MaLISSiA [ANR-JCJC]

Abstract:  

Artificial intelligence and digital twins offer tremendous opportunities for modelling and understanding Earth systems. However, these new developments have mainly been applied to geophysical processes occurring within relatively well-constrained geometries (e.g., on earth surface) and they have yet to make a breakthrough in the characterization of sub-surface architectures, even though they determine the localization of sub-surface Earth processes. The difficulty to access sub-surface causes paramount epistemic uncertainties that result in original scientific locks, e.g., the dependencies to scale, time, and geophysical processes. These limitations are generally counterbalanced by expert interpretations that rely on human learning from outcrops and simulations. The search for more formal and automated solutions is challenging both the formalization of geological knowledge and the development of innovative artificial intelligence methods. The MaLISSiA project draws its inspiration from the geocognitive process developed by human learning and interpretation. It proposes a new paradigm for automatically interpreting and explaining sub-surface architectures. The project proposes to enable the training of machine learning algorithms by developing a corpus of interpreted geological references, based on both natural objects and process-based simulations that reproduce geological history. The concept will be proved for elementary structural objects and, in a more complete example, within the framework of the development of a digital twin of the Vadose Zone Observatory (OZNS).

Timeline:

Oct. 2021 : Recrutement sur la thèse d’Imaddedine Laouici sur la formalisation du processus d’interprétation géologiques

Nov. 2023 : Recrutement sur la thèse de Thibaut Jamey : Application sur la structuration des connaissances et modélisation dans le cadre de l’O-ZNS

Dec 2024: Soutenance de la thèse d’Imaddedine Laouici et Atelier GeoKIFF

2025 : Recrutement Aya Attia pour l’intégration des graphs et de l’apprentissage automatique

fin 2026 : Soutenance Thibaut Jamey