Articles | Volume 19, issue 16
https://doi.org/10.5194/gmd-19-7961-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-7961-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
LFD (v1.0): latent-compression-free generative diffusion with geological priors and geophysical regularization for implicit structural modeling
Zhixiang Guo
Laboratory of Seismology and Physics of the Earth's Interior, School of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, China
State Key Laboratory of Precision Geodesy, University of Science and Technology of China, Hefei, 230026, China
Mengcheng National Geophysical Observatory, University of Science and Technology of China, Hefei, 230026, China
Université de Lorraine, CNRS, GeoRessources, 54000 Nancy, France
Laboratory of Seismology and Physics of the Earth's Interior, School of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, China
State Key Laboratory of Precision Geodesy, University of Science and Technology of China, Hefei, 230026, China
Mengcheng National Geophysical Observatory, University of Science and Technology of China, Hefei, 230026, China
Yimin Dou
Laboratory of Seismology and Physics of the Earth's Interior, School of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, China
State Key Laboratory of Precision Geodesy, University of Science and Technology of China, Hefei, 230026, China
Mengcheng National Geophysical Observatory, University of Science and Technology of China, Hefei, 230026, China
Laboratory of Seismology and Physics of the Earth's Interior, School of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, China
State Key Laboratory of Precision Geodesy, University of Science and Technology of China, Hefei, 230026, China
Mengcheng National Geophysical Observatory, University of Science and Technology of China, Hefei, 230026, China
Guillaume Caumon
Université de Lorraine, CNRS, GeoRessources, 54000 Nancy, France
Institut Universitaire de France (IUF), Paris, France
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Yimin Dou, Xinming Wu, Hui Gao, and Zhengfa Bi
EGUsphere, https://doi.org/10.48550/arXiv.2605.01273, https://doi.org/10.48550/arXiv.2605.01273, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Our study helps turn three-dimensional seismic images into clear models of underground layer order. We developed an artificial intelligence method that can convert seismic data into a continuous layer sequence model with little or no manual input. Tests on field data show that it follows rock layers more accurately than previous methods, even in folded, faulted, or unclear areas. This can make subsurface interpretation faster, more consistent, and more useful for geological modeling.
Guangyu Wang, Xinming Wu, and Wen Zhang
Earth Syst. Sci. Data, 17, 3447–3471, https://doi.org/10.5194/essd-17-3447-2025, https://doi.org/10.5194/essd-17-3447-2025, 2025
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Seismic paleochannel interpretation is vital for georesource exploration and paleoclimate research yet remains time-consuming. While deep learning offers automation potential, it is limited by the lack of labeled data. We present a workflow to simulate geologically reasonable 3D seismic volumes with diverse paleochannels, generating a large-scale labeled dataset. Field applications demonstrate its effectiveness. The dataset and codes are publicly available to support future research.
Hui Gao, Xinming Wu, Xiaoming Sun, Mingcai Hou, Hang Gao, Guangyu Wang, and Hanlin Sheng
Earth Syst. Sci. Data, 17, 595–609, https://doi.org/10.5194/essd-17-595-2025, https://doi.org/10.5194/essd-17-595-2025, 2025
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We propose three strategies for field seismic data curation, knowledge-guided synthesization, and generative adversarial network (GAN)-based generation to construct a massive-scale, feature-rich, and high-realism benchmark dataset of seismic facies and evaluate its effectiveness in training a deep-learning model for automatic seismic facies classification.
Melchior Schuh-Senlis, Guillaume Caumon, and Paul Cupillard
Solid Earth, 15, 945–964, https://doi.org/10.5194/se-15-945-2024, https://doi.org/10.5194/se-15-945-2024, 2024
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This paper presents the application of a numerical method for restoring models of the subsurface to a previous state in their deformation history, acting as a numerical time machine for geological structures. The method is applied to a model based on a laboratory experiment. The results show that using force conditions in the computation of the deformation allows us to assess the value of some previously unknown physical parameters of the different materials inside the model.
Jérémie Giraud, Guillaume Caumon, Lachlan Grose, Vitaliy Ogarko, and Paul Cupillard
Solid Earth, 15, 63–89, https://doi.org/10.5194/se-15-63-2024, https://doi.org/10.5194/se-15-63-2024, 2024
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We present and test an algorithm that integrates geological modelling into deterministic geophysical inversion. This is motivated by the need to model the Earth using all available data and to reconcile the different types of measurements. We introduce the methodology and test our algorithm using two idealised scenarios. Results suggest that the method we propose is effectively capable of improving the models recovered by geophysical inversion and may be applied in real-world scenarios.
Hui Gao, Xinming Wu, Jinyu Zhang, Xiaoming Sun, and Zhengfa Bi
Geosci. Model Dev., 16, 2495–2513, https://doi.org/10.5194/gmd-16-2495-2023, https://doi.org/10.5194/gmd-16-2495-2023, 2023
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We propose a workflow to automatically generate synthetic seismic data and corresponding stratigraphic labels (e.g., clinoform facies, relative geologic time, and synchronous horizons) by geological and geophysical forward modeling. Trained with only synthetic datasets, our network works well to accurately and efficiently predict clinoform facies in 2D and 3D field seismic data. Such a workflow can be easily extended for other geological and geophysical scenarios in the future.
Zhengfa Bi, Xinming Wu, Zhaoliang Li, Dekuan Chang, and Xueshan Yong
Geosci. Model Dev., 15, 6841–6861, https://doi.org/10.5194/gmd-15-6841-2022, https://doi.org/10.5194/gmd-15-6841-2022, 2022
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We present an implicit modeling method based on deep learning to produce a geologically valid and structurally compatible model from unevenly sampled structural data. Trained with automatically generated synthetic data with realistic features, our network can efficiently model geological structures without the need to solve large systems of mathematical equations, opening new opportunities for further leveraging deep learning to improve modeling capacity in many Earth science applications.
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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.
We present a fast way to generate subsurface structure models from seismic surveys while...