Real Data-Driven Seismic Low Frequency Extrapolation: A Case Study From The Asri Basin, Java Sea, Indonesia
Keywords:
Low-Frequency Extrapolation, Self-Supervised Learning, Asri Basin, Full Waveform InversionAbstract
The Asri Basin, located in the Java Sea, Indonesia, is a significant hydrocarbon province with regions that remain underexplored. However, the legacy seismic data available for this area are limited in quality, particularly due to their narrow frequency bandwidth and the absence of low-frequency components. This limitation poses a significant challenge for advanced seismic imaging techniques such as Full Waveform Inversion (FWI), which require low-frequency data to produce accurate and reliable subsurface models. The objective of this study is to reconstruct the missing low-frequency (<10 Hz) components from the band-limited seismic data to improve the viability of FWI applications. To address this challenge, we implement a real-data-driven, self-supervised learning approach for low-frequency extrapolation. Using a modified U-Net architecture, our framework is trained directly on the available band-limited seismic data, eliminating the need for synthetic or labeled datasets. The self-supervised workflow applies a frequency-specific masking strategy to learn and predict the missing low-frequency content from higher-frequency inputs. Our results demonstrate that the proposed method effectively recovers low-frequency signals, outperforming conventional deghosting techniques, while remaining computationally efficient. This approach provides a promising solution for overcoming data limitations and mitigating cycle-skipping issues in FWI applications within the Asri Basin and similar geological settings.
References
Araya‐Polo, M., Jennings, J., Adler, A. & Dahlke, T. 2018, 'Deep-learning tomography', The Leading Edge, 37(1), 58–66, https://doi.org/10.1190/tle37010058.1.
Bunks, C., Saleck, F. M., Zaleski, S., & Chavent, G. 1995, 'Multiscale seismic waveform inversion. Geophysics, 60(5), 1457–1473. https://doi.org/10.1190/1.1443880
Cheng, S., Wang, Y., Zhang, Q., Harsuko, R. & Alkhalifah, T. 2024, 'A self-supervised learning framework for seismic low-frequency extrapolation', Journal of Geophysical Research: Machine Learning and Computation, 1, e2024JH000157, https://doi.org/10.1029/2024JH000157.
Claerbout, J. F., 1992, 'Earth Sounding Analysis: Processing versus Inversion, Blackwell
Fabien‐Ouellet, G. 2020, 'Low‐frequency generation and denoising with recursive convolutional neural networks', in SEG Technical Program Expanded Abstracts 2020, Society of Exploration Geophysicists, pp. 870–874.
Fang, W., Fu, L., Zhang, M. & Li, Z. 2020, 'Seismic data interpolation based on U-Net with texture loss', Geophysics, 86, 1–107, viewed [date], https://doi.org/10.1190/geo2019-0615.1.
Li, J., Wu, X. & Hu, Z. 2021, 'Deep learning for simultaneous seismic image super-resolution and denoising', IEEE Transactions on Geoscience and Remote Sensing, PP, 1–11, https://doi.org/10.1109/TGRS.2021.3057857.
Moran, N., Schmidt, D., Zhong, Y. & Coady, P. 2020, 'Noisier2Noise: Learning to denoise from unpaired noisy data', in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020, pp. 12061–12069, https://doi.org/10.1109/CVPR42600.2020.01208.
Ralanarko, D., Wahyuadi, D., Nugroho, P., Rulandoko, W., Syafri, I., Abdurrokhim, A. & Nur, A. 2021, 'Seismic expression of Paleogene Talangakar Formation - Asri & Sunda Basins, Java Sea, Indonesia', Berita Sedimentologi, 46, 21–43, https://doi.org/10.51835/bsed.2020.46.1.58.
Ronneberger, O., Fischer, P. & Brox, T. 2015, 'U-Net: Convolutional networks for biomedical image segmentation', in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III, vol. 18, pp. 234–241.
Sigalingging, A.S. et al. 2024, 'Application of deep learning for low-frequency extrapolation to marine seismic data in the Sadewa Field, Kutei Basin, Indonesia', in Bezzeghoud, M. et al. (eds), Recent Research on Geotechnical Engineering, Remote Sensing, Geophysics and Earthquake Seismology. MedGU 2022. Advances in Science, Technology & Innovation, Springer, Cham, viewed, https://doi.org/10.1007/978-3-031-48715-6_51.
Sigalingging, A.S., Winardhie, I.S. & Dinanto, E. 2021, 'Ekstrapolasi frekuensi rendah pada Full Waveform Inversion (FWI) dengan menggunakan deep learning. Part 1: Validasi data sintetik', Jurnal Geofisika, 19(2), 74–79.
Sukanto, J., Nunuk, F., Aldrich, J.B., Rinehart, G.P. & Mitchell, J. 1998, 'Petroleum system of the Asri Basin, Java Sea, Indonesia', in Proceedings of the 26th Annual Convention, Indonesian Petroleum Association.
Sun, H. & Demanet, L. 2018, 'Low-frequency extrapolation with deep learning', in SEG Technical Program Expanded Abstracts 2018, Society of Exploration Geophysicists, pp. 2011–2015.
Sun, H. & Demanet, L. 2020, 'Extrapolated full‐waveform inversion with deep learning', Geophysics, 85(3), R275–R288, https://doi.org/10.1190/geo2019‐0195.1.
Sun, Q.F., Xu, J.Y. & Zhang, H.X. 2022, 'Random noise suppression and super-resolution reconstruction algorithm of seismic profile based on GAN', Journal of Petroleum Exploration and Production Technology, 12, 2107–2119, viewed [date], https://doi.org/10.1007/s13202-021-01447-0.
Tarantola, A., 1986, 'A strategy for nonlinear elastic inversion of seismic reflection data', Geophysics, 51(10), 1893–1903, https://doi.org/10.1190/1.1442046.
Virieux, J. & Operto, S., 2009, 'An overview of full‐waveform inversion in exploration geophysics', Geophysics, 74(6), WCC1–WCC26, https://doi.org/10.1190/1.3238367.
Winardhi, S., Sigalingging, A.S., Triyoso, W., Sukmono, S., Dinanto, E., Hendriyana, A., Wardaya, P.D., Septama, E. & Raguwanti, R. 2024, 'Deep learning-based low-frequency extrapolation: Its implication in 2D full waveform imaging for marine seismic data in the Sadewa Field, Indonesia', First Break, 42(7), 65–72, https://doi.org/10.3997/1365-2397.fb2024059.
Witte, P.A., Louboutin, M., Kukreja, N., Luporini, F., Lange, M., Gorman, G.J. & Herrmann, F.J. 2019, 'A large-scale framework for symbolic implementations of seismic inversion algorithms in Julia', Geophysics, 84(3), F57–F71, https://doi.org/10.1190/geo2018-0174.1.
Zhang, Q., Mao, W., Zhou, H., Zhang, H. & Chen, Y. 2018, 'Hybrid-domain simultaneous-source full waveform inversion without crosstalk noise', Geophysical Journal International, 215(3), 1659–1681, https://doi.org/10.1093/gji/ggy366.
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