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The geological structures resulting from the mechanisms of the salt tectonics, especially at the level of the passive continental margins, are known to be of good reservoir of energy resources. The main difficulty is to recognize structures associated. Geological map could allows to recognize these forms / structures by interpretation of the unit geology and structure in presence. This step always requires an interpretation by a structural geologist expert. This leads to very subjective and highly variable renderings. Recently, computer-based methods of machine learning (in particular deep learning branch for image analysis) make it possible to automatically extract the relevant characteristics over large amounts of data. These methods can be applied to geoscientific problems and in particular to the automatic recognition of complex geological structures from relevant descriptive variables: eg. geological information (rock type, formation type, structural elements, topology ...).
Contacts: Prof. Jean Paul Callot (jean-paul.callot[a]univ-pau.fr ) ; Christelle Loiselet (c.loiselet[a]brgm.fr) et Vincent Labbé (v.labbe[a]brgm.fr )
The candidate should hold:
. a PhD in Applied Mathematics or Computer Science with a background in Data Science and a strong interest for geology or observational sciences
. a PhD in Earth Sciences (tectonics or geophysics) with a background in numerical approaches, geological data processing and data sciences approaches.
Programming skills are essential to carry out the expected digital developments. Knowledge of geological and geophysical data to characterize geological structures is expected. The taste for field geology / field observation will also be appreciated as well as a good capacity for synthesis and writing. The candidate should be motivated by research work. He/she should be autonomous in his/her work and have a rigorous approach of scientific issues. Fluent English is necessary to publish his/her results in international publications.
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