For remote characterization of inaccessible underground mine voids, we are developing unmanned aerial vehicles (equipped with multiple sensors, including cameras) to fly into the mine voids to map their shape, condition, and most importantly, mineralization of the surface. The X-ray fluorescence (XRF) spectroscopy analysis is normally conducted on rock samples in order to detect the present elements (that constitutes minerals). Mining company staffs, however, are able to judge rock types based upon visual features alone. This implies that there are some associations between the XRF signatures and the visual features of rocks. Inspired by this, we have developed a machine learning approach to predict the presence of elements in rocks, for inferring probable rock and mineral types, from imaging features. Note that there exist a number of works in the literature for classifying rocks from digital images. However, to the best of our knowledge, limited attempt has been made to find association between the digital imaging features and the XRF signatures for mineralogy discovery that we have addressed in this paper. The machine learning algorithm is trained offline based on visual imaging and XRF spectroscopy analysis data of collected rock samples in a laboratory. The imaging features provide the visual cues, and the XRF data provide information on element presence/concentration. The machine learning algorithm (regression) discovered the non-linear relationship between these feature spaces and was able to predict the element presence with high accuracy as evidenced from the experimental results.

This study investigated the applicability of machine learning algorithms to detect the presence of elements in underground mines from rock surface images, which is proposed as a heuristic classification method inspired by the ability of human geologists to make judgments about the location of ore veins by eye. A regression algorithm was investigated to find associations between image features and X-Ray Fluorescence (XRF) signatures indicating elemental content of the surface and near-surface region of the rocks. A set of image processing algorithms was used to extract color distribution, edge orientation statistics, and texture of the rock surfaces. XRF signatures were obtained from the same samples, providing a semi-quantitative measure of element concentration. The process was performed on a set of 20 rock samples. The regression algorithm was then trained to find a mapping between image features and the semi-quantitative element concentrations (corresponding with XRF peaks). Experimental results demonstrate the potential effectiveness of the proposed approach in the context of a specific ore body.