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http://dx.doi.org/10.9719/EEG.2022.55.2.197

Detection of Titanium bearing Myeonsan Formation in the Joseon Supergroup based on Spectral Analysis and Machine Learning Techniques  

Park, Chanhyeok (Department of Astronomy, Space Science, & Geology, Chungnam National University)
Yu, Jaehyung (Department of Geological Sciences, Chungnam National University)
Oh, Min-Kyu (Department of Astronomy, Space Science, & Geology, Chungnam National University)
Lee, Gilljae (Rare Metal Ore Research Center, Korea Institute of Geoscience and Mineral Resources)
Lee, Giyeon (Department of Geological Sciences, Chungnam National University)
Publication Information
Economic and Environmental Geology / v.55, no.2, 2022 , pp. 197-207 More about this Journal
Abstract
This study investigated spectroscopic exploration of Myeonsan formation, the titanium(Ti) ore hostrock, in Joseon supergroup based on machine learning technique. The mineral composition, Ti concentration, spectral characteristics of Myeonsan and non-Myeonsan formation of Joseon supergroup were analyzed. The Myeonsan formation contains relatively larger quantity of opaque minerals along with quartz and clay minerals. The PXRF analysis revealed that the Ti concentration of Myeosan formation is at least 10 times larger than the other formations with bi-modal distribution. The bi-modal concentration is caused by high Ti concentrated sandy layer and relatively lower Ti concentrated muddy layer. The spectral characteristics of Myeonsan formation is manifested by Fe oxides at near infrared and clay minerals at shortwave infrared bands. The Ti exploration is expected to be more effective on detection of hostrock rather than Ti ore because ilmenite does not have characteristic spectral features. The random-forest machine learning classification detected the Myeonsan fomation at 85% accuracy with overall accuracy of 97%, where spectral features of iron oxides and clay minerals played an important role. It indicates that spectral analysis can detect the Ti host rock effectively, and can contribute for UAV based remote sensing for Ti exploration.
Keywords
titanium; Myeonsan formation; spectral analysis; machine learning; ore detection;
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