SulfideNet: Deep Learning for Detection and Quantification of Iron Sulfides in Drill Core Scans

Iron sulfide minerals such as pyrite, chalcopyrite, and pyrrhotite are among the most critical minerals in mining exploration, yet their precise detection and quantification remain highly challenging at a commercial scale due to the subjective nature of core logging and the inconsistencies that arise from discrepancies between individual geologists' judgement. This paper introduces SulfideNet, a novel deep learning framework designed for fast and accurate segmentation and measurement of sulfide minerals in drill core imagery. Leveraging deep learning, SulfideNet is trained on a curated dataset consisting of thousands of high-resolution core images, each paired with detailed, high-quality binary masks accurately identifying sulfide minerals. SulfideNet was evaluated with multiple validation strategies at various scales. First, it was benchmarked against expert geologist annotations on two validation datasets from orogenic gold and porphyry/epithermal deposits, achieving a high correlation in estimating sulfide mineral percentages with an MAE of 0.41%, and near human-level accuracy in pixel-to-pixel comparisons with Dice coefficient of 82%. Additionally, in prospective studies where geologists evaluated the quality of SulfideNet outputs on 5,664 intervals, it achieved an acceptance rate of 91.9%, demonstrating its reliability and potential for automated sulfide mineral quantification. The results indicate that SulfideNet delivers robust and reliable detection of sulfide minerals, positioning it as a useful AI-assistance tool for geologists in core logging. This innovation leads to improved consistency of core logging, improved geological models, and ultimately more informed decisions in mining exploration and processing.