Offre d’Emploi : PhD student – 3D Modelling and IA

Intitulé du poste PhD student - 3D Modelling and IA
Affectation ISTO
Durée du contrat 3 years
Date de début 1/10/2021
Contacts et renseignements gautier.laurent@univ-orleans.fr
Descriptif du poste

Geological knowledge framework and structural interpretation process for building 3D architectures of sub-surface

Processus d'interprétation

Ph.D. thesis - Structural modelling

Three-dimensional representations of sub-surface architectures are key for exploring georessources and quantitatively addressing geoscientific questions (e.g., about tectonics, magmatism). The description of such architectures encompasses a geometrical description of structural objects in a modelled area (e.g., stratigraphic layers, faults, folds) and their spatial and time relationships.

This doctoral project will explore an alternative paradigm for modelling 3D geological architectures of subsurface with an improved formalism of concepts and uncertainties (Fig. 1).  This project aims at (1) improving the numerical formalisation of knowledge and hypotheses embedded in the subsurface representation and (2) improving the characterization of structural uncertainties.

The student in charge of this project will develop an innovative approach that implements the cognitive interpretation process applied by geologists. Its formalisation will rely on:

  • examples gathered in a corpus of natural and simulated objects,
  • a formal description of geological concepts gathered in an ontology of structures, and
  • on an automated interpretation method based on spatiotemporal descriptors.

This new paradigm approaches geomodelling as an automated interpretation process based on artificial intelligence instead of a direct data interpolation. The proposed method will improve the integration of heterogeneous data, scale management, and exploration of epistemic uncertainties, while clarifying the structural concepts embedded in explored architectures.

Profile

We are seeking a student within a Master degree formation with components of either or both structural geosciences and numerical sciences. Candidates should be either:

  • in a math or computer science formation, with skills in data sciences and a taste for geosciences or natural objects; or
  • in a geosciences or geophysics formation with a taste for numerical approaches.

More information

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Clôture des candidatures 20/04/2021
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