Assessing Mineralisation, Penalties and Acid Rock Drainage: Characterization of a Ni-Cu Sulfide Prospect Using Drill Core Laser-Induced Breakdown Spectroscopy, Geochemical Analysis and SEM Mineral Liberation Analyser Data

The mineral industry plays a crucial role in addressing climate change by supplying essential materials for clean energy technologies. Base metals like Ni and Cu are becoming increasingly critical and strategic for global economies. Ore body knowledge (OBK) is vital for efficient recovery of these metals, and ore characterization is a key step in optimizing the project production and economic value. In this thesis, drill core Laser-Induced Breakdown Spectroscopy (LIBS), Mineral Liberation Analyser (MLA), and geochemical data were used to analyse a Ni-Cu sulfide prospect in Finland. The aim of this work was to enhance OBK through exploratory data analysis and development of workflows. Enrichment of sulfide and penalty elements in the concentrate and zoning of high-grade areas were determined. Early assessments on Acid Rock Drainage (ARD) potential were conducted using mineralogical and geochemical data to inform environmental studies. The proposed workflows demonstrated homogeneity in sulfide distribution in the high-grade zone, while clustering algorithms were more sensitive to talc, chlorite, and As distributions, which are commonly associated with valuable sulfides. The results effectively increased OBK and identified priority areas for metallurgical testing. Regarding ARD analysis, intrusive rocks showed higher acidification potential than host rocks. Mineralogical static tests provided more optimistic results compared to geochemical static tests, with silicates identified as potential neutralizing phases and sulfide speciation indicating reduced acidification potential due to the dominance of pyrrhotite. The detailed and continuous data obtained from LIBS allowed mapping sulfide variability and the identification of mineralogy/elements that may impact mineral processing. LIBS also proved effective in preliminary ARD assessments, enhancing the robustness of estimations. The integration of LIBS with other techniques holds significant potential for optimizing OBK.