Upscaling High-Resolution Mineralogical Analyses to Estimate Mineral Abundances in Drill Core Hyperspectral Data

In this paper, we propose a supervised learning method for estimating mineral quantities in drill core hyperspectral data. Our proposed method links the high-resolution mineralogical analyses and hyperspectral data to learn a dictionary. The learned dictionary is then used for linear unmixing and estimating mineral abundances of the entire drill core sample. To evaluate the performance of the proposed method, we use a drill core data set, which is composed of the VNIR-SWIR hyperspectral data and high-resolution mineralogical analyses performed by a Scanning Electron Microscopy (SEM) instrument equipped with the Mineral Liberation Analysis (MLA) software. The quantitative and qualitative analysis of the experimental results shows that the proposed method provides reliable mineral quantity estimates.