Quantitative Mineral Mapping of Drill Core Surfaces II: Long-Wave Infrared Mineral Characterization Using μXRF and Machine Learning

Long-wave infrared (LWIR) spectra can be interpreted using a Random Forest machine learning approach to predict mineral species and abundances. In this study, hydrothermally altered carbonate rock core samples from the Fourmile Carlin-type Au discovery, Nevada, were analyzed by LWIR and micro-X-ray fluorescence (mu XRF). Linear programming-derived mineral abundances from quantified mu XRF data were used as training data to construct a series of Random Forest regression models. The LWIR Random Forest models produced mineral proportion estimates with root mean square errors of 1.17 to 6.75% (model predictions) and 1.06 to 6.19% (compared to quantitative X-ray diffraction data) for calcite, dolomite, kaolinite, white mica, phlogopite, K-feldspar, and quartz. These results are comparable to the error of proportion estimates from linear spectral deconvolution (+/- 7-15%), a commonly used spectral unmixing technique. Having a mineralogical and chemical training data set makes it possible to identify and quantify mineralogy and provides a more robust and meaningful LWIR spectral interpretation than current methods of utilizing a spectral library or spectral end-member extraction. Using the method presented here, LWIR spectroscopy can be used to overcome the limitations inherent with the use of short-wave infrared (SWIR) in fine-grained, low reflectance rocks. This new approach can be applied to any deposit type, improving the accuracy and speed of infrared data interpretation.

Automated summary

In this study, LWIR spectra of hydrothermally altered carbonate rock core samples were analyzed using Random Forest machine learning approach to predict mineral species and abundances. The Random Forest models showed comparable accuracy to traditional spectral unmixing techniques, providing a more robust and meaningful interpretation of LWIR spectra. This new approach has the potential to improve the accuracy and speed of infrared data interpretation for various deposit types.