Session: Exploration Insights (in person) | Room 715
Summary:
This discussion summarizes a machine learning (ML) workflow developed by SRK for use in geotechnical photologging of exploration core photographs. This methodology involves the qualitative description of rock mass character along the length of each drill run with a range from; disintegrated core (ex. faults, overburden) to stick Rock.
This workflow has been used at a copper-gold porphyry mine in central Asia, where 44,500m of exploration drill core photos were automatically characterized using the ML model. The ML output directly supplemented the available geotechnical logging data to build a more reliable domain model of the deposit, which is the foundation for the geotechnical analysis.
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Andrew LeRiche
Consultant Rock Mechanics Engineer
Andrew specializes in geotechnical characterization and domaining for mining projects.
