Abstract
Due to the extensive drilling performed every year in exploration campaigns for the discovery and evaluation of ore deposits, drill-core mapping is becoming an essential step. While valuable mineralogical information is extracted during core logging by on-site geologists, the process is time consuming and dependent on the observer and individual background. Hyperspectral short-wave infrared (SWIR) data is used in the mining industry as a tool to complement traditional logging techniques and to provide a rapid and non-invasive analytical method for mineralogical characterization. Additionally, Scanning Electron Microscopy-based image analyses using a Mineral Liberation Analyser (SEM-MLA) provide exhaustive high-resolution mineralogical maps, but can only be performed on small areas of the drill-cores. We propose to use machine learning algorithms to combine the two data types and upscale the quantitative SEM-MLA mineralogical data to drill-core scale. This way, quasi-quantitative maps over entire drill-core samples are obtained. Our upscaling approach increases result transparency and reproducibility by employing physical-based data acquisition (hyperspectral imaging) combined with mathematical models (machine learning). The procedure is tested on 5 drill-core samples with varying training data using random forests, support vector machines and neural network regression models. The obtained mineral abundance maps are further used for the extraction of mineralogical parameters such as mineral association.
Highlights
Exploration campaigns are fundamental steps towards the discovery and evaluation of mineral deposits required to fulfil the global demand of raw materials
In order to showcase the suitability of the proposed approach, the first drill-core sample presented in the data section (DC-1) is used
The fractional abundances were computed by considering the frequency of the identified minerals in the corresponding region of the Scanning Electron Microscopy (SEM)-Mineral Liberation Analysis (MLA) image for each hyperspectral pixel
Summary
Exploration campaigns are fundamental steps towards the discovery and evaluation of mineral deposits required to fulfil the global demand of raw materials. Standard quantitative analyses include X-Ray diffraction (XRD) applied on powder samples [2] or Scanning Electron Microscopy (SEM) based image analyses techniques [3] applied on polished thin sections prepared from areas of interest in the drill-cores. Qualitative mineralogical analyses are performed through optical microscopy on thin sections. These laboratory techniques provide valuable mineralogical information and derived mineralogical and metallurgical parameters, but they are of small scale, highly time-consuming, destructive, and rather expensive. This represents a challenge since thousands of meters of core are acquired during exploration campaigns
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