Mineral Mapping of Drill Core Hyperspectral Data with Extreme Learning Machines

Abstract

Hyperspectral scanners are increasingly being used in the mining industry as a non-destructive and non-invasive technique to efficiently map minerals in drill core samples. Hyperspectral data allows the characterization of different mineral assemblages, structural features and alteration patterns based on reflectance spectrum profiles. Traditional methods to analysis drill core hyperspectral data include the use of reference spectral libraries by visual analysis or a well established software. However, although these approaches produce good results, they are time-consuming and prone to errors. Therefore, in this paper, we take advantage of the latest and advanced machine learning techniques proposed in different scientific fields and explore the use of extreme learning machines (ELM) to map minerals in drill core hyperspectral data. This is a supervised technique that provides fast and automatic means to characterize hyperspectral data. To be able to implement this technique, a reference map was generated from the drill core hyperspectral data. The obtained results indicate that ELM can successfully map minerals in drill core hyperspectral data producing better quantitative and qualitative results than a typical RF classifier.