Authors:
Van Nguyen
1
;
2
;
Dominique Fourer
2
;
Désiré Sidibé
2
;
Jean-François Lecomte
1
and
Souhail Youssef
1
Affiliations:
1
IFPEN, Ruel-Malmaison, France
;
2
IBISC - Univ.Évry Paris-Saclay,Évry-Courcouronnes, France
Keyword(s):
Self-Supervised Learning, Representation Learning, Digital Rock Physics Digital Rock Physics (DRP), Image Classification.
Abstract:
Digital Rock Physics DRP is a discipline that employs advanced computational techniques to analyze and simulate rock properties at the pore-scale level. Recently, Self-Supervised Learning (SSL) has shown promising outcomes in various application domains, but its potential in DRP applications remains largely unexplored. In this study, we propose to assess several self-supervised representation learning methods designed for automatic rock category recognition. Hence, we demonstrate how different SSL approaches can be specifically adapted for DRP, and comparatively evaluated on a new dataset. Our objective is to leverage unlabeled micro-CT (Computed Tomography) image data to train models that capture intricate rock features and obtain representations that enhance the accuracy of classical machine-learning-based rock images classification. Experimental results on a newly proposed rock images dataset indicate that a model initialized using SSL pretraining outperforms its non-self-supervis
ed learning counterpart. Particularly, we find that MoCo-v2 pretraining provides the most benefit with limited labeled training data compared to other models, including supervised model.
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