Articles | Volume 17, issue 1
https://doi.org/10.5194/se-17-155-2026
© 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/se-17-155-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Quantitative comparison of three-dimensional bodies using geometrical properties to validate the dissimilarity of a standard collection of 3D geomodels
Friedrich Carl
CORRESPONDING AUTHOR
Chair of Engineering Geology and Hydrogeology, RWTH Aachen University, Lochnerstr. 4–20, 52064 Aachen, Germany
Jian Yang
Chair of Computational Geoscience, Geothermics and Reservoir Geophysics, RWTH Aachen University, Mathieustr. 30, 52074 Aachen, Germany
Marlise Colling Cassel
Chair of Geology and Sedimentary Systems and Geological Institute, RWTH Aachen University, Wüllnerstr. 2, 52062 Aachen, Germany
Florian Wellmann
Chair of Computational Geoscience, Geothermics and Reservoir Geophysics, RWTH Aachen University, Mathieustr. 30, 52074 Aachen, Germany
Fraunhofer Research Institution for Energy Infrastructures and Geotechnologies IEG, Aureliusstr. 2, 52064 Aachen, Germany
Peter Achtziger-Zupančič
Fraunhofer Research Institution for Energy Infrastructures and Geotechnologies IEG, Aureliusstr. 2, 52064 Aachen, Germany
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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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We present curlew, an open-source Python tool for constructing 3D geological models using machine learning. It integrates diverse spatial data and structural observations into a flexible, event-based framework. Curlew captures complex features like folds and faults, handles uncertainty, and supports learning from sparse or unlabelled data. We demonstrate its capabilities on synthetic and real-world examples.
Denise Degen, Moritz Ziegler, Oliver Heidbach, Andreas Henk, Karsten Reiter, and Florian Wellmann
Solid Earth, 16, 477–502, https://doi.org/10.5194/se-16-477-2025, https://doi.org/10.5194/se-16-477-2025, 2025
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Obtaining reliable estimates of the subsurface state distributions is essential to determine the location of, e.g., potential nuclear waste disposal sites. However, providing these is challenging since it requires solving the problem numerous times, yielding high computational cost. To overcome this, we use a physics-based machine learning method to construct surrogate models. We demonstrate how it produces physics-preserving predictions, which differentiates it from purely data-driven approaches.
Raphael Burchartz, Timo Seemann, Garri Gaus, Lisa Winhausen, Mohammadreza Jalali, Brian Mutuma Mbui, Sebastian Grohmann, Linda Burnaz, Marlise Colling Cassel, Jochen Erbacher, Ralf Littke, and Florian Amann
EGUsphere, https://doi.org/10.5194/egusphere-2025-579, https://doi.org/10.5194/egusphere-2025-579, 2025
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In Germany, claystones are studied for their suitability as host-rocks for the disposal of high-level radioactive waste. The MATURITY project systematically investigates how gradual burial affects key barrier properties in the Lower Jurassic Amaltheenton Formation of the Lower Saxony Basin (Germany). Understanding these changes helps assess claystone suitability for long-term waste isolation, improving site selection for deep geological repositories.
Peter Achtziger-Zupančič, Alberto Ceccato, Alba Simona Zappone, Giacomo Pozzi, Alexis Shakas, Florian Amann, Whitney Maria Behr, Daniel Escallon Botero, Domenico Giardini, Marian Hertrich, Mohammadreza Jalali, Xiaodong Ma, Men-Andrin Meier, Julian Osten, Stefan Wiemer, and Massimo Cocco
Solid Earth, 15, 1087–1112, https://doi.org/10.5194/se-15-1087-2024, https://doi.org/10.5194/se-15-1087-2024, 2024
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We detail the selection and characterization of a fault zone for earthquake experiments in the Fault Activation and Earthquake Ruptures (FEAR) project at the Bedretto Lab. FEAR, which studies earthquake processes, overcame data collection challenges near faults. The fault zone in Rotondo granite was selected based on geometry, monitorability, and hydro-mechanical properties. Remote sensing, borehole logging, and geological mapping were used to create a 3D model for precise monitoring.
Denise Degen, Daniel Caviedes Voullième, Susanne Buiter, Harrie-Jan Hendricks Franssen, Harry Vereecken, Ana González-Nicolás, and Florian Wellmann
Geosci. Model Dev., 16, 7375–7409, https://doi.org/10.5194/gmd-16-7375-2023, https://doi.org/10.5194/gmd-16-7375-2023, 2023
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In geosciences, we often use simulations based on physical laws. These simulations can be computationally expensive, which is a problem if simulations must be performed many times (e.g., to add error bounds). We show how a novel machine learning method helps to reduce simulation time. In comparison to other approaches, which typically only look at the output of a simulation, the method considers physical laws in the simulation itself. The method provides reliable results faster than standard.
Michael Hillier, Florian Wellmann, Eric A. de Kemp, Boyan Brodaric, Ernst Schetselaar, and Karine Bédard
Geosci. Model Dev., 16, 6987–7012, https://doi.org/10.5194/gmd-16-6987-2023, https://doi.org/10.5194/gmd-16-6987-2023, 2023
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Neural networks can be used effectively to model three-dimensional geological structures from point data, sampling geological interfaces, units, and structural orientations. Existing neural network approaches for this type of modelling are advanced by the efficient incorporation of unconformities, new knowledge inputs, and improved data fitting techniques. These advances permit the modelling of more complex geology in diverse geological settings, different-sized areas, and various data regimes.
Mohammad Moulaeifard, Simon Bernard, and Florian Wellmann
Geosci. Model Dev., 16, 3565–3579, https://doi.org/10.5194/gmd-16-3565-2023, https://doi.org/10.5194/gmd-16-3565-2023, 2023
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In this work, we propose a flexible framework to generate and interact with geological models using explicit surface representations. The essence of the work lies in the determination of the flexible control mesh, topologically similar to the main geological structure, watertight and controllable with few control points, to manage the geological structures. We exploited the subdivision surface method in our work, which is commonly used in the animation and gaming industry.
Michał P. Michalak, Lesław Teper, Florian Wellmann, Jerzy Żaba, Krzysztof Gaidzik, Marcin Kostur, Yuriy P. Maystrenko, and Paulina Leonowicz
Solid Earth, 13, 1697–1720, https://doi.org/10.5194/se-13-1697-2022, https://doi.org/10.5194/se-13-1697-2022, 2022
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When characterizing geological/geophysical surfaces, various geometric attributes are calculated, such as dip angle (1D) or dip direction (2D). However, the boundaries between specific values may be subjective and without optimization significance, resulting from using default color palletes. This study proposes minimizing cosine distance among within-cluster observations to detect 3D anomalies. Our results suggest that the method holds promise for identification of megacylinders or megacones.
Alexander Schaaf, Miguel de la Varga, Florian Wellmann, and Clare E. Bond
Geosci. Model Dev., 14, 3899–3913, https://doi.org/10.5194/gmd-14-3899-2021, https://doi.org/10.5194/gmd-14-3899-2021, 2021
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Uncertainty is an inherent property of any model of the subsurface. We show how geological topology information – how different regions of rocks in the subsurface are connected – can be used to train uncertain geological models to reduce uncertainty. More widely, the method demonstrates the use of probabilistic machine learning (Bayesian inference) to train structural geological models on auxiliary geological knowledge that can be encoded in graph structures.
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Short summary
A method for shape quantification based on geometrical parameters is proposed alongside a set of regular geometries established as geomodeling benchmarks. Dimensions, gradient and curvature data is obtained on cross-sections. Data analyses provide insight into the main geometrical characteristics of the benchmark models and visualizes geometrical dis-/similarities between bodies. The method and benchmarks are usable in geomodeling workflows and structural comparisons based on sparse data.
A method for shape quantification based on geometrical parameters is proposed alongside a set of...