$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

De-aliased seismic data interpolation using conditional Wasserstein generative adversarial networks

Computers & geosciences, v.154, 2021년, pp.104801 -   

Wei, Qing (CNPC Key Laboratory of Geophysical Prospecting, China University of Petroleum-Beijing) ,  Li, Xiangyang (CNPC Key Laboratory of Geophysical Prospecting, China University of Petroleum-Beijing) ,  Song, Mingpeng (Institute of Geology and Geophysics, Chinese Academy of Sciences)

Abstract AI-Helper 아이콘AI-Helper

Abstract When sampling at offset is too coarse during seismic acquisition, spatial aliasing will appear, affecting the accuracy of subsequent processing. The receiver spacing can be reduced by interpolating one or more traces between every two traces to remove the spatial aliasing. And the seismic ...

주제어

참고문헌 (63)

  1. Alwon 1991 2018 SEG Technical Program Expanded Abstracts 2018 Generative adversarial networks in seismic data processing 

  2. Comput. Geosci. Anderson 145 104593 2020 10.1016/j.cageo.2020.104593 Multimodal imaging and machine learning to enhance microscope images of shale 

  3. Arjovsky 2017 Wasserstein gan. arXiv:1701.07875 

  4. Comput. Geosci. Avalos 141 104522 2020 10.1016/j.cageo.2020.104522 Recursive convolutional neural networks in a multiple-point statistics framework 

  5. Comput. Geosci. Bai 142 104519 2020 10.1016/j.cageo.2020.104519 Hybrid geological modeling: combining machine learning and multiple-point statistics 

  6. Comput. Geosci. Canchumuni 128 87 2019 10.1016/j.cageo.2019.04.006 Towards a robust parameterization for conditioning facies models using deep variational autoencoders and ensemble smoother 

  7. IEEE Trans. Inf. Theor. Candes 52 489 2006 10.1109/TIT.2005.862083 Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information 

  8. Geosci. Rem. Sens. Lett. IEEE Chang 1 2020 Seismic data interpolation using dual-domain conditional generative adversarial networks 

  9. Chang 2589 2019 Seismic Data Interpolation with Conditional Generative Adversarial Network in Time and Frequency Domain 

  10. IEEE Trans. Inf. Theor. Donoho 52 1289 2006 10.1109/TIT.2006.871582 Compressed sensing 

  11. Geophysics Duijndam 64 524 1999 10.1190/1.1444559 Reconstruction of band-limited signals, irregularly sampled along one spatial direction 

  12. Ebenezer 1 2019 2019 27th European Signal Processing Conference (EUSIPCO) Single image haze removal using conditional wasserstein generative adversarial networks 

  13. Geophysics Fomel 68 733 2003 10.1190/1.1567243 Seismic reflection data interpolation with differential offset and shot continuation 

  14. Comput. Geosci. Freeman 139 104479 2020 10.1016/j.cageo.2020.104479 Content search within large environmental datasets using a convolution neural network 

  15. J. Appl. Geophys. Gan 130 194 2016 10.1016/j.jappgeo.2016.03.033 Compressive sensing for seismic data reconstruction via fast projection onto convex sets based on seislet transform 

  16. Geosci. Rem. Sens. Lett. IEEE Gan 12 2150 2015 10.1109/LGRS.2015.2453119 Dealiased seismic data interpolation using seislet transform with low-frequency constraint 

  17. Geophys. Prospect. Gao 61 138 2013 10.1111/j.1365-2478.2012.01103.x Convergence improvement and noise attenuation considerations for beyond alias projection onto convex sets reconstruction 

  18. Goodfellow 2672 2014 Advances in Neural Information Processing Systems Generative adversarial nets 

  19. Geophysics Gray 78 S157 2013 10.1190/geo2012-0451.1 Spatial sampling, migration aliasing, and migrated amplitudes 

  20. Comput. Geosci. Han 133 104312 2019 10.1016/j.cageo.2019.104312 Measuring rock surface strength based on spectrograms with deep convolutional networks 

  21. He 2016 The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Deep residual learning for image recognition 

  22. Geophysics Herrmann 75 WB173 2010 10.1190/1.3506147 Randomized sampling and sparsity: getting more information from fewer samples 

  23. Geophys. J. Int. Herrmann 173 233 2008 10.1111/j.1365-246X.2007.03698.x Non-parametric seismic data recovery with curvelet frames 

  24. Science Hinton 313 504 2006 10.1126/science.1127647 Reducing the dimensionality of data with neural networks 

  25. Ioffe 2015 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 

  26. Isola 2017 The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Image-to-image translation with conditional adversarial networks 

  27. Geophysics Jia 82 V163 2017 10.1190/geo2016-0300.1 What can machine learning do for seismic data processing? an interpolation application 

  28. J. Appl. Geophys. Jia 132 137 2016 10.1016/j.jappgeo.2016.06.010 A fast rank-reduction algorithm for three-dimensional seismic data interpolation 

  29. Geophys. Prospect. Kabir 43 347 2006 10.1111/j.1365-2478.1995.tb00257.x Restoration of missing offsets by parabolic radon transformation 

  30. Kaur 2202 2019 SEG Technical Program Expanded Abstracts 2019 Seismic data interpolation using cyclegan 

  31. Comput. Geosci. Laloy 133 104333 2019 10.1016/j.cageo.2019.104333 Gradient-based deterministic inversion of geophysical data with generative adversarial networks: is it feasible? 

  32. Proc. IEEE Lecun 86 2278 1998 10.1109/5.726791 Gradient-based learning applied to document recognition 

  33. Ledig 2017 The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Photo-realistic single image super-resolution using a generative adversarial network 

  34. Geophysics Liu 76 V69 2011 10.1190/geo2010-0231.1 Seismic data interpolation beyond aliasing using regularized nonstationary autoregression 

  35. Neurocomputing Liu 311 78 2018 10.1016/j.neucom.2018.05.045 Auto-painter: cartoon image generation from sketch by using conditional wasserstein generative adversarial networks 

  36. Lead. Edge Lu 37 578 2018 10.1190/tle37080578.1 Using generative adversarial networks to improve deep-learning fault interpretation networks 

  37. Luo 2016 SPE Bergen One Day Seminar An ensemble 4d seismic history matching framework with sparse representation based on wavelet multiresolution analysis 

  38. Geophysics Ma 78 V181 2013 10.1190/geo2012-0465.1 Three-dimensional irregular seismic data reconstruction via low-rank matrix completion 

  39. Maas 3 2013 Proc. icml Rectifier nonlinearities improve neural network acoustic models 

  40. Mirza 1784 2014 Conditional Generative Adversarial Nets 

  41. Mosser 2018 80th EAGE Conference and Exhibition 2018 Rapid seismic domain transfer: seismic velocity inversion and modeling using deep generative neural networks 

  42. Geophysics Naghizadeh 74 V9 2009 10.1190/1.3008547 f-x adaptive seismic-trace interpolation 

  43. Geophysics Naghizadeh 75 WB189 2010 10.1190/1.3509468 Beyond alias hierarchical scale curvelet interpolation of regularly and irregularly sampled seismic data 

  44. Geosci. Rem. Sens. Lett. IEEE Oliveira 1 2019 Improving seismic data resolution with deep generative networks 

  45. Geosci. Rem. Sens. Lett. IEEE Oliveira 15 1952 2018 10.1109/LGRS.2018.2866199 Interpolating seismic data with conditional generative adversarial networks 

  46. Comput. Geosci. Pan 145 104609 2020 10.1016/j.cageo.2020.104609 A partial convolution-based deep-learning network for seismic data regularization 1 

  47. Interpretation Picetti 7 SF15 2019 10.1190/INT-2018-0232.1 Seismic image processing through the generative adversarial network 

  48. Reed 2016 Generative Adversarial Text to Image Synthesis. arXiv Preprint arXiv:1605.05396 

  49. Geophysics Ronen 52 973 1987 10.1190/1.1442366 Wave-equation trace interpolation 

  50. Ronneberger 234 2015 Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 U-net: convolutional networks for biomedical image segmentation 

  51. Salimans 2234 2016 Advances in Neural Information Processing Systems 29 Improved techniques for training gans 

  52. J. Petrol. Sci. Eng. Soares 195 107763 2020 10.1016/j.petrol.2020.107763 4d seismic history matching: assessing the use of a dictionary learning based sparse representation method 

  53. Geophysics Spitz 56 785 1991 10.1190/1.1443096 Seismic trace interpolation in the F-X domain 

  54. Geophysics Trad 68 2043 2003 10.1190/1.1635058 Interpolation and multiple attenuation with migration operators 

  55. Geophysics Trad 74 V123 2009 10.1190/1.3245216 Five-dimensional interpolation: recovering from acquisition constraints 

  56. Geophysics Trad 67 644 2002 10.1190/1.1468626 Accurate interpolation with high-resolution time-variant radon transforms 

  57. Trickett 2010 Seg Technical Program Expanded Rank-reduction-based trace interpolation 

  58. Geophysics Wang 84 V11 2019 10.1190/geo2017-0495.1 Deep-learning-based seismic data interpolation: a preliminary result 

  59. Comput. Geosci. Wang 36 1292 2010 10.1016/j.cageo.2010.03.012 Accelerating pocs interpolation of 3d irregular seismic data with graphics processing units 

  60. Comput. Geosci. Wang 133 104314 2019 10.1016/j.cageo.2019.104314 Ct-image of rock samples super resolution using 3d convolutional neural network 

  61. Geophysics Zhang 85 WA115 2020 10.1190/geo2019-0243.1 Can learning from natural image denoising be used for seismic data interpolation? 

  62. Zhu 2017 Unpaired Image-To-Image Translation Using Cycle-Consistent Adversarial Networks. CoRR abs/1703.10593 

  63. Zhu 597 2016 European Conference on Computer Vision Generative visual manipulation on the natural image manifold 

관련 콘텐츠

저작권 관리 안내
섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로