Learning from big data for rapid uncertainty quantification of exploratory ore deposits
Mineral resources forecasting requires integrating drill-holes and geophysical data with the guidance of geological knowledge. However, the cost of collecting more data in a specific local area can often be high, in an exploration setting. Uncertainty models built based solely on sparse local data have very limited predicting power. Would it be possible to learn from a large amount of already explored sites and rapidly build predictive models in the local area with sparse local data? To answer this question, we need to develop efficient statistical inference and modeling techniques. We take advantage of the most recent development in computer vision and computer graphics, called level set methods, for efficiently generating local uncertainty models constrained by data and geological rules, with parameters learned from big data. We aim to reduce the cost (time, money and labor) in mineral exploration with this approach.