GSA Annual Meeting in Indianapolis, Indiana, USA - 2018

Paper No. 68-2
Presentation Time: 1:45 PM

PROBABILISTIC ESTIMATE OF UNDISCOVERED RESOURCES OF THE SOUTH KAWISHIWI INTRUSION, DULUTH COMPLEX, MINNESOTA USA


COYAN, Joshua A., ZIENTEK, Michael L. and PARKS, Heather L., Department of the Interior, US Geological Survey, Spokane, WA 99201

Current US mining regulations do not require US mining companies to disclose results of exploration activities or future interests. Consequently, it can be difficult to determine the current and future direction of mining activities or the total estimated endowment of a mineral deposit. With this lack of transparency, a probabilistic estimate of undiscovered resources is required to determine a reasonable range for these values.

The Duluth Complex is located on the northwest edge of Lake Superior and is a composite of layered-mafic intrusions. It is composed of a series of gabbroic intrusions assembled during the evolution of the Mid-Continent Rift circa 1.1 Ga. The western margin of the complex consists of intrusions (e.g., the South Kawishiwi Intrusion (SKI) and the Partridge River Intrusion) that contain copper, nickel, and platinum-group elements. One such intrusion, the South Kawishiwi Intrusion, is a gently dipping, sheet-like accumulation. Local mining companies have drilled and assayed the western edge of this intrusion and released their data to Natural Resources Research Institute (NRRI), who compiled and published the data. Additional, more recent exploration data has been shared with the USGS for collaborative studies. We composited the values from these data sets for the Basal Mineralized Zone and calculated metal surface density (MSD) values to determine the metal-content-per-square-meter of surface area. As a form of exploration data analysis, empirical bayesian kriging was then used to sample the MSD data and create a distribution of semi-variograms. Differences in these distributions were used to evaluate the assumption of stationarity and permitted isolation of different variance distributions into unique domains. Once the domains were designated, gaussian geostatistical simulation was completed for each domain. Using this workflow, a probabilistic estimate of total contained metal was calculated for the entire SKI and compared against formally defined mineral inventories for each deposit area.