All Issue

2021 Vol.58, Issue 5S Preview Page

Research Paper (Special Issue)

October 2021. pp. 408-417
Abstract
References
1
Alwon, S., 2018. Generative adversarial networks in seismic data processing, SEG Technical Program Expanded Abstracts 2018, Society of Exploration Geophysicists, p.1991-1995. 10.1190/segam2018-2996002.1
2
Dondurur, D., 2018. Acquisition and processing of marine seismic data, Elsevier, Amsterdam, Netherlands, 5p. 10.1016/B978-0-12-811490-2.00002-5
3
Ebadi, M.R., 2017. Coherent and incoherent seismic noise attenuation using parabolic radon transform and its application in environmental geophysics, Modeling Earth Systems and Environment, 3(1), 18p. 10.1007/s40808-017-0273-4
4
He, K., Zhang, X., Ren, S., and Sun, J., 2016. Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, p.770-778. 10.1109/CVPR.2016.9026180094
5
Hlebnikov, V., Elboth, T., Vinje, V., and Gelius, L.J., 2021. Noise types and their attenuation in towed marine seismic: A tutorial, Geophysics, 86(2), W1-W19. 10.1190/geo2019-0808.1
6
Hore, A. and Ziou, D., 2010. Image quality metrics: PSNR vs. SSIM, Proceedings of the 20th International Conference on Pattern Recognition, Istanbul, Turkey, p.2366-2369. 10.1109/ICPR.2010.579
7
Ioffe, S. and Szegedy, C., 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, Proceedings of the 32nd International Conference on Machine Learning, PMLR 37, p.448-456.
8
Jun, H., Jou, H.T., Kim, C.H., Lee, S.H., and Kim, H.J., 2020. Random noise attenuation of sparker seismic oceanography data with machine learning, Ocean Sci., 16, p.1367-1383. 10.5194/os-16-1367-2020
9
Kaur, H., Fomel, S., and Pham, N., 2019. Ground roll attenuation using generative adversarial network, 81st EAGE Conference and Exhibition, EAGE, Extended Abstracts, p.1-5. 10.3997/2214-4609.201900762
10
Keras API reference, 2021.07.26, https://keras.io/ko/models/sequential.
11
Kim, Y., Hardisty, R., and Marfurt, K., 2019. Seismic random noise attenuation in f-x domain using complex-valued residual convolutional neural network, SEG Technical Program Expanded Abstracts 2019, Society of Exploration Geophysicists. p.2579-2583. 10.1190/segam2019-3216543.1
12
Kragh, E. and Christie, P., 2002. Seismic repeatability, normalized rms, and predictability, The Leading Edge, 21, p.640-647. 10.1190/1.1497316
13
Krizhevsky, A., Sutskever, I., and Hinton, G.E., 2012. Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, p.1097- 1105.
14
Li, H., Yang, W., and Yong, X., 2018. Deep learning for ground- roll noise attenuation, SEG Technical Program Expanded Abstracts 2018, Society of Exploration Geophysicists, p.1981-1985. 10.1190/segam2018-2981295.1
15
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?, SEG Technical Program Expanded Abstracts 2018, Society of Exploration Geophysicists, p.2016-2020. 10.1190/segam2018-2998114.1
16
Nam, H., Lim, B., Kweon, I., and Kim, J., 2020. Removal of Seabed Multiples in Seismic Reflection Data using Machine Learning, Geophysics and Geophysical Exploration, 23(3), p.168-177.
17
Nasser, M., Ronen, S., and Stammeijer, J., 2016. Introduction to this special section: 4D seismic, The Leading Edge, 35, p.828-830. 10.1190/tle35100828.1
18
Saad, O. and Chen, Y., 2020. Deep denoising autoencoder for seismic random noise attenuation, Geophysics, 85(4), V367-V376. 10.1190/geo2019-0468.1
19
Si, X. and Yuan, Y., 2018. Random noise attenuation based on residual learning of deep convolutional neural network, SEG Technical Program Expanded Abstracts 2018, Society of Exploration Geophysicists. p.1986-1990. 10.1190/segam2018-2985176.1
20
Si, X., 2020. Ground roll attenuation with conditional generative adversarial networks, SEG Technical Program Expanded Abstracts 2020, Society of Exploration Geophysicists. p.1511-1515. 10.1190/segam2020-3424945.1
21
Waage, M., Bünz, S., Landrø, M., Plaza-Faverola, A., and Waghorn, K.A., 2019. Repeatability of high-resolution 3D seismic data, Geophysics, 84(1), B75-B94. 10.1190/geo2018-0099.1
22
Yilmaz, Ö., 2001. Seismic data analysis: processing, inversion and interpretation of seismic data (2nd Ed.), Vol. I, Society of Exploration Geophysicists, Tulsa, Oklahoma, 2027p. 10.1190/1.9781560801580
23
Zhang, K., Zuo, W., Chen, Y., Meng, D., and Zhang, L., 2017. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising, IEEE Trans. Image Process, 26, p.3142-3155. 10.1109/TIP.2017.266220628166495
24
Zhao, X., Lu, P., Zhang, Y., Chen, J., and Li, X., 2019. Swell-noise attenuation: A deep learning approach, The Leading Edge, 38(12), p.934-942. 10.1190/tle38120934.1
25
Zheng, Y., Yuan, Y., and Si, X., 2020. The improved DnCNN linear noise attenuation, SEG 2019 Workshop: Mathematical Geophysics: Traditional vs Learning, Beijing, China, Global Meeting Abstracts, p.56-59. 10.1190/iwmg2019_14.1
Information
  • Publisher :The Korean Society of Mineral and Energy Resources Engineers
  • Publisher(Ko) :한국자원공학회
  • Journal Title :Journal of the Korean Society of Mineral and Energy Resources Engineers
  • Journal Title(Ko) :한국자원공학회지
  • Volume : 58
  • No :5
  • Pages :408-417
  • Received Date :2021. 08. 09
  • Revised Date :2021. 09. 14
  • Accepted Date : 2021. 10. 26