Trace elements in ilmenite, titanomagnetite and apatite unravel the petrogenesis of Fe-Ti-P-(+/-Zr) rich rocks and associated nelsonite from the Raftsund intrusion, Vesterålen-Lofoten AMCG suite, Northern Norway

Coint, Nolwenn; Mansur, Eduardo T.; Keiding, Jakob K.; Skår, Øyvind

doi:10.1016/j.lithos.2023.107389

Cited by 2 publications

(1 citation statement)

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“…In this context, the geological setting of Norway displays great potential for the discovery and subsequent exploitation of critical raw materials, especially P-REE resources [4]. Notably, significant Fe-Ti-P-REE resources have been identified in Norway that are related to layered intrusions and alkaline intrusive complexes [5,6], emphasizing the potential for discoveries in diverse settings. The Southern Oslo Rift region is renowned for hosting multiple magmatic occurrences that are rich in Fe-Ti-P-REE, with the Kodal deposit being the largest and most well-known to date [7].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Domain Expertise in Machine Learning for Critical Metal Prospecting in the Oslo Rift: A Case Study for Fe-Ti-P-Rare Earth Element Mineralization

Wang,

Coint,

Mansur

et al. 2024

Minerals

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Global demand for critical raw materials, including phosphorus (P) and rare earth elements (REEs), is on the rise. The south part of Norway, with a particular focus on the Southern Oslo Rift region, is a promising reservoir of Fe-Ti-P-REE resources associated with magmatic systems. Confronting challenges in mineral exploration within these systems, notably the absence of alteration haloes and distal footprints, we have explored alternative methodologies. In this study, we combine machine learning with geological expertise, aiming to identify prospective areas for critical metal prospecting. Our workflow involves processing over 400 rock samples to create training datasets for mineralization and non-mineralization, employing an intuitive sampling strategy to overcome an imbalanced sample ratio. Additionally, we convert airborne magnetic, radiometric, and topographic maps into machine learning-friendly features, with a keen focus on incorporating domain knowledge into these data preparations. Within a binary classification framework, we evaluate two commonly used classifiers: a random forest (RF) and support vector machine (SVM). Our analysis shows that the RF model outperforms the SVM model. The RF model generates a predictive map, identifying approximately 0.3% of the study area as promising for mineralization. These findings align with legacy data and field visits, supporting the map’s potential to guide future surveys.

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Section: Introductionmentioning

confidence: 99%