Abstract
Abstract. A multi-label classification concept is introduced for the mineral mapping task in drill-core hyperspectral data analysis. As opposed to traditional classification methods, this approach has the advantage of considering the different mineral mixtures present in each pixel. For the multi-label classification, the well-known Classifier Chain method (CC) is implemented using the Random Forest (RF) algorithm as the base classifier. High-resolution mineralogical data obtained from Scanning Electron Microscopy (SEM) instrument equipped with the Mineral Liberation Analysis (MLA) software are used for generating the training data set. The drill-core hyperspectral data used in this paper cover the visible-near infrared (VNIR) and the short-wave infrared (SWIR) range of the electromagnetic spectrum. The quantitative and qualitative analysis of the obtained results shows that the multi-label classification approach provides meaningful and descriptive mineral maps and outperforms the single-label RF classification for the mineral mapping task.
Highlights
Drill-cores are cylindrical rock samples extracted by drilling from the Earth’s subsurface up to hundreds of meters during the exploration campaigns
By performing core logging on the cores, important regions can be identified which are sent to laboratories for the implementation of analytical techniques
These laboratory methods include optical microscopy (Krahenbuhl et al, 2015), X-ray diffraction (XRD) (Fox et al, 2016), X-ray Fluorescence (XRF) (Nikonow, Rammlmair, 2017), Scanning Electron Microscopy (SEM) analysis integrated with the Mineral Liberation Analysis (MLA) (Fandrich et al, 2007) or with the QEMSCAN (Gottlieb et al, 2000) software, amongst others
Summary
Drill-cores are cylindrical rock samples extracted by drilling from the Earth’s subsurface up to hundreds of meters during the exploration campaigns. By performing core logging on the cores, important regions can be identified which are sent to laboratories for the implementation of analytical techniques. These laboratory methods include optical microscopy (Krahenbuhl et al, 2015), X-ray diffraction (XRD) (Fox et al, 2016), X-ray Fluorescence (XRF) (Nikonow, Rammlmair, 2017), Scanning Electron Microscopy (SEM) analysis integrated with the Mineral Liberation Analysis (MLA) (Fandrich et al, 2007) or with the QEMSCAN (Gottlieb et al, 2000) software, amongst others
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