| Title | Uraninite chemistry of the Central Mineral Belt, Labrador, Canada: application of grain-scale unsupervised machine-learning |
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| Author | Acosta-Góngora, P; Potter, E G ; Lawley, C J M; Petts, D; Sparkes, G |
| Source | Journal of Geochemical Exploration vol. 233, 106910, 2021 p. 1-27, https://doi.org/10.1016/j.gexplo.2021.106910 |
| Image |  |
| Year | 2021 |
| Alt Series | Natural Resources Canada, Contribution Series 20210387 |
| Publisher | Elsevier |
| Document | serial |
| Lang. | English |
| Media | digital; on-line |
| File format | pdf; html |
| Province | Newfoundland and Labrador |
| NTS | 13J/04; 13J/05; 13J/12; 13J/13; 13K; 13L/01; 13L/08; 13L/09; 13L/16; 13M/01; 13M/08; 13N/01; 13N/02; 13N/03; 13N/04; 13N/05; 13N/06; 13N/07; 13N/08; 13O/04; 13O/05 |
| Area | Labrador; Jacques Lake; Moran Lake; Anna Lake |
| Lat/Long WENS | -62.5000 -59.5000 55.3833 54.0500 |
| Subjects | economic geology; geochemistry; mineralogy; tectonics; Science and Technology; Nature and Environment; mineral deposits; uranium; base metals; precious metals; uraninite; ore mineral genesis;
mineralization; host rocks; statistical analyses; trace element geochemistry; major element geochemistry; thermal analyses; tectonic setting; magmatism; hydrothermal systems; fluid dynamics; metasomatism; alteration; mineral occurrences; petrographic
analyses; remobilization; mineral exploration; exploration methods; paragenesis; Central Mineral Belt; Dandy Prospect; Near Miss Prospect; Jacques Lake Deposit; machine learning; Artificial intelligence; Precambrian; Proterozoic |
| Illustrations | location maps; geoscientific sketch maps; stratigraphic charts; tables; bar graphs; photographs; photomicrographs; plots |
| Program | Targeted Geoscience Initiative (TGI-5) Uranium Ore Systems |
| Released | 2021 11 13 |
| Abstract | The Central Mineral Belt (CMB) in Labrador hosts several enigmatic U ± base ± precious metal showings, prospects, and deposits. Multiple mineralization styles occur within different host rocks, which
has led to a variety of ore system models to be proposed. Here, unsupervised machine-learning (principal component (PCA) and targeted cluster analyses) applied to quantitative LA-ICP-MS trace element maps of uraninite were used to understand genesis
the U systems in the CMB.
Trace element mapping, PCA and cluster analysis indicate that: i) the largest source of data variance in uraninite from the occurrences is related to Th, REE, Zr, Hf, As, V and Ba contents, and ii) there are distinct
uraninite compositions at the Two-Time deposit, Near Miss and Anomaly No. 7 mineral occurrences, not recognized from petrographic studies or major element chemistry.
Trace element chemistry indicates that uraninite precipitated from fluids under
(1) high-temperature magmatic and/or metasomatic conditions (> 350 °C; Dandy prospect; U/Th = 107 and Sigma-REE = 0.9 wt%), (2) low-temperature (< 350 °C) and locally oxidizing hydrothermal vein-type environments (e.g., Two-Time and Anomaly No. 7;
U/Th greater than or equal to104 and Sigma-REE less than or equal to 0.1 to 3.6 wt%), and (3) complex environments were precursor uraninite was overprinted by presumably lower temperature hydrothermal fluids (e.g., Jacques Lake and Nash deposits;
U/Th = 102 to 105 and Sigma-REE less than or equal to 0.1 to 1.4 wt%). Hydrothermal alteration caused LREE enrichment and/or increased U/Th ratios of the primary uraninite and locally, U remobilization into microfractures. Recognizing high- and
low-temperature uraninite from the same mineral occurrences provides new evidence for multiple stages of hydrothermal fluid influx. Therefore, our results further support previous studies indicating significant variation in the trace element contents
of uraninite due to hydrothermal alteration.
In addition to the genetic constraints, PCA results and normalized REE patterns also show that the CMB uraninites have distinct geochemical signatures. Combining petrographic studies, trace element
mapping, and unsupervised machine-learning unravelled cryptic chemical variations in key minerals that can guide future mineral exploration in the district. In the CMB, these chemical signatures provide insights on the evolution of U-rich fluids, in
particular the presence of multiple fluid sources that evolved through a complex tectono-magmatic history. |
| Summary | (Plain Language Summary, not published)
The Targeted Geoscience Initiative (TGI) is a collaborative federal geoscience program that provides industry with the next generation of geoscience
knowledge and innovative techniques to better detect buried mineral deposits, thereby reducing some of the risks of exploration. This contribution uses trace element concentrations of the mineral uraninite from mineral occurrences in the Central
Mineral Belt combined with multivariate statistical analyses to recognize subtle chemical variations that reflect how the deposits formed. |
| GEOSCAN ID | 329233 |
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