A balanced mineral prospectivity model of Canadian magmatic Ni (± Cu ± Co ± PGE) sulphide mineral systems using conditional variational autoencoders

With the increasing demand for raw materials, innovative exploration techniques are needed to discover large mineral deposits that are accessible from the surface. In recent years, various supervised machine learning techniques have proven effective for mineral prospectivity modelling (MPM). However...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Ore geology reviews Ročník 175; s. 106329
Hlavní autoři: Nagasingha, Lahiru M.A., Bérubé, Charles L., Lawley, Christopher J.M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.12.2024
Témata:
Conditional variational autoencoders
Critical minerals
Data augmentation
Generative modelling
Mineral prospectivity modelling
Mineral prospectivity modelling
Data augmentation
Conditional variational autoencoders
Critical minerals
Generative modelling
ISSN:0169-1368
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:With the increasing demand for raw materials, innovative exploration techniques are needed to discover large mineral deposits that are accessible from the surface. In recent years, various supervised machine learning techniques have proven effective for mineral prospectivity modelling (MPM). However, the successful application of these techniques has been limited due to the scarcity of known mineral deposits compared to barren regions, which leads to a model imbalance favouring the latter. We address the data imbalance challenge in MPM by proposing a novel generative modelling approach using a conditional variational autoencoder (CVAE). We compare the proposed method with two other data balancing techniques, namely the synthetic minority oversampling technique and class weighting. Furthermore, the efficacy of the balancing strategies is evaluated for three MPM classification methods, including extreme gradient boosting machines (XGBM), random forests, and multilayer perceptrons. We implement and test the approaches by modelling the prospectivity of magmatic Ni (±Cu ±Co ±Platinum group elements) sulphide mineral systems for the Canadian landmass. With an area under the success rate curve of 0.95 for a spatially distinct testing data set, we observe that a combination of the proposed CVAE framework with the XGBM classification model surpasses the other methods. Furthermore, the geographical representation of our XGBM-CVAE model demonstrates a strong association with known Ni mineral occurrences in Canada, along with new prospective regions in underexplored areas. [Display omitted] •Classification of mineral prospectivity suffers from imbalanced training data.•Conditional variational autoencoders effectively balance the training data.•XGBoost trained on balanced data outperform other classification algorithms.•The balancing approach yields improved Canadian Ni mineral prospectivity maps.
ISSN:0169-1368
DOI:10.1016/j.oregeorev.2024.106329