.,2009c, High-order seislet transform and its application ofrandom noise attenuation: Chinese Journal of Geophysics(in Chinese), 52, 2142 – 2151.
Liu, Z. P., Chen, X. H., and Li, J. Y., 2008, Study oncoherent noise attenuation using radial trace transformfiltering: Progress in Geophysics (in Chinese),23(4), 1199 – 1204.
Lu, W. K., Zhang, W., and Liu, D., 2006, Local linearcoherent noise attenuation based on local polynomialapproximation: Geophysics, 71, V163 – V169.
Lu, Y., and Lu, W., 2009, Edge-preserving polynomialfitting method to suppress random seismic noise:Geophysics, 74, V69 – V73.
Mayne, W. H., 1962, Common reflection point horizontaldata stacking techniques: Geophysics, 27, 927 – 938.
Neelamani, R., Baumstein, A. I., Gillard, D. G., Hadidi, M.T., and Soroka, W. I., 2008, Coherent and random noiseattenuation using the curvelet transform: The LeadingEdge, 27, 240 – 248.
Ristau, J. P., and Moon, W. M., 2001, Adaptive filtering of random noise in 2-D geophysical data: Geophysics, 66,342 – 349.
Tikhonov, A. N., 1963, Solution of incorrectly formulatedproblems and the regularization method: SovietMathematics–Doklady, 1035 – 1038.
Xue, Y., and Chen, X., 2009, Random noise attenuationbased on orthogonal polynomials transform: 79th Ann.Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts,3377 – 3380.
Ursin, B., and Dahl, T., 1990, Least-square estimation ofreflectivity polynomials: 60th Ann. Internat. Mtg., Soc.Expl. Geophys., Expanded Abstracts, 1069 – 1071.
Yilmaz, O., 2001, Seismic data analysis: Processing,inversion, and interpretation of seismic data: SEG.
Zhang, R., and Ulrych, T. J., 2003, Physical wavelet framedenoising: Geophysics, 68, 225 – 231.
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