Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (8): 24-31.doi: 10.11896/jsjkx.200900034

• Database & Big Data & Data Science • Previous Articles     Next Articles

Seismic Data Super-resolution Method Based on Residual Attention Network

ZHOU Wen-hui1,2, SHI Min3, ZHU Deng-ming1, ZHOU Jun4   

  1. 1 Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China;
    2 University of Chinese Academy of Sciences,Beijing 101408,China;
    3 College of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China;
    4 China National Petroleum Logging Co.,Ltd.Technology Center,Xi'an 710065,China
  • Received:2020-09-04 Revised:2020-11-17 Published:2021-08-10
  • About author:ZHOU Wen-hui,born in 1995,master.His main research interests include computer vision and computer gra-phics.(765647930@qq.com)ZHU Deng-ming,born in 1973,Ph.D,associate researcher,master supervisor,is a member of China Computer Federation.His main research interests include virtual reality and human-computer interaction.
  • Supported by:
    National Major Science and Technology Project(2017ZX05019005).

PDF (PC)

1481

Abstract: Seismic data plays a vital role in oil and gas exploration and geological exploration.Accurate and detailed seismic data can help to provide accurate guidance for oil and gas exploration,reduce the risk of exploration,and generate huge social and economic benefits.In terms of improving the resolution of seismic data,the existing methods are difficult to recover detailed geolo-gical information when facing large amounts of data,and have poor results in high-resolution recovery,denoising performance and efficiency,and it is difficult to meet the actual needs.Seismic data can reflect the composition of geological structures and strata,and have the characteristics of high local correlation and low global correlation.At the same time,the high frequency part of seismic data usually contain important geological exploration information such as layering,chasm,etc.In view of the characteristics of seismic data,this paper innovatively transforms the problem of seismic data reconstruction into the problem of image super-resolution,and proposes a method for super-resolution of seismic data based on the generative adversarial networks.In view of the characteristics of high local correlation and low global correlation of seismic data distribution,a residual attention module is designed to mine the inherent correlation of seismic data,so as to recover more refined seismic data.By training a generative adversarial network model with a relative generative adversarial loss function,the generative network is used to perform super-resolution recovery of the seismic data to recover more refined seismic data.In this paper,experimental verification is carried out on real seismic data,and the experimental results show that the proposed method has a good effect on super-resolution of seismic data and has strong practicability.

Key words: Relative generative adversarial loss, Generative adversarial network, Residual attention module, Seismic data, Super resolution

CLC Number: 

  • TP391.41
[1]JIAO H C.Research on seismic phase division method in 3Dseismic data[D].Chengdu:University of Electronic Science and Technology of China,2019.
[2]ZHAO N,YAN X P,HAO C L.Application of comprehensivegeological exploration methods in geological exploration[J].World Nonferrous Metals,2020(3):233,235.
[3]CHEN S X.Analysis on the development of petroleum geolo-gical seismic exploration technology[J].Petrochemical Technology,2019,26(3):193.
[4]SHANG J K.Application of three-dimensional seismic exploration technology under complex geological conditions[J].Jiangxi Coal Science and Technology,2020(3):143-146.
[5]ZHAN Z D,XU D P,WU H Y,et al.In-depth analysis of the accuracy of SEGY format seismic data conversion[J].Physical and Geochemical Exploration,2011,35(6):851-854.
[6]XIONG Z.Seismic exploration methods and techniques for stratigraphic and lithologic reservoirs[J].Petroleum Geophysical Prospecting,2012,47(1):1-18.
[7]REILLY D L,COOPER L N.An Overview of Neural Net-works:Early Models to Real World Systems[M].San Diego:Academic Press,2015:7-15.
[8]SPITZ S.Seismic trace interpolation in the F-X domain [J].Geophysics,1991,56(6):785.
[9]GULUNAY N,CHAMBERS R E.Unaliased fk domain trace interpolation[C]//SEG Technical Program Expanded Abstracts.Tulsa:Society of Exploration Geophysicists,1996:1461-1464.
[10]RONEN J.Wave-equation trace interpolation[J].Geophysics,2012,52(7):973-984.
[11]ZHAO Z Y,LI Z C,ZHANG M.Discrete orthogonal S-transform seismic data reconstruction using compressed sensing technology[J].Petroleum Geophysical Prospecting,2020,55(1):29-35.
[12]JIAO X M,DU Q Z,ZHAO Q.The compressive sensing seismic trace reconstruction method based on the maximum correlation entropy criterion[J].Journal of China University of Petroleum (Edition of Natural Science),2020,44(3):38-46.
[13]XU S,ZHANG Y,PHAM D,et al.Antileakage Fourier trans-form for seismic data regularizetion[J].Geophysics,2005,70(4):V87-V95.
[14]HUANG X W,WU L,NIU B H.Seismic trace reconstructionbased on parabolic Radon transformation[J].Journal of China University of Mining & Technology,2003(5):534-539.
[15]HERRMANN F J,HENNENFENT G.Non-parametric seismic data recovery with curvelet frames[J].Geophysical Journal of the Royal Astronomical Society,2010,173(1):233-248.
[16]LIU C,LI P,LIU Y,et al.Anti-aliasing iterative data interpolation method based on Seislet transform[J].Chinese Journal of Geophysics,2013,56(5):1619-1627.
[17]ZHANG L,HAN L G,XU D X,et al.Shearlet transform reconstruction of seismic data based on compressed sensing technology[J].Petroleum Geophysical Prospecting,2017,52(2):220-225.
[18]HUANG W,WU R S,WANG R.Damped dreamlet representation for exploration seismic data interpolation and denoising[J].IEEE Transactions on Geoscience & Remote Sensing,2018,56(6):3159-3172.
[19]DONG C,LOY C C,HE K,et al.Learning a deep convolutional network for image super-resolution[C]//IEEE International Conference on Computer Vision.2014.
[20]DONG C,LOY C C,HE K,et al.Image super-resolution using deep convolutional networks [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,38(2):295-307.
[21]KIM J,LEE J K,LEE K M.Accurate image super-resolutionusing very deep convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:1646-1654.
[22] LEDIG C,THEIS L,HUSZAR F,et al.Photo-Realistic SingleImage Super-Resolution Using a Generative Adversarial Network[C]//IEEE Transactions on Pattern Analysis and Machine Intelligence.2017.
[23]ROLAND P.Cortical regulation of selective attention in man.A regional cerebral blood flow study[J].Journal of Neurophy-siology,1982,48(5):1059.
[24]MORAN J,DE SIMONE R.Selective attention gates visual processing in the extrastriate cortex[J].Science,1985,229(4715):782-784.
[25]MNIH V,HEESS N,GRAVES A,et al.Recurrent Models of Visual Attention[C]//Conference and Workshop on Neural Information Processing Systems.2014.
[26]MALINOWSKI M,DOERSCH C,SANTORO A,et al.Learning visual question answering by bootstrapping hard attention[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:3-20.
[27]MCCLENNY L,BRAGA-NETO U.Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism[J].arXiv:2009.04544,2020.
[28]ZHANG B B,ZHANG Z J,FU J R,et al.Recovering low-frequency information using high-frequency components of seismic data[C]//2014 China Earth Sciences Joint Academic Annual Conference－Topic 18:Collected Papers on Reservoir Geophy-sics.2014.
[29]HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017.
[30]GOODFELLOW L,POUGET-ABADIE J,MIRZA M,et al.Generative adversary yal nets[C]//Conference and Workshop on Neural Information Processing Systems.2016.
[31]ZHAO Z,SUN Q,YANG H,et al.Compression artifacts reduction by improved generative adversarial networks[J].EURASIP Journal on Image and Video Processing,2019,2019(1).
[32]ZHAO Z S,GAO H X,SUN Q,et al.The theoretical framework of generative confrontation network,the latest development of derivative models and applications[J].Small Microcomputer System,2018,39(12):44-48.
[33]WANG X,YU K,WU S,et al.Esrgan:Enhanced super-resolution generative adversarial networks[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018.
[34]ZHANG Y,LI K P,LI K,et al.Image super-resolution using very deep residual channel attention networks[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:286-301.
[1] ZHANG Jia, DONG Shou-bin. Cross-domain Recommendation Based on Review Aspect-level User Preference Transfer [J]. Computer Science, 2022, 49(9): 41-47.
[2] SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[3] DAI Zhao-xia, LI Jin-xin, ZHANG Xiang-dong, XU Xu, MEI Lin, ZHANG Liang. Super-resolution Reconstruction of MRI Based on DNGAN [J]. Computer Science, 2022, 49(7): 113-119.
[4] XU Guo-ning, CHEN Yi-peng, CHEN Yi-ming, CHEN Jin-yin, WEN Hao. Data Debiasing Method Based on Constrained Optimized Generative Adversarial Networks [J]. Computer Science, 2022, 49(6A): 184-190.
[5] YIN Wen-bing, GAO Ge, ZENG Bang, WANG Xiao, CHEN Yi. Speech Enhancement Based on Time-Frequency Domain GAN [J]. Computer Science, 2022, 49(6): 187-192.
[6] XU Hui, KANG Jin-meng, ZHANG Jia-wan. Digital Mural Inpainting Method Based on Feature Perception [J]. Computer Science, 2022, 49(6): 217-223.
[7] DOU Zhi, WANG Ning, WANG Shi-jie, WANG Zhi-hui, LI Hao-jie. Sketch Colorization Method with Drawing Prior [J]. Computer Science, 2022, 49(4): 195-202.
[8] GAO Zhi-yu, WANG Tian-jing, WANG Yue, SHEN Hang, BAI Guang-wei. Traffic Prediction Method for 5G Network Based on Generative Adversarial Network [J]. Computer Science, 2022, 49(4): 321-328.
[9] LI Si-quan, WAN Yong-jing, JIANG Cui-ling. Multiple Fundamental Frequency Estimation Algorithm Based on Generative Adversarial Networks for Image Removal [J]. Computer Science, 2022, 49(3): 179-184.
[10] SHI Da, LU Tian-liang, DU Yan-hui, ZHANG Jian-ling, BAO Yu-xuan. Generation Model of Gender-forged Face Image Based on Improved CycleGAN [J]. Computer Science, 2022, 49(2): 31-39.
[11] LI Jian, GUO Yan-ming, YU Tian-yuan, WU Yu-lun, WANG Xiang-han, LAO Song-yang. Multi-target Category Adversarial Example Generating Algorithm Based on GAN [J]. Computer Science, 2022, 49(2): 83-91.
[12] TAN Xin-yue, HE Xiao-hai, WANG Zheng-yong, LUO Xiao-dong, QING Lin-bo. Text-to-Image Generation Technology Based on Transformer Cross Attention [J]. Computer Science, 2022, 49(2): 107-115.
[13] CHEN Gui-qiang, HE Jun. Study on Super-resolution Reconstruction Algorithm of Remote Sensing Images in Natural Scene [J]. Computer Science, 2022, 49(2): 116-122.
[14] ZHANG Wei-qi, TANG Yi-feng, LI Lin-yan, HU Fu-yuan. Image Stream From Paragraph Method Based on Scene Graph [J]. Computer Science, 2022, 49(1): 233-240.
[15] JIANG Zong-li, FAN Ke, ZHANG Jin-li. Generative Adversarial Network and Meta-path Based Heterogeneous Network Representation Learning [J]. Computer Science, 2022, 49(1): 133-139.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.