USING GIS TECHNOLOGIES AND HYPERSPECTRAL DATA TO IDENTIFY MINING EXPLORATION AT FRISCO MINE, ARIZONA
Publisher
The University of Arizona.Rights
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.Collection Information
This item is part of the MS-GIST Master's Reports collection. For more information about items in this collection, please contact the UA Campus Repository at repository@u.library.arizona.edu.Abstract
The potential benefits of incorporating digitized geologic maps and hyperspectral data for identifying new exploration mining targets at Frisco gold mine are explored in this project. The digitization of geologic maps converts valuable geologic information from traditional paper maps into a digital format, making it easier to analyze and integrate with other geological datasets. This integration helps identify spatial relationships, patterns, and trends in the geologic data, leading to the discovery of potential gold mineralization zones. Hyperspectral data is also crucial in enhancing exploration efforts. Hyperspectral imaging technology captures data across a broad range of wavelengths, enabling detailed characterization of mineralogy and alteration minerals associated with gold deposits. By analyzing hyperspectral data, geologists can identify spectral signatures indicative of gold mineralization, allowing for the precise delineation of potential exploration targets. Combining digitized geologic maps in Datamine Discover with hyperspectral data analysis provides a powerful toolset for gold mine exploration. The integrated approach efficiently identifies additional exploration targets by leveraging the spatial information from geologic maps and the spectral signatures captured by hyperspectral data. This workflow enhances understanding of the geology and mineralization processes, ultimately leading to improved targeting and resource estimation in gold mining operations.Type
Electronic Reporttext