Interpretation of Hyperspectral Shortwave Infrared Core Scanning Data Using SEM-Based Automated Mineralogy: A Machine Learning Approach

Abstract

Understanding the mineralogy and geochemistry of the subsurface is key when assessing and exploring for mineral deposits. To achieve this goal, rapid acquisition and accurate interpretation of drill core data are essential. Hyperspectral shortwave infrared imaging is a rapid and non-destructive analytical method widely used in the minerals industry to map minerals with diagnostic features in core samples. In this paper, we present an automated method to interpret hyperspectral shortwave infrared data on drill core to decipher major felsic rock-forming minerals using supervised machine learning techniques for processing, masking, and extracting mineralogical and textural information. This study utilizes a co-registered training dataset that integrates hyperspectral data with quantitative scanning electron microscopy data instead of spectrum matching using a spectral library. Our methodology overcomes previous limitations in hyperspectral data interpretation for the full mineralogy (i.e., quartz and feldspar) caused by the need to identify spectral features of minerals; in particular, it detects the presence of minerals that are considered invisible in traditional shortwave infrared hyperspectral analysis.

Keywords

• mineral exploration
• hyperspectral shortwave infrared (SWIR) core scanning
• automated mineralogy
• supervised machine learning
• CNN
• ResNet