Robust seismic data denoising based on deep learning
ZHANG Yan1, LI Xinyue1, WANG Bin1, LI Jie1, WANG Hongtao2, DONG Hongli3
1. School of Computer&Information Technology, Northeast Petroleum University, Daqing, Heilongjiang 163318, China; 2. Daqing Information Technology Research Center, Daqing, Heilongjiang 163318, China; 3. Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, Heilongjiang 163318, China
Abstract：Noise in seismic data is complicated, and the traditional modeling methods based on prior knowledge cannot describe the noise distribution accurately. In the denoising methods based on deep learning, a multi-layer convolutional neural network is employed to automatically extract the deep features of seismic data, and its nonlinear approximation ability is used for adaptive learning, which yields a complex denoising model and thus brings a new idea for the denoising of seismic data. How-ever, poor generalization ability is found in the cur-rent denoising methods based on deep learning in the case of insufficient sample coverage, greatly reducing the denoising effect. Therefore, this paper proposes a robust deep learning algorithm for denoising. The model is composed of two sub-networks, which realize the estimation of noise distribution and noise suppression of noisy seismic data respectively. The sub-network for estimating noise distribution is a multi-layer convolutional neural network. The sub-network for denoising introduces a strategy of feature fusion, which comprehensively considers the global and local information of seismic data, and a residual learning strategy is utilized to extract noise features. L1 norm loss is taken as the loss function for the two sub-networks to enhance the generalization ability of the model. Experiments show that the method proposed in this paper has a higher generalization ability than similar algorithms. Data processing results indicate that it better preserves event features and has a higher signal-to-noise ratio.
张岩, 李新月, 王斌, 李杰, 王洪涛, 董宏丽. 基于深度学习的鲁棒地震数据去噪[J]. 石油地球物理勘探, 2022, 57(1): 12-25.
ZHANG Yan, LI Xinyue, WANG Bin, LI Jie, WANG Hongtao, DONG Hongli. Robust seismic data denoising based on deep learning. Oil Geophysical Prospecting, 2022, 57(1): 12-25.
张军华,吕宁,田连玉,等.地震资料去噪方法综合评述[J].石油地球物理勘探,2005,40(增刊1):121-127.ZHANG Junhua,LYU Ning,TIAN Lianyu,et al.A comprehensive review of seismic data denoising me-thods[J].Oil Geophysical Prospecting,2005,40(S1):121-127.
[2]
胡天跃.地震资料叠前去噪技术的现状与未来[J].地球物理学进展,2002,17(2):218-223.HU Tianyue.The current situation and future of seismic data prestack noise attenuation techniques[J].Progress in Geophysics,2002,17(2):218-223.
[3]
唐金良,曹辉,王立华,等.中值滤波在井间地震资料处理中的应用[J].石油物探,2005,44(1):47-50.TANG Jinliang,CAO Hui,WANG Lihua,et al.Median filter in seismic crosshole data processing[J].Geophysical Prospecting for Petroleum,2005, 44(1):47-50.
[4]
刘财,王典,刘洋,等.二维多级中值滤波技术在随机噪声消除中的应用初探[J].石油地球物理勘探,2005,40(2):163-167.LIU Cai,WANG Dian,LIU Yang,et al.Preliminary study of using 2D multi-level median filtering technique to eliminate random noises[J].Oil Geophysical Prospecting,2005,40(2):163-167.
[5]
王伟,高静怀,陈文超,等.基于结构自适应中值滤波器的随机噪声衰减方法[J].地球物理学报,2012,55(5):1732-1741.WANG Wei,GAO Jinghuai,CHEN Wenchao,et al.Random seismic noise suppression via structure-adaptive median filter[J].ChineseJournal of Geophysics,2012,55(5):1732-1741.
[6]
BUADES A,COLL B,MOREL J M.A non-local algorithm for image denoising[C].IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005,60-65.
[7]
BONAR D,SACCHI M.Denoising seismic data using the nonlocal means algorithm[J].Geophysics,2012,77(1):A5-A8.
[8]
ALSDORF D.Noise reduction in seismic data using Fourier correction coefficient filtering[J].Geophy-sics,1997,62(5):1617-1627.
[9]
TRAD D,ULRYCH T,SACCHI M.Latest view of sparse Radon transforms[J].Geophysics,2003,68(10):386-399.
[10]
CAO S,CHEN X.The second-generation wavelet transform and its application in denoising of seismic data[J].Applied Geophysics,2005,2(2):70-74.
[11]
高静怀,毛剑,满蔚仕,等.叠前地震资料噪声衰减的小波域方法研究[J].地球物理学报,2006,49(4):1155-1163.GAO Jinghuai,MAO Jian,MAN Weishi,et al.On the denoising method of prestack seismic data in wavelet domain[J].Chinese Journal of Geophysics,2006,49(4):1155-1163.
[12]
NEELAMANI R,BAUMSTEIN A I,GILARD D,et al.Coherent and random noise attenuation using the curvelet transform[J].The Leading Edge,2008,27(2):240-248.
[13]
ELAD M,AHARON M.Image denoising via sparse and redundant representations over learned dictionaries[J].IEEE Transactions on Image Processing,2006,15(12):3736-3745.
[14]
张岩,任伟建,唐国维.应用结构聚类字典学习压制地震数据随机噪声[J].石油地球物理勘探,2018,53(6):1119-1127.ZHANG Yan,REN Weijian,TANG Guowei.Random noise suppression on seismic data based on structured-clustering dictionary learning[J].Oil Geophysical Prospecting,2018,53(6):1119-1127.
[15]
ZHOU Y T, SHI C J, CHEN H M, et al.Spike-like blending noise attenuation using structural low-rank decomposition[J].IEEE Geoscience and Remote Sensing Letters, 2017,14(9):1633-1637.
[16]
ZU S H,ZHOU H,WU R S,et al.Hybrid-sparsity constrained dictionary learning for iterative deblen-ding of extremely noisy simultaneous-source data[J].IEEE Transactions on Geoscience and Remote Sen-sing,2019,57(4):2249-2262.
[17]
DABOV K,FOI A,KATKOVNIK V,et al.Image denoising by sparse 3-D transform-domain collaborative filtering[J].IEEE Transactions on Image Processing,2007,16(8):2080-2095.
[18]
ANVARI R,KAHOO A R,MOHAMMADI M,et al.Seismic random noise attenuation using sparse low-rank estimation of the signal in the time-frequency domain[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2019,12(5):1612-1618.
[19]
ZHOU Q B,GAO J H,WANG Z G,et al.Adaptive variable time fractional anisotropic diffusion filtering for seismic data noise attenuation[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(4):1905-1917.
[20]
ZHANG H,YANG H,LI H X,et al.Random noise attenuation of non-uniformly sampled 3D seismic data along two spatial coordinates using non-equispaced curvelet transform[J].Journal of Applied Geophy-sics,2018,151:221-233.
[21]
张玉玺,刘洋,张浩然,等.基于深度学习的多属性盐丘自动识别方法[J].石油地球物理勘探,2020,55(3):475-483.ZHANG Yuxi,LIU Yang,ZHANG Haoran,et al. Multi-attribute automatic interpretation of salt domes based on deep learning[J].Oil Geophysical Prospecting,2020,55(3):475-483.
[22]
王钰清,陆文凯,刘金林,等.基于数据增广和CNN的地震随机噪声压制[J].地球物理学报,2019,62(1):421-433.WANG Yuqing,LU Wenkai,LIU Jinlin,et al.Random seismic noise attenuation based on data augmentation and CNN[J].ChineseJournal of Geophysics,2019,62(1):421-433.
[23]
李海山,陈德武,吴杰,等.叠前随机噪声深度残差网络压制方法[J].石油地球物理勘探,2020,55(3):493-503.LI Haishan,CHEN Dewu,WU Jie,et al.Pre-stack random noise suppression with deep residual network[J].Oil Geophysical Prospecting,2020,55(3):493-503.
[24]
ZHANG M,LIU Y,CHEN Y K.Unsupervised seismic random noise attenuation based on deep convolutional neural network[J].IEEE Access,2019,7:179810-179822.
[25]
CHEN Y K,ZHANG M,BAI M,et al.Improving the signal-to-noise ratio of seismological datasets by unsupervised machine learning[J].Seismological Research Letters,2019,90(4):1552-1564.
[26]
罗仁泽,李阳阳.一种基于RUnet卷积神经网络的地震资料随机噪声压制方法[J].石油物探,2020,59(1):51-59.LUO Renze,LI Yangyang.Random seismic noise attenuation based on RUnet convolutional neural network[J].Geophysical Prospecting for Petroleum,2020,59(1):51-59.
[27]
ZHANG K,ZUO W M,CHEN Y J,et al.Beyond a Gaussian denoiser:Residual learning of deep CNN for image denoising[J].IEEE Transactions on Image Processing,2017, 26(7):3142-3155.
[28]
SHAN C H,GUO X R,QU J.Residual learning of deep convolutional neural networks for image denoi-sing[J].Intelligent & Fuzzy Systems,2019,37(2):2809-2818.
[29]
WU K L,ZHANG C H.Deep generative adversarial networks for the sparse signal denoising[C].International Conference on Pattern Recognition,2018,1127-1132.
[30]
韩卫雪,周亚同,池越.基于深度学习卷积神经网络的地震数据随机噪声去除[J].石油物探,2018,57(6):862-869.HAN Weixue,ZHOU Yatong,CHI Yue.Deep lear-ning convolutional neural networks for random noise attenuation in seismic data[J].Geophysical Prospecting for Petroleum,2018,57(6):862-869.
[31]
WANG F,CHEN S C.Residual learning of deep convolutional neural network for seismic random noise attenuation[J].IEEE Geoscience and Remote Sen-sing,2019,16(8):1314-1318.
[32]
YU S W,MA J W,WANG W L.Deep learning for denoising[J].Geophysics,2019,84(6):V333-V350.
[33]
ZHAO Y X,LI Y,DONG X T,et al.Low-frequency noise suppression method based on improved DnCNN in desert seismic data[J].IEEE Geoscience and Remote Sensing,2019,16(5):811-815.
[34]
DONG X T,LI Y,YANG B J.Desert low-frequency noise suppression by using adaptive DnCNNs based on the determination of high-order statistic[J].Geophysical Journal International,2019,219(2):1281-1299.
[35]
YANG L Q,CHEN W,LIU W,et al.Random noise attenuation based on residual convolutional neural network in seismic datasets[J].IEEE Access,2020,8:30271-30286.
[36]
GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Generative adversarial nets[C].Advances in Neural Information Processing Systems,2014,2672-2680.
[37]
RADFORD A,METZ L,CHINTALA S.Unsupervised representation learning with deep convolutional generative adversarial networks[C].International Conference on Learning Representations,2016,1-16.
[38]
俞若水,张勇,周创.基于深度卷积生成对抗网络的瑞雷波信号随机噪声去除[J].地球物理学进展,2020,35(6):2276-2283.YU Ruoshui,ZHANG Yong,ZHOU Chuang.Deep convolutional generative adversarial network for random noise attenuation in Rayleigh wave signal[J].Progress in Geophysics,2020,35(6):2276-2283.
[39]
MOHAN S, KADKHODAIE Z, SIMONCELLI E P,et al.Robust and interpretable blind image denoising via bias-free convolutional neural networks[C].International Conference on Learning Representations,2020,1-22.
[40]
ZHANG K,ZUO W M,ZHANG L.FFDNet:Toward a fast and flexible solution for CNN-based image denoising[J].IEEE Transactions on Image Processing,2018,27(9):4608-4622.
[41]
GUO S,YAN Z F,ZHANG K,et al.Toward convolutional blind denoising of real photographs[C].Confe-rence on Computer Vision and Pattern Recognition,2019,1712-1722.
[42]
XU J,ZHANG L,ZHANG D,et al.Multi-channel weighted nuclear norm minimization for real color image denoising[C].IEEE Internationa Conference on Computer Vision(ICCV),2017,1105-1113.
