Comparisons from multiple realizations of a geological model. Implication for uncertainty factors identification

Gabriel Courrioux (1) , Cécile Allanic (1) , Bernard Bourgine (1) , Antonio Guillen (1) , Thierry Baudin (1) , Frédéric Lacquement (1) , Sunsearé Gabalda (1) , Florence Cagnard (1) , Benjamin Le Bayon (1) , Jean Besse (2) , Didier Marquer (3) , Pierre Trap (3) , Philippe-Hervé Leloup (4) , Dimitri Schreiber (5)

1 BRGM - Bureau de Recherches Géologiques et Minières (BRGM)
2 IPGP - Institut de Physique du Globe de Paris
3 UFC - Université de Franche-Comté
4 LGL-TPE - Laboratoire de Géologie de Lyon - Terre, Planètes, Environnement [Lyon]
5 GEOAZUR 6526 - Géoazur

This paper aims at analyzing data and models acquired from different 3D cartography training sessions performed on the same geological objects in carboniferous basins from Alès (France). The objective of the training is to reconcile traditional practice of cartography with 3D Geological modelling technics. Students are faced to the exercise of “classical” geology: quality of observations, lithological facies recognition, structures and microstructures analysis,field data acquisition; as well to the integration of these field data in a 3D Geomodelling system. The geological model and subsequent maps are built in an iterative way by incorporating new data every day. The collective objective is to combine all models from different local areas, in order to obtain a consistent regional model. Through this work students are sensitized to critical analysis of data, their relevance with respect to a modelling objective, up-scaling issues, estimation of uncertainties and model validation. Statistical analysis of data covering the different sessions allow to infer different causes of uncertainty (spatial variability) and to estimate the acquisition data errors (human factors). Each model is considered as one possible realization of a random process. The analysis of differences and resemblances between models is illustrated trough the 3D distribution of statistical parameters as standard-deviations, probabilities, entropy. This allows deciphering the impact of different factors on uncertainty. These include data variability, various field

Mots clés: Cartographie 3D

Domaines: Planète et Univers [physics]; Sciences de la Terre

Gabriel Courrioux, Cécile Allanic, Bernard Bourgine, Antonio Guillen, Thierry Baudin, et al.. Comparisons from multiple realizations of a geological model. Implication for uncertainty factors identification. IAMG 2015 : The 17th annual conference of the International Association for Mathematical Geosciences, IAMG (International Association for Mathematical Geosciences), Sep 2015, Freiberg, Germany. ⟨hal-01150994⟩