Mineral Physicochemistry Underlying Feature-Based Extraction of Mineral Abundance and Composition from Shortwave, Mid and Thermal Infrared Reflectance Spectra

Abstract

Reflectance spectroscopy allows cost-effective and rapid mineral characterisation, addressing mineral exploration and mining challenges. Shortwave (SWIR), mid (MIR) and thermal (TIR) infrared reflectance spectra are collected in a wide range of environments and scales, with instrumentation ranging from spaceborne, airborne, field and drill core sensors to IR microscopy. However, interpretation of reflectance spectra is, due to the abundance of potential vibrational modes in mineral assemblages, non-trivial and requires a thorough understanding of the potential factors contributing to the reflectance spectra. In order to close the gap between understanding mineral-diagnostic absorption features and efficient interpretation of reflectance spectra, an up-to-date overview of major vibrational modes of rock-forming minerals in the SWIR, MIR and TIR is provided. A series of scripts are proposed that allow the extraction of the relative intensity or wavelength position of single absorption and other mineral-diagnostic features. Binary discrimination diagrams can assist in rapidly evaluating mineral assemblages, and relative abundance and chemical composition of key vector minerals, in hydrothermal ore deposits. The aim of this contribution is to make geologically relevant information more easily extractable from reflectance spectra, enabling the mineral resources and geoscience communities to realise the full potential of hyperspectral sensing technologies.

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

“…To accurately differentiate halloysite from kaolinite by XRD requires chemical and physical treatment prior to the XRD analyses; small quantities of halloysite are further challenging to detect in kaolinite-rich samples due to overlapping peaks in XRD patterns [2]. Conversely, FTIR requires minimal sample preparation and is considered more cost and time effective than XRD; the individual intensities in FTIR spectra of multimineral analyses can contain information on mineral abundances [10,13].…”

“…The FTIR technique requires minimal sample preparation and is more cost and time effective than XRD. Coupled with machine learning (ML) approaches, FTIR data have been used for mineral identification and to predict parameters such as elemental abundance, grain size, density, total organic carbon, pH, and other properties of soils and rocks [3][4][5][6][7][8][9][10][11][12]. While FTIR is commonly employed to analyse these physicochemical parameters, it is less commonly applied in the quantification of mineral abundances.…”

“…While FTIR is commonly employed to analyse these physicochemical parameters, it is less commonly applied in the quantification of mineral abundances. Yet, the individual intensities in FTIR spectra of multimineral analyses reveal information on mineral abundances [13], which can be used as a tool in mineral exploration [10].…”

A Qualitative Examination of the Iron Boomerang and Trends in Spectral Metrics across Iron Ore Deposits in Western Australia

“…This has led to spectral region-specific metrics that are used to leverage the strength of each spectral region and, in many cases, is deposit specific [1,[5][6][7]. In the combined visible (VIS), near-infrared (NIR) and short-wave infrared (SWIR) between 350 and 2500 nm, the 1300-2500 nm spectral region has alteration mineral assemblages associated with hydrothermal base and precious metal deposits which can often be defined by the absorption features present [8][9][10][11][12][13], while absorptions in the VIS and NIR between 350 and 1300 nm can be used to infer iron-bearing minerals [2,14,15].…”

“…At much longer wavelengths in the thermal infrared (TIR) spectral region, absorptions between 6000 and 14,000 nm are used to assist in mineral species differentiation due to overlapping and confounding absorptions, such as carbonates and chlorites, in the SWIR as well as the identification of minerals that do not exhibit any identifiable absorptions in the VIS, NIR and SWIR such as quartz and feldspars [13,16,17].…”