Grant: Alliance Grants

Drillhole samples and remote sensing surveys provide information on subsurface resources for mineral resource estimation, mine planning, and project evaluation. The collection of this data is expensive but necessary to evaluate mineral resources and build mine plans. Additional data collected reduces subsurface uncertainty but is expensive to collect. Machine learning algorithms are becoming common in subsurface modeling workflows and as tools to assist decision making, but these algorithms perform better with access to more training data. This project explores methodologies for applying machine learning methods to subsurface modeling problems using training data from multiple datasets held by different companies. Mining companies rarely publicize data because of the data collection expense and desire for competitive advantage. Some algorithm training environments, such as federated learning, allow for multiple datasets to be remotely compiled to learn underlying relations between variables in a collaborative way; in this framework, individual datasets remain private to each company that owns the databases but allows for learnings between datasets/companies. This can be combined with transfer learning, which focuses on using knowledge gained from a previous problem (or dataset) applied to a new problem (or dataset). These methods can incorporate knowledge from geological settings with dense data (i.e. mined out deposits) to settings with sparse data (i.e. exploration) to improve subsurface modeling and decision making. This allows for the development of improved machine learning algorithms while maintaining data privacy and competitive advantages. Subsurface modeling problems addressed in this work include core logging analysis (unsupervised clustering), rock type assignment (supervised clustering), geometallurgical modeling (artificial neural networks) and spatial uncertainty modeling (convolutional neural networks).