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English Information

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Authors
# Name
1 Juan Pedro Perri Barreto(juanperri@ufrj.br)
2 Lorena Mamede Botelho(lorenamb@cos.ufrj.br)
3 Arick Jurdan dos Reis(arickjurdan.20221@poli.ufrj.br)
4 Claudio Miceli de Farias(cmicelifarias@cos.ufrj.br)

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Reference
# Reference
1 Atolagbe, J. and Koeshidayatullah, A. (2025). Toward user-guided seismic facies inter-pretation with a pre-trained large vision model. IEEE Access, 13:42965–42976.
2 Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. In European Conference on Computer Vision (ECCV), pages 801–818.
3 Chevron U.S.A. Inc. (2020). Labeled geological model of Parihaka seismic data for machine learning. SEG 2020 Annual Meeting Machine Learning Interpretation Workshop. Disponibilizado sob licenc¸a CC BY-SA 4.0; dados sísmicos base fornecidos pela New Zealand Petroleum and Minerals (NZPM).
4 Gao, H., Wu, X., Si, X., Sheng, H., Liu, M., and Wu, H. (2026). A foundation model empowered by a multi-modal prompt engine for universal seismic geobody interpretation across surveys. Information Fusion, 125:103437.
5 Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press, Cambridge, MA. http://www.deeplearningbook.org.
6 Gutierrez, G., Astudillo, C., Napoli, O., Miranda, D., Souza, A., Navarro, J. P., Villas, L., and Borin, E. (2025). On the performance evaluation of deep learning models for seismic facies segmentation. Geophysical Prospecting.
7 Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W. (2022). LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations (ICLR).
8 Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., Doll´ar, P., and Girshick, R. (2023). Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 4015–4026.
9 LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444.
10 Liu, X., Gao, P., Yu, T., Wang, F., and Yuan, R.-Y. (2025). CSWin-UNet: Transformer UNet with cross-shaped windows for medical image segmentation. Information Fusion, 113:102634.
11 Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., Glocker, B., and Rueckert, D. (2018). Attention U-Net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999.
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13 Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 9351 of LNCS, pages 234–241. Springer.
14 Sheng, H., Wu, X., Si, X., Li, J., Zhang, S., and Duan, X. (2025). Seismic foundation model: A next generation deep-learning model in geophysics. Geophysics, 90(2):IM59–IM79.