| Titre | Prospectivity modeling of Canadian magmatic Ni (± Cu ± Co ± Cr ± PGE) mineral systems |
| |
| Auteur | Lawley, C J M; Tschirhart, V ; Smith, J; Pehrsson,; Schetselaar, E; Houlé,
M; Schaeffer, A |
| Source | Ore Geology Reviews 103985, 2021 p. 1-23, https://doi.org/10.1016/j.oregeorev.2021.103985  Accès ouvert |
| Image |  |
| Année | 2021 |
| Séries alt. | Ressources naturelles Canada, Contribution externe 20200519 |
| Éditeur | Elsevier |
| Document | publication en série |
| Lang. | anglais |
| DOI | https://doi.org/10.1016/j.oregeorev.2021.103985 |
| Media | papier; en ligne; numérique |
| Formats | pdf; html |
| Province | Colombie-Britannique; Alberta; Saskatchewan; Manitoba; Ontario; Québec; Nouveau-Brunswick; Nouvelle-Écosse; Île-du-Prince-Édouard; Terre-Neuve-et-Labrador; Territoires du Nord-Ouest; Yukon; Nunavut;
Canada |
| SNRC | 1; 2; 3; 10; 11; 12; 13; 14; 15; 16; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 52; 53; 54; 55; 56; 57; 58; 59; 62; 63; 64; 65;
66; 67; 68; 69; 72; 73; 74; 75; 76; 77; 78; 79; 82; 83; 84; 85; 86; 87; 88; 89; 92; 93; 94; 95; 96; 97; 98; 99; 102; 103; 104; 105; 106; 107; 114O; 114P; 115; 116; 117; 120; 340; 560 |
| Lat/Long OENS | -140.0000 -55.0000 90.0000 45.0000 |
| Lat/Long OENS | -141.0000 -50.0000 90.0000 41.7500 |
| Sujets | potentiel minier; roches mafiques; roches ultramafiques; exploration; gisements minéraux; nickel; cuivre; cobalt; l'apprentissage machine; Sciences et technologie; minéralogie |
| Illustrations | cartes de localisation; diagrammes; tableaux; diagrammes; graphiques; représentations graphiques combinées |
| Programme | Initiative géoscientifique ciblée (IGC-5) Systèmes minéralisés de nickel-cuivre-EGP-chrome - architecture - systèmes chromifères |
| Programme | Initiative géoscientifique ciblée (IGC-5) Systèmes minéralisés de nickel-cuivre-EGP-chrome - architecture - systèmes chromifères |
| Diffusé | 2021 01 09 |
| Résumé | (disponible en anglais seulement)
New mineral deposit discoveries are required to meet the forecasted demand for some critical raw materials. Governments are responding to that challenge by
investing in mineral systems research and by by making government geoscience datasets freely available to the public and explorers. However, translating conceptual mineral system models to mappable geological, geochemical, and geophysical proxies is
difficult with incomplete data of variable quality from modern and legacy surveys. Herein we address those knowledge gaps and propose a new open source workflow in R for prospectivity modelling using public geoscience datasets. We focus on the
largest footprints of magmatic Ni (±Cu) sulphide mineral systems and their critical raw materials (±Co ± PGE). Multiple prospectivity models are presented, including data-driven methods (e.g., weights of evidence, gradient boosting machines) that use
the features of known mineral occurrences as a training set and a hybrid method that also incorporates conceptual mineral system criteria. All models are validated using data from northern Canada (i.e., north of 60° latitude) as a test set.
Statistical analysis of the prospectivity results suggests that rock types and geological ages are two of the most important predictive datasets, which correspond to the sources and drivers within the mineral system framework, respectively. Variable
importance plots further suggest that geological boundaries (e.g., horizontal gradient magnitude of the gravity data and multi-scale edges) and the close spatial association between areas of high mineral potential and the edges of thick continental
crust represent prospective ore-forming pathways. Model performance and the best combination of predictors and hyperparameters for each model are based on the receiver operating characteristics (ROC) plots, which yield a range of area under the curve
(AUC) from 0.846 to 0.923 for the spatially independent test set. Most Canadian geological provinces, possibly with the exception of the Grenville orogen for the hybrid and weights of evidence methods (AUC = 0.716-0.726), yield comparable model
performance, suggesting that the heterogeneous spatial distribution of different mineral system sub-types (e.g., komatiite-associated, rift-related, Alaskan-type, and hydrothermal awaruite) have a relatively minor impact on the prospectivity results.
Monte Carlo-type simulations further suggest that the expert weightings used in the hybrid method (AUC = 0.843) are only slightly better than an average model constructed from random combinations of weightings (AUC = 0.809). The general agreement
between different methods and multiple iterations of the same model demonstrate that public geoscience datasets can effectively reduce the search space to support mineral exploration targeting (i.e., less than 8% of map pixels contain more than 80%
of the known Ni mineralization). However, vast segments of the Canadian landmass have not undergone systematic geological surveying or data acquisition. Prospectivity modelling can thus also be used by governments and academia to prioritize areas for
future targeted geoscience research. |
| Sommaire | (Résumé en langage clair et simple, non publié)
Here we present the first national assessment of the mineral potential of nickel-rich deposits and associated essential minerals (e.g. minerals
needed for batteries and advanced technologies). The model is based on previously published databases and on new data products generated as part of the current study. We demonstrate that approximately 8% of the Canadian territory contains more than
80% of the known mineral potential, which suggests that these models can support mineral exploration efforts by refocusing drilling targeting on the most promising areas. We validate the mineral potential results using a blind area that was not used
during model construction. |
| GEOSCAN ID | 327522 |
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