A portable Raman device with a 532 nm excitation laser and a portable infrared spectrometer with ATR (Attenuated Total Reflection) mode were used to analyse the spectral features associated with the identification and compositional variation of Ca-Mg-Fe-Mn natural carbonate minerals with a calcite structure (calcite, ankerite, dolomite, siderite, rhodochrosite, and magnesite). A systematic study of the variations of the peak positions with various compositional ratios was carried out. Most of the band positions were shifted to lower wavenumbers with increasing ionic radius or atomic mass of the divalent cations but the band of the translational lattice (T) mode in Raman and the symmetric bending (ν₄) band in the mid-infrared were the most sensitive. Therefore, the elemental variation of the Ca-Mg-Fe-Mn ratio in this carbonate series can be estimated from Raman and infrared band positions from spectra acquired with portable spectrometers.

Mineralogical data acquired from drillcores provide important constraints for resource estimation, geometallurgical modelling, mineral exploration, and geological interpretation. While hyperspectral imaging is rapidly gaining traction for these applications, it lacks the ability to accurately quantify mineral abundances without extensive calibration data. Here, we build on previous work to demonstrate and benchmark workflows that combine scanning electron microscope (SEM) mineral maps with large-extent multimodal hyperspectral imaging data. The goal is to relate hyperspectral features and mineral abundances using supervised machine learning models, and then apply these models to infer mineralogy across entire drillcores. We adapt the learning process to the non-uniform (unbalanced) composition of most rocks, and achieve reasonable accuracy for most rock-forming minerals. However, we also find that prediction accuracy depends strongly on the representativity of training data—so models often fail to produce accurate maps of rare and accessory minerals. Robust, adaptive and ideally semi-automated sampling approaches might address this shortcoming by identifying locations which ensure optimal coverage of hyperspectral variance. We also emphasise that upscaling from SEM to drillcore scale inevitably involves extrapolation, meaning predictions should always be validated. However, once validated, upscaled mineralogy predictions could provide crucial quantitative data that bridge the scale gap between petrographic observations, metallurgical tests, and geometallurgical models.

Portable Raman spectroscopy offers a rapid, non-destructive approach for identifying and estimating mineral chemistry in the field, but its use for determining carbonate compositions has been limited. In this study, we present a matrix-based method to estimate Ca, Mg, and Fe concentrations in carbonate minerals from Raman spectra. The Raman spectra were acquired for pure end-member carbonates (calcite, ankerite, dolomite, siderite, magnesite) and used to construct a 5×5 calibration matrix of normalized peak intensities, requiring a single detectable vibrational mode per carbonate phase. Raman spectra from unknown samples were then projected onto this matrix to derive their mineral proportions, which were converted to elemental abundances. The method was validated against portable Raman and atomic absorption spectroscopy (AAS)-calibrated pXRF measurements from our internal database and against micro-Raman and electron microprobe data from the external RRUFF database. Synthetic binary carbonate mixtures (calcite-ankerite, calcite-dolomite, and ankerite-dolomite) produced highly linear relationships between Raman intensity ratios and mineral volume fractions (R² > 0.99), and the Raman-based compositions matched known values to within ~4 wt.%. We also demonstrate that spectral overlaps (e.g. quartz-bearing ankerite) can be effectively corrected by Voigt-profile decomposition, restoring accurate composition estimates. This portable Raman matrix method thus provides reliable in situ estimation of carbonate chemistry, enabling rapid field assessment of carbonate mineralogy and geochemistry in geological and planetary exploration contexts.