GEOPHYSICAL ANALYSIS OF THE SUPERIOR PROVINCE IN WESTERN MINNESOTA: INSIGHTS INTO MINERAL EXPLORATION USING GRAVITY AND MAGNETIC DATA

The Superior Province, an Archean craton, spans 132,000 square miles across south-central Canada and north-central United States. In Minnesota, it is dominated by granitic and granitoid intrusions, with smaller amounts of metamorphic rocks formed through orogenic events between 2.8 and 2.6 Ga. In northwestern Minnesota, minor Cretaceous clastic sediments and glacial deposits overlie bedrock, complicating direct identification of lithologies.

Renowned for hosting world-class ore deposits—including banded-iron formations, volcanogenic massive sulfides, and granitoid-hosted gold and copper—the Superior Province in northwestern Minnesota remains largely underexplored due to limited outcrops and detailed geophysical surveys. To address this, a high-resolution aeromagnetic and radiometric survey was conducted under the USGS Critical Mineral Program. Gravity data were integrated into a geophysical analysis to produce residual and derivative anomaly maps, along with two- and three-dimensional gravity and magnetic models correlated with geochemical datasets.

Preliminary results highlight gravity and magnetic minima aligning with Archean granite intrusions, while maxima correspond to banded-iron formations and basaltic dikes. Larger anomalies trend SW-NE, while smaller-scale anomalies linked to iron formations and dikes trend SE-NW. Derivative analyses validate these patterns and refine source body locations.

Forward modeling across three profiles identified dikes with high densities and magnetic susceptibilities, including localized zones of even greater values, suggesting potential mineralized or lithologically distinct bodies. The models also reveal boundaries between the sedimentary Quetico subprovince and the adjoining Wabigoon and Wawa subprovinces, inferred from gravity and magnetic contrasts. These findings enhance understanding of the region's structural framework and mineral potential, guiding exploration efforts. Future work will refine subsurface models and apply machine learning to geochemical and radiometric data for targeted exploration.