The French Geological Survey (BRGM)
BRGM is the French Geological Survey. One of its core missions is producing and disseminating geological information, through database, 3D maps and models for resource, risk managing and underground storage. So describing the geology and properties of geology at depth, and sharing this description is a fundamental objective for BRGM, as for many other geological institutes.
BRGM has invested for long in the development of 3D modelling methods in Research and Development of methods for geological modelling. These methods have progressively moved to Industrialization through « homemade » software (GDM-Multilayer, 3DGeomodeller).
Considering the wide range of applications and geological settings, the choice for own developments allows better flexibility and adaptive capabilities on tools and methods.
Developed 3D/4D modelling software:
GDM Multilayer is based on (co)Kriging of horizons elevation/thickness, accounting for inequalities constraints, allows quality control on data and cartography of uncertainty thanks to a strong geostatistics background (Bourgine, 2008).
It is mostly dedicated to Basin and Alluvium settings and has therefore finds many applications in urban geology, land management, aquifer inventory… (Fig. 1)
3D Geomodeller (© BRGM Intrepid-Geophysics) is based on an original interpolation method that takes into account orientation data and interface acquired in geological units on the field (contacts, stratifications, cleavage). Interpolation is based on Cokriging a 3D potential field and its derivatives (Lajaunie et al., 1997; Aug 2004; Chilès et al., 2004). (fig. 2). A geological model is made-up of by assemblage of different fields with respect to geological and chronogical relationships that allow designing a complete volumic modelling of different geological units (Calcagno et al., 2008). This method is adapted to complex geological settings as folded belts, metamorphic and intrusive contexts (fig. 3).
Taking into account geophysics surveying (gravimetric, magnetics, seismic soundings) are performed by confronting geological model against its geophysical effects (Courrioux et al., 2001), and by using stochastic inversion method (Markov Chain Methods, MMC) (Guillen et al., 2004, 2008, Bosch et al., 2001). This results in a probabilistic distribution of physical properties (densities, magnetic susceptibility, others …) inside geological units (fig. 4).
- RGF (“Référentiel géologique de la France”)
Must Provide an up-to-date and sharable geological information
Must be in 3D
Must contribute to answer scientific questions (scientific needs, geodynamics)
And must also be usable for societal needs (environment, water supplies, urban geology, risk, mineral resources, energy….)
So, Projects must have a strong scientific background but as well strong technologic aspects (data–base, imagery, modelling, web delivery …) (fig. 5).
- Storing and delivering numerical geological models on demand for everyday Earth Sciences applications (SCUDDD project):
We are now faced to managing hundreds of models, which were built for different purposes, at different scales, stored in different formats. There is a big demand for storing/sharing these models and being able to retrieve them in a more institutional way.
Today, every institute develops its own visualizer based on its model description. Considering that no standard is currently yet accepted for 3D geological models, we started following an alternative approach based on a very simple concept:
The idea is to try to escape from definition of standard formats, but rather try to define standard queries on models and standard services around models exploitation.
In this scheme, models are stored in their native formats. For each type of format we have to develop an AP which implements simple geometric queries, called “oracles”:
returns the geological formation for a given point p
returns an interface location in a segment [p1,p2]
With these two “oracles” we make the guess that it is possible to rebuild visualizations and extracts of any model (fig. 6).
Advantage : provides homogeneous services around models independent of native formats :
Predictive drill-holes, sections, 3D views, meshing, maps,
Delivery in interoperable exchange format.
Draw back … Necessity to implement readers and these two functions for each native format.
AUG, C., 2004. Modélisation géologique 3D et caractérisation des incertitudes par la méthode du champ de potentiel. Thèse de doctorat. BRGM – 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. Julian M. Ortiz, Xavier Emery. GEOSTATS 2008, Dec 2008, Santiago, Chile. 2, pp.951-960, 2008
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. Interiors (2008), doi:10.1016/j.pepi.2008.06.013.
Chilès J.P., Aug C., Guillen A., Lees T., 2006.- Modelling the geometry of geological units and its uncertainty in 3D from structural data: The potential-field method. In Orebody Modelling and Strategic Mine Planning - Uncertainty and Risk Management Models, R. Dimitrakopoulos (ed.), Spectrum Series, Vol. 14, The Australasian Institute of Mining and Metallurgy (AusIMM), Carlton, Victoria, Australia, pp. 329-336.
Courrioux, G Nullans, S , Guillen, A., Boissonat, J.D., Repusseau P., Renaud X., Thibaut, M., 2001. Volumetric modelling of Cadomian terranes (Northern Britanny, France): an automatic method using Voronoi diagrams. Tectonophysics, 331 (1-2), 181-196
Guillen, A., Calcagno, P., Courrioux, G., Joly, A., Ledru, P. , 2008. Geological modelling from field data and geological knowledge. Part II. Modelling validation using gravity and magnetic data inversion. Phys. Earth Planet. Interiors (2008), doi:10.1016/j.pepi.2008.06.014
Lajaunie, C., Courrioux, G. and Manuel, L., 1997.Foliation fields and 3d cartography in geology: principles of a method based on potential interpolation: Mathematical Geology 29, 571-584.
Allanic, C., Martel, L., Monod, B., Jacob, T., Courrioux, G., Bailly-Comte, V., Maréchal, J.C., 2017. 3D geological & geophysical modelling of Plateau de Sault (Eastern Pyrenees) for good water management. EAGE 2017, Paris.
Fig. 1: Geological model delivery/query from GIS - GDM_ArcGIS.
Fig. 2: Interpolation of migmatite foliation trajectories using Cokriging of a potential field and its derivatives. 3DGeomodeller software (© BRGM Intrepid-Geophysics).
Fig. 3: Modelling Pyrenean fold/thrust belt from structural data using 3D Geomodeller (Allanic et al. 2017). Geological surfaces and volumes can be extracted from the model. 3DGeomodeller software (© BRGM Intrepid-Geophysics).
Fig. 4: Density distribution obtained by Gravity Inversion – based on a priori geological model in the French Hercynian Vosges Segment (RGF BRGM report). 3DGeomodeller software (© BRGM Intrepid-Geophysics).
Fig. 5: RGF: From data to models: different scales and models for different applications.
Fig. 6: Conceptual scheme of SCUDDD Architecture.
Dr. Gabriel Courrioux
Bureau de Recherces Géologiques et Minieres