Estimates of numbers of mineral deposits are fundamental to assessing undiscovered mineral resources. Just as frequencies of grades and tonnages of well-explored deposits can be used to represent the grades and tonnages of undiscovered deposits, the density of deposits (deposits/area) in well-explored control areas can serve to represent the number of deposits. Empirical evidence presented here indicates that the processes affecting the number and quantity of resources in geological settings are very general across many types of mineral deposits. For podiform chromite, porphyry copper, and volcanogenic massive sulfide deposit types, the size of tract that geologically could contain the deposits is an excellent predictor of the total number of deposits. The number of mineral deposits is also proportional to the type’s size. The total amount of mineralized rock is also proportional to size of the permissive area and the median deposit type’s size. Regressions using these variables provide a means to estimate the density of deposits and the total amount of mineralization. These powerful estimators are based on analysis of ten different types of mineral deposits (Climax Mo, Cuban Mn, Cyprus massive sulfide, Franciscan Mn, kuroko massive sulfide, low-sulfide quartz-Au vein, placer Au, podiform Cr, porphyry Cu, and W vein) from 108 permissive control tracts around the world therefore generalizing across deposit types. Despite the diverse and complex geological settings of deposit types studied here, the relationships observed indicate universal controls on the accumulation and preservation of mineral resources that operate across all scales. The strength of the relationships (R 2=0.91 for density and 0.95 for mineralized rock) argues for their broad use. Deposit densities can now be used to provide a guideline for expert judgment or used directly for estimating the number of most kinds of mineral deposits.
Additional publication details
|Publication Subtype||Journal Article|
|Title||Mineral deposit densities for estimating mineral resources|
|Series title||Mathematical Geosciences|
|Contributing office(s)||Geology, Minerals, Energy, and Geophysics Science Center|
|Google Analytic Metrics||Metrics page|