Using Bayesian Neural Networks for Uncertainty Assessment of Ore Type Boundaries in Complex Geological Models

Building an orebody model is a key step in the design and operation of a mine because it provides the basis for follow-up mine decisions. Recently, it was shown that convolutional neural networks can successfully reproduce the manual geological interpretation of a complex ore deposit. The deep learning approach mitigates the shortcomings of a labor-intensive process that greatly limits the speed at which geological resources can be updated. However, convolutional neural network architectures lack the ability to measure the confidence of their predictions. In this study, we tried to assess the uncertainty of the boundaries of these domains so that the characterization of metal grades within them can account for this uncertainty. We explored and compared Monte Carlo Dropout and Bayesian neural networks to assess the uncertainty of deep convolutional neural network models trained to predict geological domains conditioned to drill-hole data. Monte Carlo Dropout uncertainty maps reflect the uncertainty in geological interpretations. The uncertainty is highest in areas where the interpreter/geologist had more difficulty delineating the boundaries of geological bodies. This is known as geological interpretation uncertainty. In contrast, Bayesian neural network uncertainty is visible depending on ore type frequency, complexity, and heterogeneity. Bayesian neural networks are able to better represent the uncertainty regarding the unknown. The application example here is a real case study of several ore types from a polymetallic sulfide orebody located in the south of Portugal.

Automated summary

In this study, we aimed to assess the uncertainty of deep convolutional neural network models in predicting geological domains, and compared Monte Carlo Dropout and Bayesian neural networks for evaluation. The results showed that Monte Carlo Dropout reflects the uncertainty in geological interpretations, while Bayesian neural networks are better suited for representing uncertainty regarding the unknown.