Random forest classification for volcanogenic massive sulfide mineralization in the Rouyn-Noranda Area, Quebec.

In this paper we use GIS processing techniques and the random forest (RF) algorithm to assess the mineral potential of the Noranda district.


Random forest (RF) classification was applied to 37 predictor maps (vectors to mineralization) producing a Mineral Prospectivity Map (MPM) for volcanogenic massive sulfide (VMS) mineralization in the Noranda District, Abitibi subprovince, which is host to ∼20 VMS deposits and numerous subeconomic occurrences. The predictor maps were created using geological, geochemical, and geophysical data, and the known VMS deposits were used to train the RF classifier. The RF model was applied on two regions of interest (ROI) to investigate the effect of different sized ROIs on the results. Five sets of balanced and unbalanced training data were used in the experiments to examine the sensitivity of RF to different sets of training data. The probability/prospectivity maps showed very high success rate of classification with regard to training data particularly for the training sets with higher ratio of non-deposits/deposits. Accuracy assessment of classification maps using cross-validation also showed very high accuracy when assessed by the training data in all experiments again giving a higher accuracy for the training sets with higher ratios of non-deposits/deposits, however the later showed very low accuracy when applying k-fold cross-validation or when assessed by 15 VMS showings used as the test data indicating an overfitting problem with the unbalanced data. In most of the experiments, principal component 4 (PC4) of geochemical analysis, proximity to synvolcanic tonalite-trondhjemite-granodiorite (TTG) intrusions, lithology, and sericite alteration maps showed higher predictive power. The importance of individual variables changed when the ROI was changed to a smaller area suggesting that RF is sensitive to the study area.

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Last Updated March 25, 2026, 02:05 (UTC)
Created March 25, 2026, 02:05 (UTC)
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Random forest classification for volcanogenic massive sulfide mineralization in the Rouyn-Noranda Area, Quebec.
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In this paper we use GIS processing techniques and the random forest (RF) algorithm to assess the mineral potential of the Noranda district.


Random forest (RF) classification was applied to 37 predictor maps (vectors to mineralization) producing a Mineral Prospectivity Map (MPM) for volcanogenic massive sulfide (VMS) mineralization in the Noranda District, Abitibi subprovince, which is host to ∼20 VMS deposits and numerous subeconomic occurrences. The predictor maps were created using geological, geochemical, and geophysical data, and the known VMS deposits were used to train the RF classifier. The RF model was applied on two regions of interest (ROI) to investigate the effect of different sized ROIs on the results. Five sets of balanced and unbalanced training data were used in the experiments to examine the sensitivity of RF to different sets of training data. The probability/prospectivity maps showed very high success rate of classification with regard to training data particularly for the training sets with higher ratio of non-deposits/deposits. Accuracy assessment of classification maps using cross-validation also showed very high accuracy when assessed by the training data in all experiments again giving a higher accuracy for the training sets with higher ratios of non-deposits/deposits, however the later showed very low accuracy when applying k-fold cross-validation or when assessed by 15 VMS showings used as the test data indicating an overfitting problem with the unbalanced data. In most of the experiments, principal component 4 (PC4) of geochemical analysis, proximity to synvolcanic tonalite-trondhjemite-granodiorite (TTG) intrusions, lithology, and sericite alteration maps showed higher predictive power. The importance of individual variables changed when the ROI was changed to a smaller area suggesting that RF is sensitive to the study area.
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0.12
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2023-09-21
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public
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https://metalearth.geohub.laurentian.ca/datasets/75102e1e2c7146188f47ff9dda6f4be0
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MetalEarth
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MetalEarth
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2023-09-21
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75102e1e2c7146188f47ff9dda6f4be0
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English
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https://metalearth.geohub.laurentian.ca/datasets/75102e1e2c7146188f47ff9dda6f4be0
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