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 ari...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11048610/ |
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| author | Maral Rasoolijaberi Chuiqing Zeng John Manchuk Michelle Legat Abigail Jackson-Gain |
| author_facet | Maral Rasoolijaberi Chuiqing Zeng John Manchuk Michelle Legat Abigail Jackson-Gain |
| author_sort | Maral Rasoolijaberi |
| collection | DOAJ |
| description | 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’ judgment. This article introduces <bold>SulfideNet</bold>, 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%. In addition, in prospective studies where geologists evaluated the quality of SulfideNet outputs on 5664 intervals, it achieved an acceptance rate of <bold>91.9%</bold>, 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. |
| format | Article |
| id | doaj-art-5a92153ecaba4f779fbcda087119f8f9 |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-5a92153ecaba4f779fbcda087119f8f92025-08-20T02:39:59ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118170801709110.1109/JSTARS.2025.358252011048610SulfideNet: Deep Learning for Detection and Quantification of Iron Sulfides in Drill Core ScansMaral Rasoolijaberi0https://orcid.org/0009-0005-0950-4728Chuiqing Zeng1https://orcid.org/0000-0003-0198-5617John Manchuk2Michelle Legat3Abigail Jackson-Gain4R&D Department, GeologicAI, Toronto, ON, CanadaTechnology Development Group, GeologicAI, Toronto, ON, CanadaManagement Team, GeologicAI, Calgary, AB, CanadaGeosciences Department, GeologicAI, Calgary, AB, CanadaGeosciences Department, GeologicAI, Santiago, ChileIron 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’ judgment. This article introduces <bold>SulfideNet</bold>, 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%. In addition, in prospective studies where geologists evaluated the quality of SulfideNet outputs on 5664 intervals, it achieved an acceptance rate of <bold>91.9%</bold>, 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.https://ieeexplore.ieee.org/document/11048610/Computer visiondeep learninggeologymachine learningmineralsmining industry |
| spellingShingle | Maral Rasoolijaberi Chuiqing Zeng John Manchuk Michelle Legat Abigail Jackson-Gain SulfideNet: Deep Learning for Detection and Quantification of Iron Sulfides in Drill Core Scans IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Computer vision deep learning geology machine learning minerals mining industry |
| title | SulfideNet: Deep Learning for Detection and Quantification of Iron Sulfides in Drill Core Scans |
| title_full | SulfideNet: Deep Learning for Detection and Quantification of Iron Sulfides in Drill Core Scans |
| title_fullStr | SulfideNet: Deep Learning for Detection and Quantification of Iron Sulfides in Drill Core Scans |
| title_full_unstemmed | SulfideNet: Deep Learning for Detection and Quantification of Iron Sulfides in Drill Core Scans |
| title_short | SulfideNet: Deep Learning for Detection and Quantification of Iron Sulfides in Drill Core Scans |
| title_sort | sulfidenet deep learning for detection and quantification of iron sulfides in drill core scans |
| topic | Computer vision deep learning geology machine learning minerals mining industry |
| url | https://ieeexplore.ieee.org/document/11048610/ |
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