Abstract
Technological development and mineral extraction have always been close-knit. Devices have become dependent on dozens of elements with scant concentrations in the Earth's crust. In order to compensate for the scarcity, the search for reliable mineral survey techniques is crucial. It is possible to take advantage of the fact that many minerals are sensitive to the physical phenomena of electromagnetic radiation. The ultraviolet light shows dazzling results since it can excite some minerals to the point of causing them to fluoresce, as is the case of scheelite (main ore of tungsten), a critical mineral that is under a significant mining activity. The purpose of this article is to present a methodology both for collecting spectral data of scheelite, supported by airborne multispectral sensors, and for working with machine learning algorithms, in order to develop an accurate procedure for mineral mapping. The results show that scheelite is easily detectable with a characteristic spectral footprint by making it react with an ultraviolet light source, which could be used in further research to detect this mineral without a severe environmental impact.
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