基于变分模态分解参数优化的地震随机噪声去除方法
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摘要
为解决变分模态分解在地震数据去噪中依赖人工经验,模态分解和去噪效果具有一定随机性和偶然性的问题,提出基于频域奇异值分解信噪比估计的参数优化方法。该方法在参数范围内以较高的估计信噪比为评价参数对模态分量数目与有效模态进行选取,自适应寻找去噪最有效的参数,从而避免主观选取参数的随机性,改善去噪效果。仿真模型实验表明:估计信噪比与真实信噪比的误差为正相关关系,能够有效反映地震数据中噪声程度,所估计信噪比可以作为去噪效果的评价参数。通过仿真模型和实际地震数据对方法进行验证,结果表明基于估计信噪比参数优化后的变分模态分解方法能够有效压制噪声、凸显同相轴信息。
Due to the influence of artificial, environmental and geological conditions, seismic data is inevitably mixed with random noise, and noise must be effectively suppressed before subsequent processing and interpretation. Variational mode decomposition is one of the effective methods for seismic denoising, with good robustness and decomposition accuracy. However, the number of modal components decomposed and the recognition of effective modalities after decomposition mainly depend on manual experience selection, which leads to modal decomposition and denoising effects with a certain randomness and contingency. In order to solve this problem, this paper proposes a parameter optimization method based on singular value decomposition for signal-to-noise ratio(SNR) estimation. The modal component number and effective mode are selected by using the higher estimated SNR as the evaluation parameter in the parameter range. The method can be adapted to find the most effective parameters for denoising, thus avoiding the randomness of subjective selection parameters and improving the denoising effect. Simulation model experiments show that the estimated SNR is positively correlated with the true SNR error, which can effectively reflect the noise level in the seismic data. The estimated SNR ratio can be used as the evaluation parameter of the denoising effect. The simulation method and actual seismic data are used to verify the method, and the results of wavelet denoising and empirical mode decomposition are compared. The results show that the variational mode decomposition method based on the optimized SNR parameter can effectively suppress the noise and highlight the in-phase axis information.
Due to the influence of artificial, environmental and geological conditions, seismic data is inevitably mixed with random noise, and noise must be effectively suppressed before subsequent processing and interpretation. Variational mode decomposition is one of the effective methods for seismic denoising, with good robustness and decomposition accuracy. However, the number of modal components decomposed and the recognition of effective modalities after decomposition mainly depend on manual experience selection, which leads to modal decomposition and denoising effects with a certain randomness and contingency. In order to solve this problem, this paper proposes a parameter optimization method based on singular value decomposition for signal-to-noise ratio(SNR) estimation. The modal component number and effective mode are selected by using the higher estimated SNR as the evaluation parameter in the parameter range. The method can be adapted to find the most effective parameters for denoising, thus avoiding the randomness of subjective selection parameters and improving the denoising effect. Simulation model experiments show that the estimated SNR is positively correlated with the true SNR error, which can effectively reflect the noise level in the seismic data. The estimated SNR ratio can be used as the evaluation parameter of the denoising effect. The simulation method and actual seismic data are used to verify the method, and the results of wavelet denoising and empirical mode decomposition are compared. The results show that the variational mode decomposition method based on the optimized SNR parameter can effectively suppress the noise and highlight the in-phase axis information.
引文
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[17] 陶珂,朱建军.小波去噪质量评价方法的对比研究[J].大地测量与地球动力学,2012,32(2):128- 133.TAO K,ZHU J J.A comparative study on validity assessment of wavelet de-noising[J].Journal of Geodesy and Geodynamics,2012,32(2):128- 133.(in Chinese)
[2] LIU S C,GAO E G,XUN C.Seismic data denoising simulation research based on wavelet transform[J].Applied Mechanics and Materials ,2014,490/491:1356- 1360.
[3] CHEN Y K,LI X,ZHANG G Y,et al.Delineating karstification using synchros queezeing wavelet transform[C]//SEG Annual Meeting.New Orleans,2015:1835- 1840.
[4] 陈晓玉.基于小波变换的地震信号去噪研究[J].科技经济导刊,2017(35):56.CHEN X Y.Research on denoising of seismic signal based on wavelet transform[J].Technology and Economic Guide,2017(35):56.(in Chinese)
[5] 崔少华,单巍.基于小波分析的地震资料去噪方法的研究和应用[J].淮北师范大学学报(自然科学版),2016,37(3):43- 46.CUI S H,SHAN W.Research and application of seismic data denoise method based on wavelet analysis[J].Journal of Huaibei Normal University(Natural Science),2016,37(3):43- 46.(in Chinese)
[6] 崔少华,方振国,王江涛,等.基于小波变换的地震数据去噪的研究[J].曲阜师范大学学报,2018,44(3):54- 58.CUI S H,FANG Z G,WANG J T,et al.Research on seismic data denoising based on wavelet transform[J].Journal of Qufu Normal University,2018,44(3):54- 58.(in Chinese)
[7] 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.QIN X,CAI J C,LIU S Y,et al.Microseismic data denoising method based on EMD mutual information entropy and synchrosqueezing transform[J].Geophysical Prospecting for Petroleum,2017,56(5):658- 666.(in Chinese)
[8] 黄翔.基于EMD重构地震信号的去噪方法[J].油气地球物理,2017,15(2):18- 23.HUANG X.Denoising of the seismic signal reconstrucyion based on EMD[J].Petroleum Geophysics,2017,15(2):18- 23.(in Chinese)
[9] 卢秋悦.改进EMD在地震勘探随机噪声压制中的应用[D].长春:吉林大学,2016.LU Q Y.The application of improved EMD in seismic random noise suppression[D].Changchun:Jilin University,2016.(in Chinese)
[10] 温志平.基于模态分解技术的地震信号随机噪声压制[D].上海:华东理工大学,2018.WEN Z P.Seismic random noise attenuation based on modal decomposition technique[D].Shanghai:East China University of Technology,2018.(in Chinese)
[11] 何元,曹思远,崔震,等.变分模态分解及其在地震去噪中的应用[C]// 中国地球科学联合学术年会.北京,2014.HE Y,CAO S Y,CUI Z,et al.Variational mode decomposition and its application in seismic denoising[C]// Annual Meeting of Chinese Geoscience Union.Beijing,2014.(in Chinese)
[12] 康佳星,胡英,陈辉,等.VMD与EMD在地震信号时频分析中的对比研究[C]//中国地球科学联合学术年会.北京,2016.KANG J X,HU Y,CHEN H,et al.Comparative study of VMD and EMD in time-frequency of seismic signals[C]//Annual Meeting of Chinese Geoscience Union.Beijing,2016.(in Chinese)
[13] KONSTANTIN D,DOMINIQUE Z.Variation mode decomposition[J].IEEE Transactions on Signal Processing,2014,62(3):531- 544.
[14] 唐贵基,王晓龙.变分模态分解方法及其在滚动轴承早期故障诊断中的应用[J].振动工程学报,2016,29(4):638- 648.TANG G J,WANG X L.Variational mode decomposition method and its application in early fault diagnosis of rolling bearings[J].Journal of Vibration Engineering,2016,29(4):638- 648.(in Chinese)
[15] FLANDRIN P,GONCALVES P.Empirical mode decompositions as data-driven wavelet-like expansions[J].International Journal of Wavelet,Multiresolution and Information Processing,2004,2(4):477- 496.
[16] 刘洋,李承楚.地震资料估计信噪比的几种方法[J].石油地球物理勘探,1997,32(2):257- 262.LIU Y,LI C C.Several methods for estimating signal/noise ratio of seismic data[J].Oil Geophysical Prospecting,1997,32(2):257- 262.(in Chinese)
[17] 陶珂,朱建军.小波去噪质量评价方法的对比研究[J].大地测量与地球动力学,2012,32(2):128- 133.TAO K,ZHU J J.A comparative study on validity assessment of wavelet de-noising[J].Journal of Geodesy and Geodynamics,2012,32(2):128- 133.(in Chinese)
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