DOI QR코드

DOI QR Code

Research Trend analysis for Seismic Data Interpolation Methods using Machine Learning

머신러닝을 사용한 탄성파 자료 보간법 기술 연구 동향 분석

  • Bae, Wooram (Department of Energy Resources Engineering, Pukyong National University) ;
  • Kwon, Yeji (Department of Energy Resources Engineering, Pukyong National University) ;
  • Ha, Wansoo (Department of Energy Resources Engineering, Pukyong National University)
  • 배우람 (부경대학교 에너지자원공학과) ;
  • 권예지 (부경대학교 에너지자원공학과) ;
  • 하완수 (부경대학교 에너지자원공학과)
  • Received : 2020.06.10
  • Accepted : 2020.08.24
  • Published : 2020.08.31

Abstract

We acquire seismic data with regularly or irregularly missing traces, due to economic, environmental, and mechanical problems. Since these missing data adversely affect the results of seismic data processing and analysis, we need to reconstruct the missing data before subsequent processing. However, there are economic and temporal burdens to conducting further exploration and reconstructing missing parts. Many researchers have been studying interpolation methods to accurately reconstruct missing data. Recently, various machine learning technologies such as support vector regression, autoencoder, U-Net, ResNet, and generative adversarial network (GAN) have been applied in seismic data interpolation. In this study, by reviewing these studies, we found that not only neural network models, but also support vector regression models that have relatively simple structures can interpolate missing parts of seismic data effectively. We expect that future research can improve the interpolation performance of these machine learning models by using open-source field data, data augmentation, transfer learning, and regularization based on conventional interpolation technologies.

References

  1. Abma, R., and Kabir, N., 2006, 3D interpolation of irregular data with a POCS algorithm, Geophysics, 71(6), E91-E97., doi:10.1190/1.2356088
  2. Cai, J. F., Ji, H., Shen, Z., and Ye, G. B, 2014, Data-driven tight frame construction and image denoising, Appl. Comput. Harmon. Anal., 37(1), 89-105., doi: 10.1016/j.acha.2013.10.001
  3. Chai, X., Gu, H., Li, F., Duan, H., Hu, X., and Lin, K., 2020, Deep learning for irregularly and regularly missing data reconstruction, Scientific Reports, 10(1), 1-18., doi: 10.1016/j.acha.2013.10.001
  4. Chang, D. K., Yang, W. Y., Yong, X. S., and Li, H. S., 2019, Seismic data interpolation with conditional generative adversarial network in time and frequency domain, 89th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 2589-2593., doi: 10.1190/segam2019-3210118.1
  5. Chang, D., Yang, W., Yong, X., and Li, H., ,2018, Generative adversarial networks for seismic data interpolation, In SEG 2018 Workshop: SEG Maximizing Asset Value Through Artificial Intelligence and Machine Learning, Beijing, China, Global Meeting Abstracts, 40-43, doi: 10.1190/AIML2018-11.1
  6. Chen, Y., Zhang, D., Jin, Z., Chen, X., Zu, S., Huang, W., and Gan, S., 2016, Simultaneous denoising and reconstruction of 5-D seismic data via damped rank-reduction method, Geophys. J. Int. 206(3), 1695-1717., doi: 10.1093/gji/ggw230
  7. Cunha, A., Pochet, A., Lopes, H., and Gattass, M., 2020, Seismic fault detection in real data using transfer learning from a convolutional neural network pre-trained with synthetic seismic data, Comput. Geosci., 135, 104344., doi: 10.1016/j.cageo.2019.104344
  8. Di, H., 2018, Developing a seismic pattern interpretation network (SpiNet) for automated seismic interpretation, arXiv preprint arXiv:1810.08517
  9. Fomel, S., 2002, Applications of plane-wave destruction filters, Geophysics, 67(6), 1946-1960., doi: 10.1190/1.1527095
  10. Fomel, S., 2003, Seismic reflection data interpolation with differential offset and shot continuation, Geophysics, 68(2), 733-744., doi: 10.1190/1.1567243
  11. Gan, S., Wang, S., Chen, Y., Zhang, Y., and Jin, Z., 2015, Dealiased seismic data interpolation using seislet transform with low-frequency constraint. IEEE Geosci. Remote Sens. Lett., 12(10), 2150-2154., doi: 10.1109/LGRS.2015.2453119
  12. Gao, J., Sacchi, M. D., and Chen, X., 2013a, A fast reducedrank interpolation method for prestack seismic volumes that depend on four spatial dimensions, Geophysics, 78(1), V21-V30., doi: 10.1190/geo2012-0038.1
  13. Gao, J., Stanton, A., and Sacchi, M. D., 2015, Parallel matrix factorization algorithm and its application to 5D seismic reconstruction and denoising, Geophysics, 80(6), V173-V187., doi: 10.1190/geo2014-0594.1
  14. Gao, J., Stanton, A., Naghizadeh, M., Sacchi, M. D., & Chen, X., 2013b, Convergence improvement and noise attenuation considerations for beyond alias projection onto convex sets reconstruction, Geophys. Prospect., 61, 138-151., doi: 10.1111/j.1365-2478.2012.01103.x
  15. Geron, A., 2017, Hands-On Machine Learning with Scikit-Learn and TensorFlow, O'Reilly Media, Sebastopol, CA, 54-56.
  16. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y., 2014, Generative adversarial nets, In Advances in neural information processing systems (pp. 2672-2680).
  17. He, K., Zhang, X., Ren, S., and Sun, J., 2016, Deep residual learning for image recognition, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 770-778., doi: 10.1109/CVPR.2016.90
  18. Herrmann, F. J., and Hennenfent, G., 2008, Non-parametric seismic data recovery with curvelet frames, Geophys. J. Int., 173(1), 233-248., doi: 10.1111/j.1365-246X.2007.03698.x
  19. Isola, P., Zhu, J. Y., Zhou, T., and Efros, A. A., 2017, Image-toimage translation with conditional adversarial networks, Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit, 1125-1134., doi: 10.1109/CVPR.2017.632
  20. Jia, Y., and Ma, J., 2017, What can machine learning do for seismic data processing? An interpolation application, Geophysics, 82(3), V163-V177., doi: 10.1190/geo2016-0300.1
  21. Jia, Y., Yu, S., and Ma, J., 2018, Intelligent interpolation by Monte Carlo machine learning, Geophysics, 83(2), V83-V97., doi: 10.1190/geo2017-0294.1
  22. Kaur, H., Pham, N., and Fomel, S., 2019, Seismic data interpolation using CycleGAN, 89th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 2202-2206., doi:10.1190/segam2019-3207424.1
  23. Keys, R., 1981, Cubic convolution interpolation for digital image processing, IEEE Trans. Acoust., 29(6), 1153-1160., doi: 10.1109/TASSP.1981.1163711
  24. Li, S., Yang, C., Sun, H., and Zhang, H., 2019, Seismic fault detection using an encoder-decoder convolutional neural network with a small training set, Journal of Geophysics and Engineering, 16(1), 175-189., doi: 10.1093/jge/gxy015
  25. Liang, J., Ma, J., and Zhang, X., 2014, Seismic data restoration via data-driven tight frame, Geophysics, 79(3), V65-V74., doi: 10.1190/geo2013-0252.1
  26. Liu, W., Cao, S., Gan, S., Chen, Y., Zu, S., and Jin, Z., 2016, One-step slope estimation for dealiased seismic data reconstruction via iterative seislet thresholding, IEEE Geosci. Remote Sens. Lett., 13(10), 1462-1466., doi:10.1109/LGRS.2016.2591939
  27. Liu, Y., and Fomel, S., 2011, Seismic data interpolation beyond aliasing using regularized nonstationary autoregression, Geophysics, 76(5), V69-V77., doi: 10.1190/geo2010-0231.1
  28. Ma, J., 2013, Three-dimensional irregular seismic data reconstruction via low-rank matrix completion, Geophysics, 78(5), V181-V192., doi: 10.1190/geo2012-0465.1
  29. Maaten, L. V. D., and Hinton, G., 2008, Visualizing data using t-SNE, J. Mach. Learn. Res., 2579-2605.
  30. Mandelli, S., Borra, F., Lipari, V., Bestagini, P., Sarti, A., and Tubaro, S., 2018, Seismic data interpolation through convolutional autoencoder, 88th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 4101-4105., doi:10.1190/segam2018-2995428.1
  31. Mirza, M., and Osindero, S., 2014, Conditional generative adversarial nets, arXiv preprint arXiv:1411.1784.
  32. Naghizadeh, M., and Innanen, K. A.., 2011, Seismic data interpolation using a fast generalized Fourier transform, Geophysics, 76(1), V1-V10., doi: 10.1190/1.3511525
  33. Naghizadeh, M., and Sacchi, M. D., 2007, Multistep autoregressive reconstruction of seismic records, Geophysics, 72(6), V111-V118., doi: 10.1190/1.2771685
  34. Naghizadeh, M., and Sacchi, M. D., 2010, Beyond alias hierarchical scale curvelet interpolation of regularly and irregularly sampled seismic data, Geophysics, 75(6), WB189-WB202., doi: 10.1190/1.3509468
  35. Oliveira, D. A., Ferreira, R. S., Silva, R., and Brazil, E. V., 2018, Interpolating seismic data with conditional generative adversarial networks, IEEE Geosci. Remote Sens. Lett., 15(12), 1952-1956., doi: 10.1109/LGRS.2018.2866199
  36. Oropeza, V., and Sacchi, M., 2011, Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis, Geophysics, 76(3), V25-V32., doi: 10.1190/1.3552706
  37. Park, J., Yoon, D., Seol, S. J., and Byun, J., 2019, Reconstruction of seismic field data with convolutional U-Net considering the optimal training input data, 89th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 4650-4654., doi:10.1190/segam2019-3216017.1
  38. Ronen, J., 1987, Wave-equation trace interpolation, Geophysics, 52(7), 973-984., doi: 10.1190/1.1442366
  39. Ronneberger, O., Fischer, P., and Brox, T., 2015, U-net: Convolutional networks for biomedical image segmentation, In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.
  40. Spitz, S., 1991, Seismic trace interpolation in the FX domain, Geophysics, 56(6), 785-794. doi: 10.1190/1.1443096
  41. Trickett, S., Burroughs, L., Milton, A., Walton, L., and Dack, R., 2010, Rank-reduction-based trace interpolation, 80th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 3829-3833., doi: 10.1190/1.3513645
  42. Wang, B., Zhang, N., and Lu, W., 2018, Intelligent shot gather reconstruction using residual learning networks, 88th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 2001-2005., doi: 10.1190/segam2018-2997541.1
  43. Wang, B., Zhang, N., Lu, W., and Wang, J., 2019, Deeplearning-based seismic data interpolation: A preliminary result, Geophysics, 84(1), V11-V20., doi: 10.1190/geo2017-0495.1
  44. Wang, Y., Wang, B., Tu, N., and Geng, J., 2020, Seismic trace interpolation for irregularly spatial sampled data using convolutional autoencoder, Geophysics, 85(2), V119-V130., doi: 10.1190/geo2018-0699.1
  45. Wu, X., Liang, L., Shi, Y., and Fomel, S., 2019, FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation, Geophysics, 84(3), IM35-IM45., doi: 10.1190/geo2018-0646.1
  46. Yoon, D., Yeeh, Z., and Byun, J., 2020, Seismic Data Reconstruction Using Deep Bidirectional Long Short-Term Memory With Skip Connections, IEEE Geosci. Remote Sens. Lett.
  47. Yu, S., Ma, J., and Osher, S., 2016, Monte Carlo data-driven tight frame for seismic data recovery, Geophysics, 81(4), V327-V340., doi: 10.1190/geo2015-0343.1
  48. Yu, S., Ma, J., Zhang, X., and Sacchi, M., 2015, Denoising and interpolation of high-dimensional seismic data by learning tight frame, Geophysics, 80(5), V119-V132., doi: 10.1190/geo2014-0396.1
  49. Zhang, H., Yang, X., and Ma, J., 2020, Can learning from natural image denoising be used for seismic data interpolation?, Geophysics, 85(4), WA115-WA136., doi: 10.1190/geo2019-0243.1
  50. Zhu, J. Y., Park, T., Isola, P., and Efros, A. A., 2017, Unpaired image-to-image translation using cycle-consistent adversarial networks, Proc. IEEE Int. Conf. Comput. Vis., 2223-2232., doi: 10.1109/ICCV.2017.244