Abstract
Reflectance spectroscopy is a nondestructive, rapid, and easy-to-use technique which can be used to assess the composition of rocks qualitatively or quantitatively. Although it is a powerful tool, it has its limitations especially when it comes to measurements of rocks with a phaneritic texture. The external variability is reflected only in spectroscopy and not in the chemical-mineralogical measurements that are performed on crushed rock in certified laboratories. Hence, the spectral variability of the surface of an uncrushed rock will, in most cases, be higher than the internal chemical-mineralogical variability, which may impair statistical models built on field measurements. For this reason, studying ore-bearing rocks and evaluating their spectral variability in different scales is an important procedure to better understand the factors that may influence the qualitative and quantitative analysis of the rocks. The objectives are to quantify the spectral variability of three types of altered granodiorite using well-established statistical methods with an upscaling approach. With this approach, the samples were measured in the laboratory under supervised ambient conditions and in the field under semisupervised conditions. This study further aims to conclude which statistical method provides the best practical and accurate classification for use in future studies. Our results showed that all statistical methods enable the separation of the rock types, although two types of rocks have exhibited almost identical spectra. Furthermore, the statistical methods that supplied the most significant results for classification purposes were principal component analysis combined with k-nearest neighbor with a classification accuracy for laboratory and field measurements of 68.1% and 100%, respectively.
Highlights
Over the past few decades, many studies have investigated the spectral properties of metal-bearing minerals and clay minerals present in igneous rocks using the visible light, near and shortwave infrared (VNIR-SWIR) spectroscopy. is spectral domain between 350–2500 nm has been proven to be a reliable and rapid tool for detecting and identifying clayJournal of Spectroscopy minerals [1, 2] or for predicting the concentrations of Cu in waste dump material [3]
E high spectral standard deviation (SSD) values in the laboratory are caused by differences in small mineral clusters and oxidation processes within each rock piece, which are not observed in the field measurements due to the large area covered by the measurements. e presence and quantity of various minerals in the rocks generate spectral absorptions to occur at different wavelengths as discussed in 2.1. erefore, due to the variability in the quantity of minerals in the rocks, we can expect high spectral variability at the same wavelengths
M3 obtained the second highest prediction in Level 1 (L1) with 71.4% success. e poor results achieved in Level 2 (L2) are due to the small number of samples in the principal component analysis (PCA) space accompanied with k 1 with prediction of 0%. e best overall prediction was obtained in Level 0x (L0x) with 100% success for all rock types. e average predictions of the rock types in Level 0 (L0), L1, L2, and L0x were 72.5%, 57%, 0%, and 100%, respectively
Summary
Over the past few decades, many studies have investigated the spectral properties of metal-bearing minerals and clay minerals present in igneous rocks using the visible light, near and shortwave infrared (VNIR-SWIR) spectroscopy. is spectral domain between 350–2500 nm has been proven to be a reliable and rapid tool for detecting and identifying clay. Hydroxyl-bearing minerals including clay and sulfate groups and carbonates in the alteration assemblages show spectral absorption features due to vibrational processes of Al-OH at 2200 nm, Mg-OH at 2300 nm, and CO3 groups at 2350 nm [12, 13] Phyllosilicates, such as kaolinite, montmorillonite, and chlorite which are Al-Si-(OH) and Mg-Si-(OH)-bearing minerals and the Ca-Al-Si-(OH)bearing minerals such as the epidote group, can be identified using the SWIR region [2, 14,15,16]. Us, for classification purposes as well as for quantification and geochemical properties, it is important to study the rocks’ microcomplexity which is affected by the mineral chemistry and structure, grain size, and texture This microcomplexity affects the spectral properties and spectral variability at different observational scales [10]. E main objective of this study is to examine and quantify the spectral variability of three types of granodiorites to bridge the gap between laboratory measurements and field data and enable a more precise classification of the selected rock types. is is achieved by examining wellestablished statistical methods: mean and spectral standard deviation (SSD), SAM, average sum of deviation square (ASDS) and by performing PCA followed by k-nearest neighbors (kNN) algorithm for classification purposes
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