SelfMin: Self-Supervised Deep Learning for Advanced Mineralogical Analysis

Mineralogical analysis is critical for understanding the origins and properties of various rock types. Recent advancements in artificial intelligence technologies, particularly supervised deep learning, have transformed qualitative and quantitative mineral analysis. However, current deep-learning approaches require high-quality annotated datasets to acquire and identify different mineralogical characteristics and properties. This is exacerbated by the time-consuming and error-prone labeling processes for the datasets. With self-supervised architectures matching supervised approaches in many computer vision tasks, it is timely to investigate the potential of Self-Supervised Learning (SSL) models in the geosciences. As a result, we use a self-supervised semantic segmentation model to identify and characterize minerals in thin sections, with the model attempting to obtain categories of interest from images without the need for human intervention in annotating the minerals. In this study, we adopted a Self-Supervised Transformer architecture and proposed SelfMin to automatically segment out pyrite minerals from other background gangue minerals in the thin section. Our proposed method achieved 80% in the mean Intersection over Union (mIoU) metric, indicating the model's ability to accurately segment minerals that were not labeled during the annotation process. This work describes the first use of self-supervised deep learning in mineralogical analysis. Further application of this proposed method would allow a robust and efficient advanced qualitative and quantitative mineralogical analysis. It also demonstrates how this technique can be implemented to avoid the need for a large volume of high-quality labeled datasets in other image-based deep learning geosciences analyses.