Research on random noise attenuation method for seismic data from deserts based on DBBCNN
ZHONG Tie1,2, CHEN Yun2, DONG Xintong3, LI Yue4, YANG Baojun5
1. Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technolo-gy, Ministry of Education, Jilin, Jilin 132012, China; 2. Department of Communication Engineering, Northeast Electric Power University, Jilin, Jilin 132012, China; 3. College of Instrumentation and Electrical Engineering, Jilin University, Changchun, Jilin 130026, China; 4. College of Communication Engineering, Jilin University, Changchun, Jilin 130012, China; 5. College of Geo-exploration Science and Technolo-gy, Jilin University, Changchun, Jilin 130026, China
Abstract:In the desert area of the Tarim Basin, the collected exploration data generally has a low signal-to-noise ratio (SNR) due to the harsh collection environment and complex surface geological conditions. In addition, spectrum aliasing exists between random noise and effective signals, and noise suppression is challenging, which have negative impacts on subsequent procedures such as inversion, imaging, and interpretations. In recent years, deep learning denoising methods, represented by feed-forward denoising convolutional neural networks (DnCNNs), have been employed to suppress complex random noise. However, traditional denoising networks generally extract data features on the basis of single-scale information, which results in the degeneration of denoising capability when confronting complex exploration data. To achieve effective attenuation of complex noise from deserts, this paper proposed a new denoising network, namely the diverse branch block convolutional neural network (DBBCNN). Unlike traditional networks, DBBCNN combines the branches in different scales and complexity to enrich the feature space. Then, the long-path operation fuses global and local features to improve the feature expression ability of the network for weak signals. Both simulations and field experimental results show that the proposed method can effectively suppress the complex random noise from deserts with a significant increment of SNR.
钟铁, 陈云, 董新桐, 李月, 杨宝俊. 基于DBBCNN的沙漠区地震资料随机噪声衰减方法[J]. 石油地球物理勘探, 2022, 57(2): 268-278.
ZHONG Tie, CHEN Yun, DONG Xintong, LI Yue, YANG Baojun. Research on random noise attenuation method for seismic data from deserts based on DBBCNN. Oil Geophysical Prospecting, 2022, 57(2): 268-278.
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.
[2]
李光辉,李月.沙漠地区地震勘探随机噪声建模及其在噪声压制中的应用[J].地球物理学报,2016,59(2):682-692.LI Guanghui,LI Yue.Random noise of seismic exploration in desert modeling and its applying in noise attenuation[J].Chinese Journal of Geophysics,2016,59(2):682-692.
[3]
林红波,张丹丹.自适应结构方向复扩散压制沙漠地震随机噪声[J].吉林大学学报(信息科学版),2019,37(5):463-469.LIN Hongbo,ZHANG Dandan.Adaptive structure-oriented complex diffusion filtering for desert seismic random noise attenuation[J].Journal of Jilin University (Information Science Edition),2020,37(5):463-469.
[4]
HARRIS P E,WHITE R E.Improving the perfor-mance of f-x prediction filtering at low signal-to-noise ratios[J].Geophysical Prospecting,1997,45(2):269-302.
[5]
MENDEL J.White-noise estimators for seismic data processing in oil exploration[J].IEEE Transactions on Automatic Control,1977,22(5):694-706.
[6]
CANALES L L.Random noise reduction[C].SEG Te-chnical Program Expanded Abstracts,1984:525-527.
[7]
AMEZQUITA SANCHEZ J P,CHAVEZ ALEGRIA O,VALTIERRA RODRIGUEZ M,et al.Detection of ULF geomagnetic anomalies associated to seismic activity using EMD method and fractal dimension theory[J].IEEE Latin America Transactions,2017,15(2):197-205.
[8]
TIAN Y A,LI Y,YANG B J.Variable-eccentricity hyperbolic-trace TFPF for seismic random noise atte-nuation[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(10):6449-6458.
[9]
MA H T,QIAN Z B,LI Y,et al.Noise reduction for desert seismic data using spectral kurtosis adaptive bandpass filter[J].ActaGeophysica,2019,67(1):123-131.
[10]
XIONG M J,LI Y,WU N.Random-noise attenuation for seismic data by local parallel radial-trace TFPF[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(7):4025-4031.
[11]
MOUSAVI S M,LANGSTON C A,HORTON S P.Automatic microseismic denoising and onset detection using the synchrosqueezed continuous wavelet transform[J].Geophysics,2016,81(4):V341-V355.
[12]
YU Z,ABMA R,ETGEN J,et al.Attenuation of noise and simultaneous source interference using wavelet denoising[J].Geophysics,2017,82(3):V179-V190.
[13]
SHAN H,MA J W,YANG H Z.Comparisons of wavelets,contourlets and curvelets in seismic denoising[J].Journal of Applied Geophysics,2009,69(2):103-115.
[14]
ZHAO X,LI Y,ZHUANG G H,et al.2-D TFPF based on Contourlet transform for seismic random noise attenuation[J].Journal of Applied Geophysics,2016,129:158-166.
[15]
董新桐,马海涛,李月.丘陵地带地震资料随机噪声压制新技术:高阶加权阈值函数的Shearlet变换[J].地球物理学报,2019,62(10):4039-4046.DONG Xintong,MA Haitao,LI Yue.The new technology for suppression of hilly land seismic random noise:Shearlet transform and the high order weighted threshold function[J].Chinese Journal of Geophysics,2019,62(10):4039-4046.
[16]
ZHANG C,VAN DER BAAN M.Multicomponent microseismic data denoising by 3D shearlet transform[J].Geophysics,2018,83(3):A45-A51.
[17]
DONG X T,JIANG H,ZHENG S,et al.Signal-to-noise ratio enhancement for 3C downhole microseismic data based on the 3D shearlet transform and improved back-propagation neural networks[J].Geophysics,2019,84(4):V245-V254.
[18]
张建明,詹智财,成科扬,等.深度学习的研究与发展[J].江苏大学学报(自然科学版),2015,36(2):191-200.ZHANG Jianming,ZHAN Zhicai,CHENG Keyang,et al.Review on development of deep learning[J].Journal of Jiangsu University (Natural Science Edition),2015,36(2):191-200.
[19]
张岩,李新月,王斌,等.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,et al.Random noise suppression of seismic data based on joint deep learning[J].Oil Geophysical Prospecting,2021,56(1):9-25,56.
[20]
宋辉,高洋,陈伟,等.基于卷积降噪自编码器的地震数据去噪[J].石油地球物理勘探,2020,55(6):1210-1219.SONG Hui,GAO Yang,CHEN Wei,et al.Seismic noise suppression based on convolutional denoising autoencoders[J].Oil Geophysical Prospecting,2020,55(6):1210-1219.
[21]
曲之琳,胡晓飞.基于改进激活函数的卷积神经网络研究[J].计算机技术与发展,2017,27(12):77-80.QU Zhilin,HU Xiaofei.Research on convolutional neural network based on improved activation function[J].Computer Technology and Development,2017,27(12):77-80.
[22]
孙俊,何小飞,谭文军,等.空洞卷积结合全局池化的卷积神经网络识别作物幼苗与杂草[J].农业工程学报,2018,34(11):159-165.SUN Jun,HE Xiaofei,TAN Wenjun,et al.Recognition of crop seedling and weed recognition based on dilated convolution and global pooling in CNN[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(11):159-165.
[23]
张利刚.基于全空洞卷积神经网络的图像语义分割[D].吉林长春:东北师范大学,2018.ZHANG Ligang.Fully Dilated Convolutional Networks for Semantic Segmentation[D].Northeast Normal University,Changchun,Jilin,2018.
[24]
LIU Z M,GAO G Y,SUN L,et al.IPG-Net:image pyramid guidance network for small object detection[C].2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW),Seattle,WA,USA,2020,4422-4430.
[25]
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.
[26]
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.
[27]
王琪琪,汤井田,张良,等.利用多层感知机的地震数据去噪[J].石油地球物理勘探,2020,55(2):272-281.WANG Qiqi,TANG Jingtian,ZHANG Liang,et al.Seismic data denoising based on multi-layer perceptron[J].Oil Geophysical Prospecting,2020,55(2):272-281.
LU Tianzhu,QIAN Xiaochao,HE Shu,et al.A time series prediction method based on deep learning[J].Control and Decision,2021,36(3):645-652.
[18]
SHELHAMER E,LONG J,DARRELL T.Fully convolutional networks for semantic segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(4):640-651.
[19]
YU F,KOLTUN V.Multi-scale context aggregation by dilated convolutions[DB/OL].(2015-11-23).https://arxiv.org/abs/1511.07122.
[20]
VAN DEN OORD A,DIELEMAN S,ZEN H,et al.WaveNet:a generative model for raw audio[DB/OL].(2016-09-12).https://arxiv.org/abs/1609.03499v2.
[21]
HE K M,ZHANG X Y,REN S Q,et al.Deep residual learning for image recognition[C].2016 IEEE Con-ference on Computer Vision and Pattern Recognition (CVPR),Las Vegas,NV,USA,2016:770-778.
峡山."历史-因果论大地构造学"简介[J].中国地质,1992,28(12):12-28.XIA Shan.A brief introduction to "history causality geotectonics"[J].Geology in China,1992,28(12):12-28.
[24]
吴张帆,和钟铧,姜炜,等.大港油田板南深层区块沙三段断块岩性油气藏成藏特征[J].世界地质,2019,38(1):184-193.WU Zhangfan,HE Zhonghua,JIANG Wei,et al.Characteristics of fault block-lithologic reservoir in third member of Shahejie formation in deep South-Banqiao sag of Dagang oilfield[J].Global Geology,2019,38(1):184-193.
[25]
郭金凤,李洪香,苏俊青,等.滨海断鼻沙三段油气成藏特征[J].天然气地球科学,2010,21(4):554-558.GUO Jinfeng,LI Hongxiang,SU Junqing,et al.Petroleum formation and accumulation in the third member of Shahejie formation in Binhai nose-shaped high structure[J].Natural Gas Geoscience,2010,21(4):554-558.
[26]
郑亚斌,王延斌,邓宝荣,等.港172断块储层特征研究[J].特种油气藏,2007,14(4):26-28,35.ZHENG Yabin,WANG Yanbin,DENG Baorong,et al.Reservoir characteristic study of block Gang172[J].Special Oil&Gas Reservoirs,2007,14(4):26-28,35.