Articles | Volume 16, issue 12
https://doi.org/10.5194/gmd-16-3565-2023
© Author(s) 2023. 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-16-3565-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
PySubdiv 1.0: open-source geological modeling and reconstruction by non-manifold subdivision surfaces
Mohammad Moulaeifard
CORRESPONDING AUTHOR
Computational Geoscience and Reservoir Engineering
Department, RWTH Aachen University, 52062 Aachen, Germany
Simon Bernard
Computational Geoscience and Reservoir Engineering
Department, RWTH Aachen University, 52062 Aachen, Germany
Florian Wellmann
Computational Geoscience and Reservoir Engineering
Department, RWTH Aachen University, 52062 Aachen, Germany
Related authors
No articles found.
Friedrich Carl, Peter Achtziger-Zupančič, Jian Yang, Marlise Colling Cassel, and Florian Wellmann
EGUsphere, https://doi.org/10.5194/egusphere-2025-3203, https://doi.org/10.5194/egusphere-2025-3203, 2025
This preprint is open for discussion and under review for Solid Earth (SE).
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Stephanie Thiesen, Diego M. Vieira, Mirko Mälicke, Ralf Loritz, J. Florian Wellmann, and Uwe Ehret
Hydrol. Earth Syst. Sci., 24, 4523–4540, https://doi.org/10.5194/hess-24-4523-2020, https://doi.org/10.5194/hess-24-4523-2020, 2020
Short summary
Short summary
A spatial interpolator has been proposed for exploring the information content of the data in the light of geostatistics and information theory. It showed comparable results to traditional interpolators, with the advantage of presenting generalization properties. We discussed three different ways of combining distributions and their implications for the probabilistic results. By its construction, the method provides a suitable and flexible framework for uncertainty analysis and decision-making.
Cited articles
Börner, J. H., Bär, M., and Spitzer, K.: Electromagnetic methods
for exploration and monitoring of enhanced geothermal systems – a virtual
experiment, Geothermics, 55, 78–87, https://doi.org/10.1016/j.geothermics.2015.01.011, 2015.
Botsch, M., Kobbelt, L., Pauly, M., Alliez, P., and Lévy, B.: Polygon
mesh processing, CRC press, https://doi.org/10.1201/b10688,
2010.
Cashman, T. J.: NURBS-compatible subdivision surfaces, BCS Learning & Development Limited, ISBN 1906124825, 9781906124823,
2010.
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.
De Kemp, E. A.: Visualization of complex geological structures using 3-D
Bézier construction tools, Comput. Geosci., 25, 581–597,
https://doi.org/10.1016/S0098-3004(98)00159-9, 1999.
de la Varga, M., Schaaf, A., and Wellmann, F.: GemPy 1.0: open-source stochastic geological modeling and inversion, Geosci. Model Dev., 12, 1–32, https://doi.org/10.5194/gmd-12-1-2019, 2019.
De Paor, D. G.: Bézier curves and geological design, in: Computer
methods in the geosciences, Elsevier, 389–417, https://doi.org/10.1016/S1874-561X(96)80031-9, 1996.
DeRose, T., Kass, M., and Truong, T.: Subdivision surfaces in character
animation, Proceedings of the 25th annual conference on Computer graphics
and interactive techniques, 19–24 July 1998, Orlando, Florida, United States of America, 85–94, https://doi.org/10.1145/280814.280826, 1998.
Farin, G. and Hamann, B.: Current trends in geometric modeling and selected
computational applications, J. Comput. Phys., 138, 1–15,
https://doi.org/10.1006/jcph.1996.5621, 1997.
Freymark, J., Scheck-Wenderoth, M., Bär, K., Stiller, M., Fritsche,
J.-G., Kracht, M., and Gomez Dacal, M. L.: 3D-URG: 3D gravity constrained
structural model of the Upper Rhine Graben, GFZ Data Services [data set], https://doi.org/10.5880/GFZ.4.5.2020.004, 2020.
Halstead, M., Kass, M., and DeRose, T.: Efficient, fair interpolation using
Catmull-Clark surfaces, Proceedings of the 20th annual conference on
Computer graphics and interactive techniques, 1–6 August 1993, Anaheim, California, United States of America, 35–44, https://doi.org/10.1145/166117.166121, 1993.
Hoppe, H., DeRose, T., Duchamp, T., Halstead, M., Jin, H., McDonald, J.,
Schweitzer, J., and Stuetzle, W.: Piecewise smooth surface reconstruction,
Proceedings of the 21st annual conference on Computer graphics and
interactive techniques, 24–29 July 1994, Orlando, Florida, United States of America, 295–302, https://doi.org/10.1145/192161.192233, 1994.
Jacquemyn, C., Jackson, M. D., and Hampson, G. J.: Surface-based geological
reservoir modelling using grid-free NURBS curves and surfaces, Math.
Geosci., 51, 1–28, https://doi.org/10.1007/s11004-018-9764-8, 2019.
Kälberer, F., Nieser, M., and Polthier, K.: Quadcover-surface
parameterization using branched coverings, Computer graphics forum, 26, 375–384,
https://doi.org/10.1111/j.1467-8659.2007.01060.x, 2007.
Kennedy, J. and Eberhart, R.: Particle swarm optimization, Proceedings of
ICNN'95-international conference on neural networks, 27 November–1 December 1995, Perth, WA, Australia, 1942–1948, https://doi.org/10.1109/ICNN.1995.488968, 1995.
Lavoué, G., Dupont, F., and Baskurt, A.: A framework for quad/triangle
subdivision surface fitting: Application to mechanical objects, Computer
Graphics Forum, 26, 1–14, https://doi.org/10.1111/j.1467-8659.2007.00930.x, 2007.
Lévy, B. and Mallet, J.-L.: Discrete smooth interpolation: Constrained
discrete fairing for arbitrary meshes, ACM Transactions on Graphics, 8, 121–144, https://doi.org/10.1145/62054.62057, 1999.
Loop, C.: Smooth subdivision surfaces based on triangles, Department of Mathematics, University of Utah, 1987.
Ma, X., Keates, S., Jiang, Y., and Kosinka, J.: Subdivision surface fitting
to a dense mesh using ridges and umbilics, Comput. Aided Geom. D.,
32, 5–21, https://doi.org/10.1016/j.cagd.2014.10.001, 2015.
Mallet, J.-L.: Geomodeling, Oxford University Press, Oxford University Press Inc, ISBN-10 0195144600, ISBN-13 978-0195144604, 2002.
Marinov, M. and Kobbelt, L.: Optimization methods for scattered data
approximation with subdivision surfaces, Graphical Models, 67, 452-473,
https://doi.org/10.1016/j.gmod.2005.01.003, 2005.
Miranda, L. J.: PySwarms: a research toolkit for Particle Swarm Optimization
in Python, J. Open Source Softw., 3, 433, https://doi.org/10.21105/joss.00433, 2018.
Moulaeifard, M., Wellmann, F., Bernard, S., de la Varga, M., and Bommes, D.:
Subdivide and Conquer: Adapting Non-Manifold Subdivision Surfaces to
Surface-Based Representation and Reconstruction of Complex Geological
Structures, Math. Geosci., 55, 81–111, https://doi.org/10.1007/s11004-022-10017-x, 2023.
Paluszny, A., Matthäi, S. K., and Hohmeyer, M.: Hybrid finite
element–finite volume discretization of complex geologic structures and a
new simulation workflow demonstrated on fractured rocks, Geofluids, 7,
186–208, https://doi.org/10.1111/j.1468-8123.2007.00180.x,
2007.
Peters, J.: Point-augmented biquadratic C1 subdivision surfaces, Graphical
models, 77, 18–26, https://doi.org/10.1016/j.gmod.2014.10.003,
2015.
Powell, M. J.: An efficient method for finding the minimum of a function of
several variables without calculating derivatives, Computer J., 7,
155–162, https://doi.org/10.1093/comjnl/7.2.155, 1964.
Reif, U.: A unified approach to subdivision algorithms near extraordinary
vertices, Comput. Aided Geom. D., 12, 153–174, https://doi.org/10.1016/0167-8396(94)00007-F, 1995.
Rossignac, J. and Cardoze, D.: Matchmaker: Manifold Breps for non-manifold
r-sets, Proceedings of the fifth ACM symposium on Solid modeling and
applications, 1 June 1999, Ann Arbor Michigan USA, 31–41, https://doi.org/10.1145/304012.304016, 1999.
Sederberg, T. W., Finnigan, G. T., Li, X., Lin, H., and Ipson, H.:
Watertight trimmed NURBS, ACM Transactions on Graphics (TOG), 27, 1–8,
https://doi.org/10.1145/1360612.1360678 2008.
SimBe-hub and MohammadCGRE:SimBe-hub/PySubdiv: PySubdiv (v1.0.0), Zenodo [code and data set], https://doi.org/10.5281/zenodo.6878051, 2022.
Stam, J.: Evaluation of loop subdivision surfaces, SIGGRAPH'98 CDROM
Proceedings, 19–24 July 1998, Orlando, Florida, United States of America, Corpus ID: 8420692, 85–94, 1998.
Sullivan, C. and Kaszynski, A.: PyVista: 3D plotting and mesh analysis
through a streamlined interface for the Visualization Toolkit (VTK), J. Open Source Softw., 4, 1450, https://doi.org/10.21105/joss.01450, 2019.
Suzuki, H., Takeuchi, S., and Kanai, T.: Subdivision surface fitting to a
range of points, Proceedings. Seventh Pacific Conference on Computer
Graphics and Applications (Cat. No. PR00293), 158–167, https://doi.org/10.1109/PCCGA.1999.803359, 1999.
Van Der Walt, S., Colbert, S. C., and Varoquaux, G.: The NumPy array: a
structure for efficient numerical computation, Comput. Sci.
Eng., 13, 22–30, https://doi.org/10.1109/MCSE.2011.37,
2011.
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., and Bright, J.:
SciPy 1.0: fundamental algorithms for scientific computing in Python, Nature
Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020.
Wellmann, F. and Caumon, G.: 3-D Structural geological models: Concepts,
methods, and uncertainties, Adv. Geophys., 59, 1–121,
https://doi.org/10.1016/bs.agph.2018.09.001, 2018.
Wu, X., Zheng, J., Cai, Y., and Li, H.: Variational reconstruction using
subdivision surfaces with continuous sharpness control, Computational Visual
Media, 3, 217–228, https://doi.org/10.1007/s41095-017-0088-2, 2017.
Ying, L. and Zorin, D.: Nonmanifold subdivision, Proceedings Visualization,
VIS'01, 21–26 October 2001, San Diego California, 325–569, https://doi.org/10.1109/VISUAL.2001.964528,
2001.
Short summary
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.
In this work, we propose a flexible framework to generate and interact with geological models...