Date of Graduation

Spring 2026

Degree

Master of Science in Geography and Geology

Department

School of Earth, Environment & Sustainability

Committee Chair

Kevin Mickus

Abstract

The South Pass-Granite Mountains, comprised of Archean to Tertiary rocks embedded with an Archean greenstone belt in Central Wyoming, have historically been mined for gold, iron, steel, and silver. Rare earth elements have been of increasing interest in the region and thus, several datasets have become available. Knowledge driven and data driven models are used in mineral prospectivity mapping with input data including geological mapping, geochemical data, geophysical (USGS Critical Mineral magnetic and gravity data), radiometric (U, Th, K) data and multispectral remote sensing data. Mineral prospectivity has seen a rapid improvement in recent years using advancements in machine learning for processing large and complex datasets to identify new potential deposit locations. The magnetic and gravity data are utilized to create lineaments that may act as conduits for ore fluids and for fault density evidential maps for input into the machine learning algorithms. This study utilizes and compares algorithms such as Random Forest, XgBoost, support vector machine, and convolutional neural networks on evidential maps to identify potential new gold and rare earth elements deposits within the South Pass region. The combination of the multiple algorithms identified many locations within the underexplored placer deposits of the Wasatch formation as well as key locations surrounding the Bradley Peak location within the Seminoe Mountains.

Keywords

mineral prospectivity mapping, machine learning, neural network, gravity, magnetics, radiometrics, spectral, South Pass-Granite Mountains Wyoming

Subject Categories

Geology | Geophysics and Seismology

Copyright

© Carl D. York

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Open Access

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