Carranza, Emmanuel John M., and Sadeghi, Martiya (2010) Predictive mapping of prospectivity and quantitative estimation of undiscovered VMS deposits in Skellefte district (Sweden). Ore Geology Reviews, 38 (3). pp. 219-241.

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Abstract

Mapping of mineral prospectivity and assessment of undiscovered mineral deposits both aim to delineate prospective ground for mineral exploration, but the latter is usually carried out exclusive of the former. We propose that the spatial distribution of known mineral deposits of the type sought is the key to link mapping of mineral prospectivity and assessment of undiscovered deposits. We demonstrate this proposition in regional-scale mapping of prospectivity for volcanogenic massive sulphides (VMS) deposits and estimation of undiscovered VMS endowment in the Skellefte district (Sweden). The results of analyses of the spatial distribution of known VMS deposits and their spatial associations with geological features are consistent with existing knowledge of geological controls on VMS mineralization in the district, and we used them to define spatial recognition criteria of regional-scale VMS prospectivity. Integration of layers of evidence representing spatial recognition criteria of VMS prospectivity via application of data-driven evidential belief functions results in a regional-scale map of prospective areas occupying 15% of the district and having fitting and prediction-rates of 100%. We used the map of prospective areas and proxy measures for degrees of exploration based on the spatial distribution of known VMS deposits in one-level prediction of undiscovered mineral endowment. We obtained estimates of 709 Kt undiscovered Cu endowment, 3190 Kt undiscovered Zn endowment, 95 Mt undiscovered ore tonnage, and 48 undiscovered VMS deposits. These estimates are slightly (ca. 5% on average) lower than, and thus corroborated by, estimates obtained via radial-density fractal analysis of the spatial distribution of known VMS deposits. Therefore, mineral prospectivity mapping can be a part of mineral resource assessment if the spatial distribution of discovered deposits of the type sought is considered in both predictive modeling processes.

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