Charlotte Sennersten

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.

A machine learning approach to find association between imaging features and XRF signatures of rocks in underground mines

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.

Modeling cognitive load and physiological arousal through pupil diameter and heart rate

This study investigates individuals’ cognitive load processing abilities while engaged on a decision-making task in serious games, to explore how a substantial cognitive load dominates over the physiological arousal effect on pupil diameter. A serious game was presented to the participants, which displayed the on–line biofeedback based on physiological measurements of arousal. In such dynamic decision-making environment, the pupil diameter was analyzed in relation to the heart rate, to evaluate if the former could be a useful measure of cognitive abilities of individuals. As pupil might reflect both cognitive activity and physiological arousal, the pupillary response will show an arousal effect only when the cognitive demands of the situation are minimal. Evidence shows that in a situation where a substantial level of cognitive activity is required, only that activity will be observable on the pupil diameter, dominating over the physiological arousal effect indicated by the pupillary response. It is suggested that it might be possible to design serious games tailored to the cognitive abilities of an individual player, using the proposed physiological measurements to observe the moment when such dominance occurs.