Articles | Volume 19, issue 16
https://doi.org/10.5194/gmd-19-7961-2026
https://doi.org/10.5194/gmd-19-7961-2026
Development and technical paper
 | 
26 Aug 2026
Development and technical paper |  | 26 Aug 2026

LFD (v1.0): latent-compression-free generative diffusion with geological priors and geophysical regularization for implicit structural modeling

Zhixiang Guo, Xinming Wu, Yimin Dou, Hui Gao, and Guillaume Caumon

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Cited articles

Alemi, A. A., Fischer, I., Dillon, J. V., and Murphy, K.: Deep variational information bottleneck, arXiv [preprint], https://doi.org/10.48550/arXiv.1612.00410, 2016. a
Arienti, G., Bistacchi, A., Caumon, G., Monopoli, B., and Dal Piaz, G.: 3D structural implicit modelling of folded metamorphic units at Lago di Cignana with uncertainty assessment, J. Struct. Geol., 191, 105329, https://doi.org/10.1016/j.jsg.2024.105329, 2025. a
Belhachmi, A., Benabbou, A., and Mourrain, B.: A spline-based regularized method for the reconstruction of complex geological models, Math. Geosci., 57, 89–114, https://doi.org/10.1007/s11004-024-10149-2, 2025. a, b
Bi, Z., Wu, X., Geng, Z., and Li, H.: Deep relative geologic time: A deep learning method for simultaneously interpreting 3-D seismic horizons and faults, J. Geophys. Res.-Sol. Ea., 126, e2021JB021882, https://doi.org/10.1029/2021JB021882, 2021. a
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. a, b
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Short summary
We present a fast way to generate subsurface structure models from seismic surveys while honoring known horizons and faults. Instead of compressing the data into a hidden representation, our method works directly with the original model values and applies geological constraints during generation. Tests on synthetic and real surveys show more realistic structures and efficient prediction, producing a 512 by 512 model in 1.56 seconds on an NVIDIA H20 graphics processing unit.
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