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

Abstract

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 (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.

Quantification of Kaolinite and Halloysite Using Machine Learning from FTIR, XRF, and Brightness Data

“…Previous studies have demonstrated the effectiveness of FTIR to quantify kaolinite and halloysite and have highlighted the benefits of FTIR over XRD when concentrations of kaolinite and halloysite in samples are low [2,13,15]. Researchers recently started implementing ML on mineral quantification based on spectral data [11,[17][18][19]. For example, hyperspectral data collected on drill core samples paired with hierarchical density-based clustering algorithms were reported to assist in the rapid identification of differing lithologies, alteration, and/or weathering overprints [12].…”

“…However, the quantitative discrimination between halloysite and kaolinite remains problematic. Machine learning is a fast-evolving technique that was recently employed in mineral quantification based on spectral data [17][18][19]. These studies imply that using an ML approach on spectral and other sample characterisation techniques may result in robust prediction of kaolinite and halloysite abundance.…”

A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

“…ML algorithms such as artificial neural network (ANN), support vector machine (SVM), regression tree (RT), and random forest (RF) are powerful data driven methods that are becoming extremely popular in such applications as the mapping of mineral prospectivity [26][27][28], mapping geochemical anomalies [29][30][31], geological mapping [32][33][34][35], drill-core mapping [36][37][38], and mineral phase segmentation for X-ray microcomputed tomography data [39][40][41].…”

Resolution Enhancement for Drill-Core Hyperspectral Mineral Mapping

“…Drill-core hyperspectral data have been analyzed following well-established methods such as minimum wavelength mapping, band ratios, spectral distance measurements using reference libraries, endmember extraction, and unmixing [8,[10][11][12]. Machine learning techniques have also been implemented for the analysis of drill-core hyperspectral data in recent years to ameliorate the automation of analyses and to provide more robust results, especially by using supervised methods [13][14][15][16][17]. However, supervised learning algorithms require reference data (i.e., training sets) that can be difficult to obtain for drill-cores since, for example, they are not usually labelled at the millimeter scale.…”