Abstract
AbstractLong-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 (μXRF). Linear programming-derived mineral abundances from quantified μXRF 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.
Highlights
Short-wave infrared (SWIR) spectroscopy techniques are increasingly utilized in mining and mineral exploration to recognize and classify various mineral species of significance for exploration and mineral processing (Ahmed, 2010; Browning, 2014; Maydagán et al, 2016; Bedell et al, 2017)
In order to overcome the lack of reflectance and the difficulty of distinguishing minerals such as quartz and feldspars in SWIR, long-wave infrared (LWIR) spectroscopy has been implemented by hyperspectral core logging systems such as the Hylogger-3 (Mauger et al, 2012; Arne et al, 2016) and SisuROCK (Tappert et al, 2015)
We demonstrate the use of micro-X-ray fluorescence mapping, supported by machine learning, to provide robust, quantitative analysis of LWIR spectra to predict mineral abundances within LWIR-scanned rock samples
Summary
Short-wave infrared (SWIR) spectroscopy techniques are increasingly utilized in mining and mineral exploration to recognize and classify various mineral species of significance for exploration and mineral processing (Ahmed, 2010; Browning, 2014; Maydagán et al, 2016; Bedell et al, 2017). Recent efforts have been made to apply infrared spectroscopy techniques such as handheld and benchtop infrared analyzers (Ahmed, 2010; Bradford, 2008; Ahmed et al, 2009; Mateer, 2010; Browning, 2014), and, most recently, infrared core scanning technologies (Barker, 2017; Barker and Ridley, 2020) to Carlin-type gold deposits in Nevada. The utility of SWIR for Carlin-type gold deposits, has been limited due to the low reflectivity of samples which often produces flat, undiagnostic spectra. Many minerals contained within these fine-grained samples have characteristic peaks that overlap within the spectral range used in this
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