Quantitative Mineral Mapping of Drill Core Surfaces II: Long-Wave Infrared Mineral Characterization UsingμXRF and Machine Learning
| Autor: | Rocky D. Barker, Shaun L.L. Barker, Matthew J. Cracknell, Elizabeth D. Stock, Geoffrey Holmes |
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| Rok vydání: | 2021 |
| Předmět: |
010504 meteorology & atmospheric sciences
Infrared business.industry Dolomite Geology 010502 geochemistry & geophysics Machine learning computer.software_genre 01 natural sciences Spectral line Random forest Root mean square Geophysics Geochemistry and Petrology Economic Geology Deconvolution Artificial intelligence Spectroscopy business Quartz computer 0105 earth and related environmental sciences |
| Zdroj: | Economic Geology. 116:821-836 |
| ISSN: | 1554-0774 0361-0128 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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