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Poststack Seismic Data Denoising Based on 3-D Convolutional Neural Network

IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society, v.58 no.3, 2020년, pp.1598 - 1629  

Liu, Dawei (Xi’an Jiaotong University, School of Information and Communication Engineering, Xi’an, China) ,  Wang, Wei (InvestBrain, Shanghai, China) ,  Wang, Xiaokai (Xi’an Jiaotong University, School of Information and Communication Engineering, Xi’an, China) ,  Wang, Cheng (Daqing Oilfield Company, Ltd., Exploration and Development Research Institute, Daqing, China) ,  Pei, Jiangyun (Daqing Oilfield Company, Ltd., Exploration and Development Research Institute, Daqing, China) ,  Chen, Wenchao (Daqing Oilfield Company, Ltd., Exploration and Development Research Institute, Daqing, China)

Abstract AI-Helper 아이콘AI-Helper

Deep learning has been successfully applied to image denoising. In this study, we take one step forward by using deep learning to suppress random noise in poststack seismic data from the aspects of network architecture and training samples. On the one hand, poststack seismic data denoising mainly ai...

참고문헌 (75)

  1. Ogden, R. T., Nievergelt, Yves. Wavelets Made Easy. Journal of the American Statistical Association, vol.95, no.451, 1007-.

  2. Proc 32nd Int Conf Mach Learn Batch normalization: Accelerating deep network training by reducing internal covariate shift ioffe 2015 448 

  3. Proc Adv Neural Inf Process Syst Imagenet classification with deep convolutional neural networks krizhevsky 2012 1106 

  4. arXiv 1505 02000 Deep learning for medical image segmentation lai 2015 

  5. 10.1109/ICCV.2015.123 

  6. 10.1109/CVPR.2016.90 

  7. Jiang, Dongsheng, Dou, Weiqiang, Vosters, Luc, Xu, Xiayu, Sun, Yue, Tan, Tao. Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network. Japanese journal of radiology, vol.36, no.9, 566-574.

  8. arXiv 1409 1556 Very deep convolutional networks for large-scale image recognition simonyan 2014 

  9. Ferahtia, Jalal, Baddari, Kamel, Djarfour, Nouredine, Kassouri, Abdel Kader. Incorporation of a Non-linear Image Filtering Technique for Noise Reduction in Seismic Data. Pure and applied geophysics, vol.167, no.11, 1389-1404.

  10. Bekara, Maïza, van der Baan, Mirko. Random and coherent noise attenuation by empirical mode decomposition. Geophysics, vol.74, no.5, V89-V98.

  11. Anagaw, Amsalu Y, Sacchi, Mauricio D. Edge-preserving seismic imaging using the total variation method. Journal of Geophysics and Engineering, vol.9, no.2, 138-146.

  12. AlBinHassan, Nasher M., Luo, Yi, Al-Faraj, Mohammed N.. 3D edge-preserving smoothing and applications. Geophysics, vol.71, no.4, P5-P11.

  13. Bonar, David, Sacchi, Mauricio. Denoising seismic data using the nonlocal means algorithm. Geophysics, vol.77, no.1, A5-A8.

  14. Yuan, Sanyi, Wang, Shangxu, Li, Guofa. Random noise reduction using Bayesian inversion. Journal of Geophysics and Engineering, vol.9, no.1, 60-68.

  15. Baddari, Kamel, Ferahtia, Jalal, Aïfa, Tahar, Djarfour, Noureddine. Seismic noise attenuation by means of an anisotropic non-linear diffusion filter. Computers & geosciences, vol.37, no.4, 456-463.

  16. Liu, Yang, Fomel, Sergey, Liu, Guochang. Nonlinear structure-enhancing filtering using plane-wave prediction*. Geophysical prospecting, vol.58, no.3, 415-427.

  17. Yu, Siwei, Ma, Jianwei, Wang, Wenlong. Deep learning for denoising. Geophysics, vol.84, no.6, V333-V350.

  18. arXiv 1803 04189 Noise2Noise: Learning image restoration without clean data lehtinen 2018 

  19. LeCun, Yann, Bengio, Yoshua, Hinton, Geoffrey. Deep learning. Nature, vol.521, no.7553, 436-444.

  20. Wang, Wei, Wang, Xiangzeng, Zeng, Hongliu, Liang, Quansheng. Preconditioning point-source/point-receiver high-density 3D seismic data for lacustrine shale characterization in a loess mountain area. Interpretation, vol.5, no.2, SF177-SF188.

  21. Ristau, J. P., Moon, Wooil M.. Adaptive filtering of random noise in 2-D geophysical data. Geophysics, vol.66, no.1, 342-349.

  22. Fehmers, Gijs C., Höcker, Christian F. W.. Fast structural interpretation with structure‐oriented filtering. Geophysics, vol.68, no.4, 1286-1293.

  23. Tang, Gang, Ma, Jian-Wei, Yang, Hui-Zhu. Seismic data denoising based on learning-type overcomplete dictionaries. Applied geophysics = 應用地球物理. 英文版, vol.9, no.1, 27-32.

  24. Image structure analysis for seismic interpretation bakker 2002 

  25. 10.1109/ISBI.2017.7950542 

  26. Jeng, Y., Li, Y.W., Chen, C.S., Chien, H.Y.. Adaptive filtering of random noise in near-surface seismic and ground-penetrating radar data. Journal of applied geophysics, vol.68, no.1, 36-46.

  27. Qi Dou, Hao Chen, Lequan Yu, Lei Zhao, Jing Qin, Defeng Wang, Mok, Vincent Ct, Lin Shi, Pheng-Ann Heng. Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks. IEEE transactions on medical imaging, vol.35, no.5, 1182-1195.

  28. 10.1109/CVPR.2018.00685 

  29. Zhong, Zilong, Li, Jonathan, Luo, Zhiming, Chapman, Michael. Spectral–Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework. IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society, vol.56, no.2, 847-858.

  30. Liu, Guo-Chang, Chen, Xiao-Hong, Li, Jing-Ye, Du, Jing, Song, Jia-Wen. Seismic noise attenuation using nonstationary polynomial fitting. Applied geophysics = 應用地球物理. 英文版, vol.8, no.1, 18-26.

  31. Hagen, D.C.. The application of principal components analysis to seismic data sets. Geoexploration : international journal of mining and technical geophysics and related subjects, vol.20, no.1, 93-111.

  32. Geophysics Spectral decomposition of seismic data with continuous-wavelet transform sinha 2008 10.1190/1.2127113 70 19 

  33. Candès, Emmanuel J., Donoho, David L.. Continuous curvelet transform : I. Resolution of the wavefront set. Applied and computational harmonic analysis, vol.19, no.2, 162-197.

  34. 10.1190/1.2370118 

  35. Neelamani, Ramesh, Baumstein, Anatoly I., Gillard, Dominique G., Hadidi, Mohamed T., Soroka, William L.. Coherent and random noise attenuation using the curvelet transform. The leading edge, vol.27, no.2, 240-248.

  36. Hennenfent, G., Herrmann, F.J.. Seismic denoising with nonuniformly sampled curvelets. Computing in science & engineering, vol.8, no.3, 16-25.

  37. 10.1109/CVPR.2006.142 

  38. Aharon, M., Elad, M., Bruckstein, A.. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society, vol.54, no.11, 4311-4322.

  39. DeVries, Phoebe M. R., Thompson, T. Ben, Meade, Brendan J.. Enabling large‐scale viscoelastic calculations via neural network acceleration. Geophysical research letters, vol.44, no.6, 2662-2669.

  40. Comput Techn Geophys Geochem Explor Deep learning and its application in deep gas reservoir prediction cao 2017 39 775 

  41. Wu, Hao, Zhang, Bo, Lin, Tengfei, Li, Fangyu, Liu, Naihao. White noise attenuation of seismic trace by integrating variational mode decomposition with convolutional neural network. Geophysics, vol.84, no.5, V307-V317.

  42. Zhang, Kai, Zuo, Wangmeng, Chen, Yunjin, Meng, Deyu, Zhang, Lei. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol.26, no.7, 3142-3155.

  43. Proc Adv Neural Inf Process Syst Image denoising and inpainting with deep neural networks xie 2012 341 

  44. 10.1109/CVPR.2012.6247952 

  45. Proc 58th Annu Int Meeting SEG Expanded Abstracts Deep learning for ground-roll noise attenuation li 2018 1981 

  46. 10.1190/segam2018-2996306.1 

  47. 10.1190/segam2018-2984932.1 

  48. 10.1190/segam2018-2995341.1 

  49. J Seismic Explor ARMA formulation of FX prediction error filters and projection filters sacchi 2001 9 185 

  50. Abma, Ray, Claerbout, Jon. Lateral prediction for noise attenuation byt-xandf-xtechniques. Geophysics, vol.60, no.6, 1887-1896.

  51. Proc Tutorial Abstr ACL Deep learning for NLP (without magic) socher 2012 5 

  52. Chase, Michael K.. Random Noise Reduction by FXY Prediction Filtering. Exploration geophysics, vol.23, no.2, 51-55.

  53. 10.1190/1.3063880 

  54. Yuan, Sanyi, Wang, Shangxu. A local f-x Cadzow method for noise reduction of seismic data obtained in complex formations. Petroleum science, vol.8, no.3, 269-277.

  55. Prog Geophys An overview of the methods and techniques for seismic data noise attenuation zhang 2006 21 546 

  56. Oil Geophys Prospecting Seismic random noise suppression based on the discrete cosine transform lu 2011 46 202 

  57. Oil Geophys Prospecting A study on noise-suppression method in fx domain ye 2003 38 136 

  58. Gabor, D.. Theory of communication. Part 1: The analysis of information. The journal of the Institution of Electrical Engineers. Part 3, Radio and communication engineering, vol.93, no.26, 429-441.

  59. Mallat, S.G.. A theory for multiresolution signal decomposition: the wavelet representation. IEEE transactions on pattern analysis and machine intelligence, vol.11, no.7, 674-693.

  60. Lu, Wenkai. Adaptive noise attenuation of seismic images based on singular value decomposition and texture direction detection. Journal of Geophysics and Engineering, vol.3, no.1, 28-34.

  61. Bekara, Maïza, Van der Baan, Mirko. Local singular value decomposition for signal enhancement of seismic data. Geophysics, vol.72, no.2, V59-V65.

  62. JONES, I.F., LEVY, S.. SIGNAL‐TO‐NOISE RATIO ENHANCEMENT IN MULTICHANNEL SEISMIC DATA VIA THE KARHUNEN‐LOÉVE TRANSFORM*. Geophysical prospecting, vol.35, no.1, 12-32.

  63. URSIN, B., ZHENG, Y.. IDENTIFICATION OF SEISMIC REFLECTIONS USING SINGULAR VALUE DECOMPOSITION*. Geophysical prospecting, vol.33, no.6, 773-799.

  64. 10.1007/978-3-642-25792-6_15 

  65. AL‐YAHYA, KAMAL M.. APPLICATION OF THE PARTIAL KARHUNEN‐LOÈVE TRANSFORM TO SUPPRESS RANDOM NOISE IN SEISMIC SECTIONS1. Geophysical prospecting, vol.39, no.1, 77-93.

  66. Prog Geophys Lithology identification method based on continuous restricted Boltzmann machine and support vector machine wu 2016 31 821 

  67. 10.3997/2214-4609.201402382 

  68. Araya-Polo, Mauricio, Dahlke, Taylor, Frogner, Charlie, Zhang, Chiyuan, Poggio, Tomaso, Hohl, Detlef. Automated fault detection without seismic processing. The leading edge, vol.36, no.3, 208-214.

  69. Proc Adv Neural Inf Process Syst Predicting geological features in 3D Seismic Data dahlke 2016 29 1 

  70. 10.2118/180359-MS 

  71. arXiv 1611 08655 A deep neural network to identify foreshocks in real time vikraman 2016 

  72. Valentine, Andrew P., Trampert, Jeannot. Data space reduction, quality assessment and searching of seismograms: autoencoder networks for waveform data. Geophysical journal international, vol.189, no.2, 1183-1202.

  73. 10.1109/ICCV.2015.312 

  74. Liu, Qihe, Hu, Xiaonan, Ye, Mao, Cheng, Xianqiong, Li, Fan. Gas Recognition under Sensor Drift by Using Deep Learning. International journal of intelligent systems, vol.30, no.8, 907-922.

  75. Valentine, Andrew P., Kalnins, Lara M., Trampert, Jeannot. Discovery and analysis of topographic features using learning algorithms: A seamount case study. Geophysical research letters, vol.40, no.12, 3048-3054.

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