# 3D Geomodelling experience at the BRGM (French Geological Survey)

Philippe Calcagno

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Philippe Calcagno. 3D Geomodelling experience at the BRGM (French Geological Survey). 3D Geological Mapping Workshop, The Geological Society of America, Sep 2024, Anaheim (USA), France. hal-04834480

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# 3D Geomodelling experience at the BRGM (French Geological Survey)

Philippe Calcagno and the 3D geomodelling team

[pCALCAGNO@BRGM.fr](mailto:p.calcagno@brgm.fr)

## Introduction

BRGM, the French geological survey, is France's leading public institution for Earth Science applications in the management of surface and sub-surface resources towards sustainable development. Under partnerships with numerous public and private stakeholders, BRGM focuses on scientific research, expertise and innovation. Its activity meets four objectives:

- Understanding geological phenomena and related risks,

- Developing new techniques and methodologies,

- Producing and distributing data for surface, subsurface and resource management,

- Providing the tools required to manage the surface, subsurface and resources, prevent risks and pollution, and manage policies in response to climate change.

Our missions as a Geological Survey benefit from fifty years of experience from field mapping to quantitative and predictive geosciences. One of BRGM's main roles is to produce and disseminate 3D geological information on the subsurface (Courrioux et al., 2016; Lopez et al., 2017). Outcrop and borehole observation data, geological maps, geophysical surveys, and any other data giving information on the geological structures are used to construct BRGM's geomodels. They are produced for various applications such as tectonostratigraphy (Maxelon and Mancktelow, 2005), urban planning (Bourgine et al., 2008), water resources (Lacquement et al., 2011, Figure 1; Pennequin et al., 2017), geothermal energy (Calcagno et al., 2022; Mas et al., 2022), and mineral resources (Pochon et al., 2023).

<figure><img src="image_2.png" /><figcaption>Figure 1: 3D geological model of the Givet zone (France) for water resources management.
Excerpt from Lacquement et al. (2011).</figcaption></figure>

BRGM is investing in the development of 3D geological modelling methods as well. The goal is to produce models as much realistic and accurate as needed by the final applications. We need to take into account as much as possible the full range of available data and information to improve our geomodels, but also the geological processes driving the underground. In addition, we pay a close attention in making interdisciplinary 3D geological interpretations.

## Original methodologies and packages for 3D modelling

Georges Matheron, who spent a part of his carrier at BRGM, was a pioneer in geostatistics in the 60's. His work (Matheron, 1989) has deeply driven the setup of geological modelling techniques (Chilès and Delfiner, 1999), often co-constructed with MINES Paris Tech (Renard et al., 2019). Based on these techniques, BRGM was the first geological survey organisation to develop in-house geological modelling tools that are still in use in various countries.

One of the most famous software in that lineage is GDM (Geological Data Management). It served in the middle of the 80's to optimise the geological reconnaissance campaigns of the Channel Tunnel and helped the design
of its route. GDM is based on a powerful geostatistical toolbox dedicated to the interpolation of elevation surfaces and thicknesses in an explicit way. It produces 2.5D models especially efficient in multilayer geological contexts. GDM is also a powerful tool to manage borehole data and cross-validate them.

Another emblematic software developed by BRGM is GeoModeller. In the 90's, it was born from the will to transpose geologists classical representation tools into the numerical world. The goal was to work with geological maps, cross-sections and boreholes in a computer environment and to interpolate all these data in an implicit 3D model. The process is based on a potential field interpolation, initially developed in 2D (Lajaunie et al., 1997). It was generalised a few years later in 3D and improved with a geological pile concept to manage various potential fields (Calcagno et al., 2008, Figure 2). The potential field interpolation method allows the estimation of uncertainties in theoretical (Aug, 2004) and empirical (Courrioux et al., 2015) ways. In addition, capabilities for gravimetric and magnetic susceptibility forward and inverse computations were added to GeoModeller (Guillen et al., 2008).

<figure><img src="image_3.png" /><figcaption>(b) Formation 2 interpolated using an Onlap relation and data for potential field of formation 3.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>(a) Interpolated Formation 1 (basement) and data for potential field of Formation 2.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>(c) Formation 3 interpolated using an Erode relation.</figcaption></figure>

## Interdisciplinary approach

The way in which geomodelling tools are used plays a role in the validity and robustness of the geomodels. Particularly, the integration of data and information coming from various scientific fields, enabled by geomodelling is crucial to produce quality models. Using these data in an interdisciplinary approach is powerful to set up a common interpretation (Hollis et al., 2024). If the construction of the model is interactive and allow on the fly adjustments, the process can be used as a collaborative platform to exchange and debate among the specialists of the disciplines feeding the model.

Usually, data are aggregated in a workflow process (Maxelon et al., 2009). For instance, data and information from geology, gravimetry, magnetotellurics, and geochemistry can be combined to construct a geomodel. Typically, in a workflow, the contributions from each of these four scientific fields are added one after the other in a sequential way. Associating data in a workflow methodology satisfies the forecasted objective of combining multi-discipline data. However, these disciplines are used chronologically and independently; making difficult common and crossed reasoning.

Merging the contributions from the scientific fields instead of simply aggregating them is a step forward. The interpretation of a given discipline may benefit from the input of another discipline, with no chronological preference in the process. For instance, the geological interpretation could benefit from the magnetotellurics interpretation or vice versa. In that case, the geomodel is no more only the final result of a sequential data integration but is the main and central object of the interpretation process (Calcagno, 2015; Figure 3).
<figure><img src="image_6.png" /></figure>

Figure 3: The 3D geomodel is the central part of the interpretation. Scientific fields cooperate and interact to produce a common interpretation.

## Tackling the next challenges

Producing 3D geomodels better adapted to the final application, e.g. mineral resources exploration or risk mitigation is one of the main challenges at BRGM. It requires to take into account the geological context specificities and an increasing number and types of data and information. We need to keep R&D capacities to propose new solutions able to overcome the obstacles we encounter in the geomodelling process. Our in-house historical tools were the place to develop new methodologies and turn them into operational functionalities. However, these packages were designed as monolithic tools offering few options for combining capacities from one to the other. On top of that, their technological debt has increased and they have become increasingly difficult to maintain.

Consequently, we have started a transition phase to move from our historical packages to codes that are more versatile. The first step is to make the original methodologies, we developed over the years, independent and combinable via an integrative platform. This modular approach allows the mix with other codes, e.g. open source, to construct a 3D geomodel. As an example, geological surfaces, each one interpolated by the most appropriated algorithm depending on the nature of the surface, can be assembled to complete the model. We have developed a set of modules embedded in QGIS plugins. This brings the GIS, commonly used by geologists, and 3D modelling ways of working closer together (Figure 4).

<figure><img src="image_7.png" /></figure>

Figure 4: 3D scene plugin in QGIS combining geology and anthropic tunnels (Janvier, 2023).

In a second step, we intend to go further in leading edge R&D to fulfil better the needs of the final application but also to improve the computation performance in the modelling process. New methodologies to tackle fault offset or to consider geophysical inversion remains to be set up. The ability to operate in 3D when integrating data and working on the interpretation, beyond classical 2D maps and cross-sections, would improve the user experience. On top of that, being able to perform 3D modelling on the field would be a step forward in the interaction with the outcrop during fieldwork.
## References

Aug, C. 2004. Modélisation géologique 3D et caractérisation des incertitudes par la méthode du champ de potentiel. Thèse de doctorat. Ecole des Mines de Paris.

Bourgine, B., Leparmentier, A.-M., Lembezat, C., Thierry, P., Luquet, C., et al. 2008. Tools and methods constructing 3D geological models in the urban environment. The Paris case. Proceeding of the Eighth international Geostatistics congress, J.M. Ortiz and X. Emery Editors, Vol2, pp 951-960.

Calcagno, P., Chilès, J. P., Courrioux, G. & Guillen, A. 2008. Geological modelling from field data and geological knowledge: Part I. Modelling method coupling 3D potential-field interpolation and geological rules. Phys. Earth Planet. Inter. 171, 147-157. https://doi.org/10.1016/j.pepi.2008.06.013.

Calcagno P. 2015. 3D GeoModelling for a Democratic Geothermal Interpretation, In Proceedings of World Geothermal Congress 2015 (WGC2015), Melbourne, Australia, 19-25 April 2015. 7 p.

Calcagno, P., Trumpy, E., Gutiérrez-Negrín, L. C. & Liotta, D. 2022. A collection of 3D geomodels of the Los Humeros and Acoculco geothermal systems (Mexico). Sci. Data 9, 280. https://doi.org/10.1038/s41597-022-01327-0.

Chilès, J.-P., and Delfiner, P. 1999. Geostatistics: Modeling spatial uncertainty. Wiley.

Courrioux, G., Allanic, C., Bourgine, B., Guillen, A., Baudin, T., Lacquement, F., Gabalda, S., Cagnard, F., Le Bayon, B., Besse, J., Marquer, D., Trap, P., Leloup, P. H., Schreiber, D. 2015. Comparisons from multiple realizations of a geological model: Implication for uncertainty factors identification. In Proceedings of IAMG 2015. Freiberg (Saxony), Germany.

Courrioux, G., Lopez, S., Allanic, C., Gabalda, S., & Bourgine, B. 2016. From 2D maps to 3D modelling: much more than "just one more dimension." In 3rd European meeting on 3D geological modelling. Wiesbaden.

Guillen, A., Calcagno, Ph., Courrioux, G., Joly, A. & Ledru, P. 2008. Geological modelling from field data and geological knowledge. Phys. Earth Planet. Inter. 171, 158-169. https://doi.org/10.1016/j.pepi.2008.06.014.

Hollis, J., Calcagno, P., Bertrand, G., de Oliveira, D., Negrel, P., Díaz-Martínez, E., La Vigna, F., Poyiadji, E., Tonné, N., van Heteren, S., Dakin, N., Hinsby, K., van der Keur, P., Siddiqi, G., Čápová, D., Pizzocolo F. 2024. A Geological Service for Europe - building trust through interdisciplinary and intersectoral collaboration. European Geologist, issue 57. In press.

Janvier, T. 2023. Modélisation 3D du Bassin Ferrifère Lorrain : Généralisation des démontrateurs à toutes les communes du bassin de Briey. Report. BRGM/RP-AP23DRP505-FR.

Lacquement, F., Courrioux, G., Ortega, C., Thuon, Y. 2011. Réalisation du modèle géologique 3D de la pointe de Givet (08) - Etape 1 : Caractérisation des potentialités d'exploitation des eaux souterraines. Rapport BRGM/RP-60384-FR.

Lajaunie, C., Courrioux, G., & Manuel, L. (1997). Foliation Fields and 3D Cartography in Geology: Principles of a Method Based on Potential Interpolation. Mathematical Geology, 29(4), 571-584.

Lopez, S., Allanic, C., Courrioux, G., Bourgine, B., Calcagno, P., Caritg, S., Gabalda, S. 2017. La Modélisation géologique 3D : un outil pour la cartographie, Revue officielle de la Société géologique de France, 193, pp. 48-53.

Mas, P., Calcagno, P., Caritg-Monnot, S., Beccaletto, L., Capar, L., Hamm, V. 2022. A 3D geomodel of the deep aquifers in the Orléans area of the southern Paris Basin (France). Sci Data 9, 781. https://doi.org/10.1038/s41597-022-01876-4.

Matheron, G. (1989), Estimating and Choosing, an Essay on Probability in Practice, Springer.

Maxelon, M., Mancktelow, N. 2005. Three-dimensional geometry and tectonostratigraphy of the Pennine zone, Central Alps, Switzerland and Northern Italy, Earth-Science Reviews, Volume 71, Issues 3-4, Pages 171-227, ISSN 0012-8252. https://doi.org/10.1016/j.earscirev.2005.01.003.

Maxelon, M., Renard, P., Courrioux, C., Brändlid, M., Mancktelow, N. 2009, A workflow to facilitate three-dimensional geometrical modelling of complex poly-deformed geological units. Computers & Geosciences 35, 644-658.

Pennequin D., David PY., Servière M., Amraoui N., Loiselet C. 2017. Hydro-System Flow Modelling for Water Resources Management in the Fractured and Karstified Chalk Aquifer Environment of Eastern Normandy. In: Renard P., Bertrand C. (eds) EuroKarst 2016, Neuchâtel. Advances in Karst Science. Springer, Cham.

Pochon A., Iacono-Marziano G., Gloaguen E., Tuduri J., Erdmann S. 2023. High-temperature alteration during cooling of mafic intrusions: Insights from the Saint-Jean-du-Doigt intrusive complex (Armorican Massif, France), Lithos, 436-437, DOI:10.1016/j.lithos.2022.106977.

Renard, D., Lajaunie C., Lopez S., Allanic, C., Courrioux, G., Bourgine, B., Calcagno, P. 2019. La géostatistique au service de la modélisation géologique 3D, Annales des Mines - Responsabilité et environnement, 2019/2 (N° 94), p. 30-33. DOI: 10.3917/re1.094.0030. URL : https://www.cairn.info/revue-responsabilite-et-environnement-2019-2-page-30.htm