Association Between Imaging and XRF Sensing: A Machine Learning Approach to Discover Mineralogy in Abandoned Mine Voids

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.