A Comparative Evaluation of Self-Supervised Methods Applied to Rock Images 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-supervised 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.
Origin : Files produced by the author(s)
Licence : CC BY NC - Attribution - NonCommercial
Licence : CC BY NC - Attribution - NonCommercial
