과제정보
이 논문은 2022년도 정부(산업통상자원부)의 재원으로 해외자원개발협회의 지원을 받아 수행된 연구임(데이터사이언스 기반 석유·가스 탐사 컨소시엄).
참고문헌
- Benesty, J., Chen, J., Huang, Y., and Cohen, I., 2009, Pearson correlation coefficient, In Noise reduction in speech processing, Springer, Berlin, Heidelberg. 1-4. doi: 10.1007/978-3-642-00296-0_5.
- Canales, L. L., 1984, Random noise reduction, In SEG Technical Program Expanded Abstracts 1984, Society of Exploration Geophysicists, 525-527. doi: 10.1190/1.1894168.
- Chen, Y., and Fomel, S., 2014, Random noise attenuation using local similarity, In SEG Technical Program Expanded Abstracts 2014, Society of Exploration Geophysicists, 4360-4365. doi: 10.1190/segam2014-0594.1.
- Chen, Y., Zhang, M., Bai, M., and Chen, W., 2019, Improving the signal-to-noise ratio of seismological datasets by unsupervised machine learning, Seismological Research Letters, 90(4), 1552-1564. doi: 10.1785/0220190028.
- Di, H., Li, C., Smith, S., and Abubakar, A., 2019, Machine learning-assisted seismic interpretation with geologic constraints, In SEG International Exposition and Annual Meeting, Society of Exploration Geophysicists, 5360-5364. doi: 10.1190/segam2019-w4-01.1.
- Dondurur, D., 2018, Acquisition and processing of marine seismic data, Elsevier, 174-212. https://www.elsevier.com/books/acquisition-and-processing-of-marine-seismic-data/dondurur/978-0-12-811490-2
- Fabien-Ouellet, G., and Sarkar, R., 2020, Seismic velocity estimation: A deep recurrent neural-network approach, Geophysics, 85(1), U21-U29. doi: 10.1190/geo2018-0786.1.
- Fomel, S, 2007, Local seismic attributes, Geophysics, 72(3), A29-A33. doi: 10.1190/1.2437573.
- Graves, R. W., 1996, Simulating seismic wave propagation in 3D elastic media using staggered-grid finite differences, Bulletin of the seismological society of America, 1091-1106. http://web.gps.caltech.edu/~clay/Ge263/Graves1996.pdf.
- Hore, A. and Ziou, D., 2010, Image quality metrics: PSNR vs. SSIM, Proceedings of the 20th International Conference on Pattern Recognition, Istanbul, Turkey, 2366-2369. doi: 10.1109/ICPR.2010.579.
- 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.
- 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 Science, 16(6), 1367-1383. https://os.copernicus.org/articles/16/1367/2020/os-16-1367-2020.pdf. https://doi.org/10.5194/os-16-1367-2020
- Jun, H., Kim, C., Kim, H.-J., 2021, Machine-learning based noise attenuation of field seismic data using noise data acquisition, Journal of the Korean Society of Mineral and Energy Resources Engineers, 58(5), 408-417. (in Korean with English abstract) https://www.jksmer.or.kr/articles/article/Pwv9/. https://doi.org/10.32390/ksmer.2021.58.5.408
- Jun, H., and Cho, Y., 2022, Repeatability enhancement of time-lapse seismic data via a convolutional autoencoder, Geophysical Journal International, 228(2), 1150-1170. doi: 10.1093/gji/ggab397.
- Kumar, P. C., and Sain, K., 2018, Attribute amalgamation-aiding interpretation of faults from seismic data: An example from Waitara 3D prospect in Taranaki basin off New Zealand, Journal of Applied Geophysics, 159, 52-68. doi: 10.1016/j.jappgeo.2018.07.023.
- Lehtinen, J., Munkberg, J., Hasselgren, J., Laine, S., Karras, T., Aittala, M., and Aila, T., 2018, Noise2noise: Learning image restoration without clean data, arXiv. doi: 10.48550/arXiv.1803.04189.
- Li, D., Peng, S., Lu, Y., Guo, Y., and Cui, X., 2019, Seismic structure interpretation based on machine learning: A case study in coal mining, Interpretation, 7(3), SE69-SE79. doi: 10.1190/INT-2018-0208.1.
- Li, H., Yang, W., and Yong, X., 2018, Deep learning for ground-roll noise attenuation, In SEG Technical Program Expanded Abstracts 2018, Society of Exploration Geophysicists, 1981-1985. doi: 10.1190/segam2018-2981295.1.
- Liu, B., Yue, J., Zuo, Z., Xu, X., Fu, C., Yang, S., and Jiang, P., 2021, Unsupervised deep learning for random noise attenuation of seismic data, IEEE Geoscience and Remote Sensing Letters, 19, 1-5. doi: 10.1109/LGRS.2021.3057631.
- Martin G. S., Wiley R., and Marfurt K. J., 2006, Marmousi2: An elastic upgrade for Marmousi, The Leading Edge, 25, 156-166. doi: 10.1190/1.2172306.
- Quan, Y., Chen, M., Pang, T., and Ji, H., 2020, Self2self with dropout: Learning self-supervised denoising from single image, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10, 1890-1898. doi: 10.1109/CVPR42600.2020.00196.
- Ronneberger, O., Fischer, P., and Brox, T., 2015, October, U-net: Convolutional networks for biomedical image segmentation, In International Conference on Medical image computing and computer-assisted intervention, Springer, Cham, 234-241. https://link.springer.com/chapter/10.1007/978-3-319-24574-4_28.
- Saad, O. M., and Chen, Y., 2020, Deep denoising autoencoder for seismic random noise attenuation, Geophysics, 85(4), V367-V376. doi: 10.1190/geo2019-0468.1.
- Saad, O. M., and Chen, Y., 2021, A fully unsupervised and highly generalized deep learning approach for random noise suppression, Geophysical Prospecting, 69(4), 709-726. doi: 10.1111/1365-2478.13062.
- Schober, P., Boer, C., and Schwarte, L. A., 2018, Correlation coefficients: appropriate use and interpretation, Anesthesia & Analgesia, 126(5), 1763-1768. doi: 10.1213/ANE.0000000000002864.
- Schuba, C. N., Schuba, J. P., Gray, G. G., and Davy, R. G., 2019, Interface-targeted seismic velocity estimation using machine learning, Geophysical Journal International, 218(1), 45-56. doi: 10.1093/gji/ggz142.
- Si, X., and Yuan, Y., 2018, Random noise attenuation based on residual learning of deep convolutional neural network, In SEG technical program expanded abstracts 2018, Society of Exploration Geophysicists, 1986-1990. doi: 10.1190/segam2018-2985176.1.
- Si, X., 2020, Ground roll attenuation with conditional generative adversarial networks, In SEG International Exposition and Annual Meeting, Society of Exploration Geophysicists, Society of Exploration Geophysicists. doi: 10.1190/segam2020-3424945.1.
- Wang, H., Zhang, Q., Zhang, G., Fang, J., and Chen, Y., 2020, Self-training and learning the waveform features of microseismic data using an adaptive dictionary, Geophysics, 85(3), KS51-KS61. doi: 10.1190/geo2019-0213.1.
- Yang, L., Wang, S., Chen, X., Saad, O. M., Chen, W., Oboue, Y. A. S. I., and Chen, Y., 2021, Unsupervised 3-D random noise attenuation using deep skip autoencoder, IEEE Transactions on Geoscience and Remote Sensing, 60, 1-16. doi: 10.1109/tgrs.2021.3100455.
- Zhang, C., Frogner, C., Araya-Polo, M., and Hohl, D., 2014, Machine-learning based automated fault detection in seismic traces, In 76th EAGE Conference and Exhibition 2014, European Association of Geoscientists & Engineers, 1-5. doi: 10.3997/2214-4609.20141500.
- 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 Transactions on Image Processing, 26(7), 3142-3155. doi: 10.1109/TIP.2017.2662206.
- Zhou, C., and Brown, S., 2020, Automatic velocity model building with machine learning, In SEG Technical Program Expanded Abstracts 2020, Society of Exploration Geophysicists, 1596-1600. doi: 10.1190/segam2020-3427836.1.