Geophysics and Geophysical Exploration (지구물리와물리탐사)

Korean Society of Earth and Exploration Geophysicists (한국지구물리·물리탐사학회)
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Case Analysis of Applications of Seismic Data Denoising Methods using Deep-Learning Techniques

심층 학습 기법을 이용한 탄성파 자료 잡음 제거 적용사례 분석

  • Jo, Jun Hyeon (Department of Energy Resources Engineering, Pukyong National University) ;
  • Ha, Wansoo (Department of Energy Resources Engineering, Pukyong National University)
  • 조준현 (부경대학교 에너지자원공학과) ;
  • 하완수 (부경대학교 에너지자원공학과)
  • Received : 2020.03.04
  • Accepted : 2020.05.07
  • Published : 2020.05.31
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Abstract

Recent rapid advances in computer hardware performance have led to relatively low computational costs, increasing the number of applications of machine-learning techniques to geophysical problems. In particular, deep-learning techniques are gaining in popularity as the number of cases successfully solving complex and nonlinear problems has gradually increased. In this paper, applications of seismic data denoising methods using deep-learning techniques are introduced and investigated. Depending on the type of attenuated noise, these studies are grouped into denoising applications of coherent noise, random noise, and the combination of these two types of noise. Then, we investigate the deep-learning techniques used to remove the corresponding noise. Unlike conventional methods used to attenuate seismic noise, deep neural networks, a typical deep-learning technique, learn the characteristics of the noise independently and then automatically optimize the parameters. Therefore, such methods are less sensitive to generalized problems than conventional methods and can reduce labor costs. Several studies have also demonstrated that deep-learning techniques perform well in terms of computational cost and denoising performance. Based on the results of the applications covered in this paper, the pros and cons of the deep-learning techniques used to remove seismic noise are analyzed and discussed.

최근 컴퓨터 하드웨어 성능의 급속한 발전으로 인해 계산 비용이 상대적으로 낮아지면서 기계 학습 기법을 지구물리학적 문제에 적용하는 사례가 점차 증가하고 있다. 특히 심층 학습 기법이 복잡하고 비선형적인 문제를 성공적으로 해결하는 사례가 많아지면서 큰 인기를 얻고 있다. 이 논문에서는 심층 학습 기법을 이용한 탄성파 자료 잡음 제거 적용사례를 조사하고 소개하였다. 감쇠하고자 하는 잡음 유형에 따라 일관성 잡음 적용사례, 무작위 잡음 적용사례, 일관성 잡음 및 무작위 잡음 적용사례로 분류하였고 해당 잡음 제거에 사용된 심층 학습 기법에 대해 조사하였다. 대표적인 심층 학습 기법인 심층 신경망은 탄성파 잡음 제거에 사용된 기존 기법과 달리 잡음의 특징을 스스로 학습하며 매개변수를 자동으로 최적화한다. 따라서 기존 기법에 비해 일반화 문제에 덜 민감하며 인적 비용을 절감할 수 있다. 또한 여러 연구 사례를 통해 계산 비용이나 잡음 제거 성능 측면에서도 심층 학습 기법이 뛰어난 성과를 달성하는 것을 보여주었다. 연구 결과들을 토대로 탄성파 잡음 제거에 사용된 심층 학습 기법의 장단점에 대해 분석하고 논의하였다.

Keywords

References

  1. Abma, R., and Claerbout, J., 1995, Lateral prediction for noise attenuation by t-x and f-x techniques, Geophysics, 60, 1887-1896, doi: 10.1190/1.1443920.
  2. Canales, L. L., 1984, Random noise reduction, 54th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 525-527, doi: 10.1190/1.1894168.
  3. Chen, Y., 2016, Dip-separated structural filtering using seislet transform and adaptive empirical mode decomposition based dip filter, Geophys. J. Int., 206(1), 457-469, doi: 10.1093/gji/ggw165.
  4. Chollet, F., 2018, Deep Learning with Python, Manning, 29-35.
  5. Deighan, A. J., and Watts, D. R., 1997, Ground-roll suppression using the wavelet transform, Geophysics, 62(6), 1896-1903, doi: 10.1190/1.1444290.
  6. Goodfellow, I., Bengio, Y., and Courville, A., 2016, Deep learning, MIT Press, 12-26.
  7. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y., 2014, Generative adversarial nets, Adv. Neural. Inf. Process. Syst., 2672-2680.
  8. Greenspan, H., Ginneken, B. van, and Summers, R. M., 2016, Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique, IEEE Trans. Med. Imaging., 35(5), 1153-1159, doi: 10.1109/TMI.2016.2553401.
  9. 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.
  10. Jia, Z., Lu, W., Zhang, M., and Miao, Y., 2018, Separating ground-roll from land seismic record via convolutional neural network, SEG 2018 Workshop: SEG Maximizing Asset Value Through Artificial Intelligence and Machine Learning, Beijing, China, Global Meeting Abstracts, 60-63, doi: 10.1190/AIML2018-16.1.
  11. Jin, Y., Wu, X., Chen, J., Han, Z., and Hu, W., 2018, Seismic data denoising by deep-residual networks, 88th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 4593-4597, doi: 10.1190/segam2018-2998619.1.
  12. Kim, Y., Hardisty, R., and Marfurt, K. J., 2019, Seismic random noise attenuation in f-x domain using complex-valued residual convolutional neural network, 89th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 2579-2583, doi: 10.1190/segam2019-3216543.1.
  13. Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., and Shi, W., 2017, Photo-realistic single image super-resolution using a generative adversarial network, Proc. IEEE. Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 4681-4690, doi: 10.1109/CVPR.2017.19.
  14. Li, H., Chen, D., and Chang, D., 2019, Ground-roll noise attenuation based on convolutional neural network, SEG 2019 Workshop: Fractured Reservoir & Unconventional Resources Forum: Prospects and Challenges in the Era of Big Data, Lanzhou, China, Global Meeting Abstracts, 69-73, doi: 10.1190/frur2019_18.1.
  15. Li, H., Yang, W., and Yong, X., 2018, Deep learning for groundroll noise attenuation, 88th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 1981-1985, doi: 10.1190/segam2018-2981295.1.
  16. Liu, D., Deng, Z., Wang, X., Wang, W., Shi, Z., Wang, C., and Chen, W., 2020, Must we have labels for denoising seismic data based on deep learning?, SEG 2019 Workshop: Mathematical Geophysics: Traditional vs Learning, Beijing, China, Global Meeting Abstracts, 31-35, doi: 10.1190/iwmg2019_08.1.
  17. Liu, D., Wang, W., Chen, W., Wang, X., Zhou, Y., and Shi, Z., 2018, Random-noise suppression in seismic data: What can deep learning do?, 88th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 2016-2020, doi: 10.1190/segam2018-2998114.1.
  18. Liu, J., and Marfurt, K. J., 2004, 3-d high resolution radon transforms applied to ground-roll suppression in orthogonal seismic surveys, 74th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 2144-2147, doi: 10.1190/1.1851203.
  19. 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.
  20. Rowley, H. A., Baluja, S., and Kanade, T., 1998, Neural networkbased face detection, IEEE Trans. Pattern Anal. Mach. Intell., 20(1), 23-38, doi: 10.1109/34.655647.
  21. Si, X., and Yuan, Y., 2018, Random noise attenuation based on residual learning of deep convolutional neural network, 88th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 1986-1990, doi: 10.1190/segam2018-2985176.1.
  22. Si, X., Yuan, Y., Ping, F., Zheng, Y., and Feng, L., 2020, Ground roll attenuation based on conditional and cycle generative adversarial networks, SEG 2019 Workshop: Mathematical Geophysics: Traditional vs Learning, Beijing, China, Global Meeting Abstracts, 95-98, doi: 10.1190/iwmg2019_23.1.
  23. Trickett, S., 2008, F-xy cadzow noise suppression, 78th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 2586-2590, doi: 10.1190/1.3063880.
  24. Wang, E., and Nealon, J., 2019, Applying machine learning to 3D seismic image denoising and enhancement, Interpret., 7(3), SE131-SE139, doi: 10.1190/INT-2018-0224.1.
  25. Xie, P., Boelle, J. L., and Puntous, H., 2018, Generativeadversarial network-based fast-noise removal on land-seismic data, 88th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 2171-2175, doi: 10.1190/segam2018-2995310.1.
  26. Xu, J., Mai, H., and Cao, S., 2019, Joint dictionary learning and super-resolution network on seismic noise removal, 89th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 2473-2477, doi: 10.1190/segam2019-3215320.1.
  27. Yarham, C., and Herrmann, F. J., 2008, Bayesian ground-roll separation by curvelet-domain sparsity promotion, 78th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 2576-2580.
  28. Yilmaz, O., 2001, Seismic data analysis: processing, inversion and interpretation of seismic data, Vol. I, Soc. Expl. Geophys., 837-998.
  29. Yu, S., Ma, J., and Wang, W., 2019, Deep learning for denoising, Geophysics, 84(6), V333-V350, doi: 10.1190/geo2018-0668.1.
  30. Zhang, C., and Baan, M. van der, 2019, Microseismic and seismic noise attenuation by supervised deep learning with noisy natural images, 89th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 4485-4489, doi: 10.1190/segam2019-3213158.1.
  31. Zhang, F., Liu, D., Wang, X., Chen, W., and Wang, W., 2018, Random noise attenuation method for seismic data based on deep residual networks, SEG International Geophysical Conference, Beijing, China, Global Meeting Abstracts, 1774-1777, doi: 10.1190/IGC2018-435.
  32. Zhang, K., Zuo, W., Y., Meng, D., and Zhang, L., 2017, Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising, IEEE Trans. Image Process., 26(7), 3142-3155, doi: 10.1109/TIP.2017.2662206.
  33. Zhang, Y., Lin, H., and Li, Y., 2018, Noise attenuation for seismic image using a deep-residual learning, 88th Ann. Internat. Mtg. Soc. Expl. Geophys., Expanded Abstracts, 2176-2180, doi: 10.1190/segam2018-2997974.1.
  34. Zhao, X., Lu, P., Zhang, Y., Chen, J., and Li, X., 2019, Swellnoise attenuation: A deep learning approach, Lead. Edge, 38(12), 934-942, doi: 10.1190/tle38120934.1.
  35. Zheng, Y., Yuan, Y., and Si, X., 2020, The improved DnCNN for linear noise attenuation, SEG 2019 Workshop: Mathematical Geophysics: Traditional vs Learning, Beijing, China, Global Meeting Abstracts, 56-59, doi: 10.1190/iwmg2019_14.1.