Articles | Volume 19, issue 17
https://doi.org/10.5194/gmd-19-8407-2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
10 Sep 2026
Development and technical paper |
| 10 Sep 2026
| 10 Sep 2026
Three-dimensional geological modeling based on dual-task stratigraphy-aware attention networks (Geo-SAN v1.0)
Related authors
Geosci. Model Dev., 16, 3651–3674, 2023
Cited articles
Alcalde, J., Bond, C. E., Johnson, G., Butler, R. W., Cooper, M. A., and Ellis, J. F.: The importance of structural model availability on seismic interpretation, J. Struct. Geol., 97, 161–171, 2017.
Bergen, K. J., Johnson, P. A., de Hoop, M. V., and Beroza, G. C.: Machine learning for data-driven discovery in solid earth geoscience, Science, 363, eaau0323, https://doi.org/10.1126/science.aau0323, 2019.
Bi, Z., Wu, X., Li, Z., Chang, D., and Yong, X.: DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network, Geosci. Model Dev., 15, 6841–6861, https://doi.org/10.5194/gmd-15-6841-2022, 2022.
Caumon, G., Collon-Drouaillet, P., Le Carlier de Veslud, C., Viseur, S., and Sausse, J.: Surface-Based 3D Modeling of Geological Structures, Math. Geosci., 41, 927–945, https://doi.org/10.1007/s11004-009-9244-2, 2009.
Chen, Z., Badrinarayanan, V., Lee, C.-Y., and Rabinovich, A.: Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks, International conference on machine learning, 794–803, PMLR, https://proceedings.mlr.press/v80/chen18a.html (last access: 6 September 2026), 2018.
