Random noise suppression of seismic data based on joint deep learning
ZHANG Yan1, LI Xinyue1, WANG Bin1, LI Jie1, DONG Hongli2
1. Institute of Computer and Information Techno-logy, Northeast Petroleum University, Daqing, Heilongjiang 163318, China; 2. Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, Heilongjiang 163318, China
Abstract：Random noise suppression is the key task of seismic data processing. To improve SNR (signal-to-noise ratio) and increase the efficiency and accuracy of following processing and interpretation, appropriate suppression methods should be used for noises induced by different mechanisms and with different characteristics. Applicable denoising methods based on deep learning usually focus on feature extraction in time or frequency domain, which result in over-smoothed or blurred textures in local zones. In addition, the kernel of a traditional convolution neural network is usually set to be a small and fixed block, which limits the size of the receptive field and reduces the diversity of the target characteristics extracted from seismic data. This paper proposes a method of random noise suppression based on joint deep learning. Firstly, features in both time domain and frequency domain are considered, and the joint error is used to define the loss function to improve effect of various extracted features. Secondly, by analyzing the influence of the kernel size and network depth on the size of the receptive field, the method of expanding convolution is used to extract more diverse features and reduce the loss of details of seismic data. Thirdly, according to the similarity between the input and output samples of the network, a residual learning strategy is introduced. Finally, the batch normalization (BN) algorithm is used to accelerate the convergence of the model and improve denoising efficiency. Compared with similar algorithms, the method proposed in this paper has a better effect on preserving the features of events and provides higher SNR.
张岩, 李新月, 王斌, 李杰, 董宏丽. 基于联合深度学习的地震数据随机噪声压制[J]. 石油地球物理勘探, 2021, 56(1): 9-25,56.
ZHANG Yan, LI Xinyue, WANG Bin, LI Jie, DONG Hongli. Random noise suppression of seismic data based on joint deep learning. Oil Geophysical Prospecting, 2021, 56(1): 9-25,56.
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]
关新平,赵立兴,唐英干.图像去噪混合滤波方法[J].中国图象图形学报,2005,10(3):332-337.GUAN Xinping,ZHAO Lixing,TANG Yinggan.Mixed filter for image denoising[J].Journal of Image and Graphics,2005,10(3):332-337.
[4]
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].Chinese Journal of Geophysics,2012,55(5):1732-1741.
[5]
Alsdorf D.Noise reduction in seismic data using Fourier correction coefficient filtering[J].Geophysics,1997,62(5):1617-1627.
[6]
Trad D,Ulrych T,Sacchi M.Latest view of sparse Radon transforms[J].Geophysics,2003,68(10):386-399.
[7]
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.
[8]
章珂,刘贵忠,邹大文,等.二进小波变换方法的地震信号分时分频去噪处理[J].地球物理学报,1996,39(2):265-271.ZHANG Ke,LIU Guizhong,ZOU Dawen,et al. Seismic data time-frequency domain denoising based on dyadic wavelet transform[J].Chinese Journal of Geophysics,1996,39(2):265-271.
[9]
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.
[10]
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.
[11]
Tang G,Ma J W,Yang H Z.Seismic data denoising based on learning-type overcomplete dictionaries[J].Applied Geophysics,2012,9(1):27-32.
[12]
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.
[13]
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.Multiattribute automatic interpretation of salt domes based on deep learning[J].Oil Geophysical Prospecting,2020,55(3):475-483.
[14]
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.
[15]
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].Chinese Journal of Geophysics,2019,62(1):421-433.
[16]
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.
[17]
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.
[18]
曹仰杰,贾丽丽,陈永霞,等.生成式对抗网络及其计算机视觉应用研究综述[J].中国图象图形学报,2018,23(10):1433-1449.CAO Yangjie,JIA Lili,CHEN Yongxia,et al. Review of computer vision based on generative adversarial networks[J].Journal of Image and Graphics,2018,23(10):1433-1449.
[19]
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.
[20]
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.
[21]
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.
[22]
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.
[23]
Yu S W,Ma J W,Wang W L.Deep learning for denoising[J].Geophysics,2019,84(6):V333-V350.
[24]
Wang F,Chen S C.Residual learning of deep convolutional neural network for seismic random noise attenuation[J].IEEE Geoscience and Remote Sensing,2019,16(8):1314-1318.
[25]
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.
[26]
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.
[27]
Liu P J,Zhang H Z,Lian W,et al.Multilevel wavelet convolutional neural networks[J].IEEE Access,2019,7:74973-74985.
[28]
Zhu W Q,Mousavi S M,Beroza G C.Seismic signal denoising and decomposition using deep neural networks[J].IEEE Transactions on Geoscience and Remote Sensing,2019,57(11):9476-9488.
[29]
李传朋,秦品乐,张晋京.基于深度卷积神经网络的图像去噪研究[J].计算机工程,2017,43(3):253-260.LI Chuanpeng,QIN Pinle,ZHANG Jinjing.Research on image denoising based on deep convolutional neural network[J].Computer Engineering,2017,43(3):253-260.
