Mineral Classification Using Machine Learning and Images of Microscopic Rock Thin Section

Abstract

The most widely used method for mineral type classification from a rock thin section is done by the observation of optical properties of a mineral in a polarized microscope rotation stage. Several studies propose the application of digital image processing techniques and Neural Networks to automate this task. This study uses simpler and more scalable machine learning techniques, being nearest neighbor and decision tree, and adds new optical properties to be extracted from the digital images. Two datasets are used, one provided by Ferdowsi University of Mashhad, with 17 different minerals, and another built from scratch in the geology department of Federal University of Pelotas, containing 4 different minerals. The datasets are composed of mineral images captured under cross and plane polarized light from different rock thin sections. For each dataset used, we took a pair of images of the same mineral taken on different lights, extracted optical properties of color and texture, applied a machine learning algorithm and provided the results. At the end of this study, we demonstrate it is possible to achieve high accuracy as Neural Networks with more simple machine learning algorithms as the our dataset showed average results as high as 97% and Mashhad’s as high as 93%.