Vibrational Spectroscopy for Mineral Exploration, Mining, and Environmental Monitoring

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Crystallography and Physical Chemistry of Minerals & Nanominerals".

Deadline for manuscript submissions: closed (18 December 2020) | Viewed by 20984

Special Issue Editor


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Guest Editor
Commonwealth Scientific and Industrial Research Organization (CSIRO) Mineral Resources, Perth, Australia
Interests: mineralogy; geochemistry; reflectance spectroscopy; remote sensing; spectral sensing solutions to mineral exploration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vibrational spectroscopy is increasingly used by the exploration and mining industry for characterization of mineral assemblages and prediction of, for example, geochemical and geometallurgical parameters. Visible and infrared sensing technologies are cost effective tools for rapidly acquiring large amounts of reflectance spectra from the continental to micro scales, enabling geologists to recognize patterns that would be impossible to identify with conventional mineralogical and geochemical analytical tools. Applications in the resources sector range from early stages of exploration and baseline monitoring to resource characterization, mine site monitoring, and predicting feed to processing plants and tailings dams. The non-destructive collection of geoscience data and objective sample characterization further add value to the large suite of currently available remote and proximal sensing instruments.

However, despite the rapidly increasing demand for accurate and rapid mineral characterization by means of reflectance spectroscopy, published literature is lacking that clarifies the physicochemical processes that lead to the spectral signatures encountered in the collected reflectance spectra. This has led to ambiguous mineral characterization and higher-level products, and even interpretations of spectral signatures that are not supported by the underlying physicochemical processes or the limitations of the respective applied technologies (e.g., interpreting mineral species from multispectral remotes sensing data). Many published case studies do not use the physicochemical information contained within reflectance spectra, but instead rely on pure statistical evidence such as that obtained from conventional unmixing methods. The published spectral reference libraries are sparse or lack sufficient validation work, resulting in significant differences in interpretations depending on which spectral reference library was applied.

This Special Issue entitled "Vibrational Spectroscopy for Mineral Exploration, Mining, and Environmental Monitoring" features a collection of case studies that demonstrate the wide range of applications of vibrational spectroscopy in the mining value chain. All contributions strongly focus on (1) clarifying the physicochemical processes that lead to the spectral signatures encountered in the collected reflectance spectra and (2) validating the mineralogical interpretation derived from reflectance spectra with, for example, independent mineralogical and geochemical analyses.

Dr. Carsten Laukamp
Guest Editor

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Keywords

  • Mineralogy
  • Crystal chemistry
  • Vibrational spectroscopy
  • Reflectance spectroscopy
  • Proximal sensing
  • Remote sensing
  • Exploration
  • Regolith characterisation
  • Mining
  • Resource characterisation

Published Papers (6 papers)

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Research

37 pages, 4450 KiB  
Article
Mineral Physicochemistry Underlying Feature-Based Extraction of Mineral Abundance and Composition from Shortwave, Mid and Thermal Infrared Reflectance Spectra
by Carsten Laukamp, Andrew Rodger, Monica LeGras, Heta Lampinen, Ian C. Lau, Bobby Pejcic, Jessica Stromberg, Neil Francis and Erick Ramanaidou
Minerals 2021, 11(4), 347; https://doi.org/10.3390/min11040347 - 26 Mar 2021
Cited by 39 | Viewed by 6780
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 [...] Read more.
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. Full article
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16 pages, 4190 KiB  
Article
Feature Extraction and Clustering of Hyperspectral Drill Core Measurements to Assess Potential Lithological and Alteration Boundaries
by Andrew Rodger, Adrian Fabris and Carsten Laukamp
Minerals 2021, 11(2), 136; https://doi.org/10.3390/min11020136 - 29 Jan 2021
Cited by 12 | Viewed by 2623
Abstract
A workflow incorporating hyperspectral reflectance data, hull corrections, absorption feature extraction and clustering is presented. The workflow is applied to dense hyperspectral datasets, as collected by hyperspectral drill core logging systems. The extracted absorption features of the reflectance spectra collected from drill cores [...] Read more.
A workflow incorporating hyperspectral reflectance data, hull corrections, absorption feature extraction and clustering is presented. The workflow is applied to dense hyperspectral datasets, as collected by hyperspectral drill core logging systems. The extracted absorption features of the reflectance spectra collected from drill cores are shown to form assemblage clusters when plotting the wavelength position of the first, second and third deepest absorption features in two and three dimensions. Using an unsupervised clustering method to establish clusters based on the extracted absorption features yields viewable down hole distributions of similar mineral assemblages. The proposed workflow has the potential for the rapid identification of differing lithologies, alteration and/or weathering overprints. Application of the workflow with no a-priori assumptions about the composition of the potential mineral assemblages provides a means of generating an informative overview of the dataset that is not biased or constrained by preconceptions. The workflow can easily be added to the current workflows of geologists whom are working with dense hyperspectral data to provide an overview of the potential down hole mineral assemblages and aid in the visual logging process or assist in quickly identifying areas for more detailed observation. Furthermore, key mineralogical parameters for resource characterisation, such as the presence of clay minerals can be assessed in a cost and time efficient manner. The proposed workflow is applied to spectra collected from four different drill cores collected in the Gawler Craton located in South Australia and demonstrates the potential outlined above. Full article
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18 pages, 58583 KiB  
Article
Garnet Characteristics Associated with Jiama Porphyry-Skarn Cu Deposit 1# Skarn Orebody, Tibet, Using Thermal Infrared Spectroscopy
by Yi-Ru Huang, Na Guo, Ju-Xing Tang, Wei-Xin Shi and Feng-Qin Ran
Minerals 2021, 11(1), 5; https://doi.org/10.3390/min11010005 - 23 Dec 2020
Cited by 7 | Viewed by 2747
Abstract
Field measurements of the thermal infrared (TIR) reflectance from drill hole samples proved to be an effective method to map variations in garnet species associated with hydrothermal alteration zonation of the Jiama porphyry-skarn Cu deposit 1# skarn orebody, Tibetan Plateau, China. The [...] Read more.
Field measurements of the thermal infrared (TIR) reflectance from drill hole samples proved to be an effective method to map variations in garnet species associated with hydrothermal alteration zonation of the Jiama porphyry-skarn Cu deposit 1# skarn orebody, Tibetan Plateau, China. The TIR mineral spectral information was combined with electron probe micro-analysis (EPMA) measurements to provide geological insights on effectively determining (a) garnet end components and providing a format for further research on the type and genesis of the deposit; (b) the significance of the characteristic spectrum of garnet to the variation of mineralization environment; (c) the relationship between the characteristic spectrum of garnet and Fe/Al content; (d) the garnet characteristic spectrum to the economic mineralization. The results suggest that garnet characteristics of the thermal infrared spectrum can be used as an indicator for skarn deposit prospecting. Full article
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21 pages, 4778 KiB  
Article
Infrared Thermography: A Method to Visualise and Analyse Sulphide Oxidation
by Marjan Knobloch and Bernd G. Lottermoser
Minerals 2020, 10(11), 933; https://doi.org/10.3390/min10110933 - 22 Oct 2020
Cited by 4 | Viewed by 2512
Abstract
Environmental testing of sulphidic ores and wastes aims to assess the release of metals and metalloids at acid pH conditions and the samples’ likelihood to produce acid rock drainage (ARD). However, the majority of established ARD tests measure geochemical properties of samples and [...] Read more.
Environmental testing of sulphidic ores and wastes aims to assess the release of metals and metalloids at acid pH conditions and the samples’ likelihood to produce acid rock drainage (ARD). However, the majority of established ARD tests measure geochemical properties of samples and cannot visualise the exothermic oxidation reactions and their intensity leading to metalliferous drainage in all pH environments. This paper proposes a new protocol to detect and visualise the sulphide oxidation in ores and wastes. Six nearly monomineralic sulphides were crushed, sieved to two size fraction (0.09–4 mm), milled to powder and treated with H2O2. The thermal energy released upon sulphide oxidation was optically detected and temperatures measured using a portable infrared thermographic camera. Based on temperature–time progression curves, the oxidation reactivity of sulphides was derived from high to low: chalcopyrite > arsenopyrite > pyrite > sphalerite > stibnite > galena, which depends on grain size, amount of sulphides and other non-sulphide mineral phases present in the sample material. The study demonstrates that the application of H2O2 to sulphide sample powders and subsequent visualisation of the treated materials using a thermographic camera represents a rapid technique in revealing the presence of oxidising sulphides under all pH conditions. Full article
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21 pages, 2657 KiB  
Article
Data Fusion for the Prediction of Elemental Concentrations in Polymetallic Sulphide Ore Using Mid-Wave Infrared and Long-Wave Infrared Reflectance Data
by Feven Desta, Mike Buxton and Jeroen Jansen
Minerals 2020, 10(3), 235; https://doi.org/10.3390/min10030235 - 5 Mar 2020
Cited by 9 | Viewed by 3086
Abstract
The increasing availability of complex multivariate data yielded by sensor technologies permits qualitative and quantitative data analysis for material characterization. Multivariate data are hard to understand by visual inspection and intuition. Thus, data-driven models are required to derive study-specific insights from large datasets. [...] Read more.
The increasing availability of complex multivariate data yielded by sensor technologies permits qualitative and quantitative data analysis for material characterization. Multivariate data are hard to understand by visual inspection and intuition. Thus, data-driven models are required to derive study-specific insights from large datasets. In the present study, a partial least squares regression (PLSR) model was used for the prediction of elemental concentrations using the mineralogical techniques mid-wave infrared (MWIR) and long-wave infrared (LWIR) combined with data fusion approaches. In achieving the study objectives, the usability of the individual MWIR and LWIR datasets for the prediction of the concentration of elements in a polymetallic sulphide deposit was assessed, and the results were compared with the outputs of low- and mid-level data fusion methods. Prior to low-level data fusion implementation, data filtering techniques were applied to the MWIR and LWIR datasets. The pre-processed data were concatenated and a PLSR model was developed using the fused data. The mid-level data fusion was implemented by extracting features using principal component analysis (PCA) scores. As the models were applied to the MWIR, LWIR, and fused datasets, an improved prediction was achieved using the low-level data fusion approach. Overall, the acquired results indicate that the MWIR data can be used to reliably predict a combined Pb–Zn concentration, whereas LWIR data has a good correlation with the Fe concentration. The proposed approach could be extended for generating indicative element concentrations in polymetallic sulphide deposits in real-time using infrared reflectance data. Thus, it is beneficial in providing elemental concentration insights in mining operations. Full article
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16 pages, 4428 KiB  
Article
Near-Infrared Spectroscopic Study of Heavy-Metal-Contaminated Loess Soils in Tongguan Gold Area, Central China
by Min Yang, Youning Xu, Jianghua Zhang, Huaqing Chen, San Liu, Weiliang Li and Ying Hao
Minerals 2020, 10(2), 89; https://doi.org/10.3390/min10020089 - 21 Jan 2020
Cited by 6 | Viewed by 2346
Abstract
Loess soil is a kind of widespread soil type in northwest China. Human engineering activities such as mining have caused numerous problems related to heavy metal pollution in soils, which threaten people’s health. The band formation mechanism of the near-infrared (NIR) spectral features [...] Read more.
Loess soil is a kind of widespread soil type in northwest China. Human engineering activities such as mining have caused numerous problems related to heavy metal pollution in soils, which threaten people’s health. The band formation mechanism of the near-infrared (NIR) spectral features in loess soils forms the theoretical basis for the study of the soil environment by hyperspectral remote sensing. Some NIR features of loess soils will shift because of the variations of the soil composition and microstructure after they adsorb heavy metal cations. In this study, we focused on the heavy metal adsorption of the illite, smectite, and illite–smectite (I/S) mixed layer in loess soils; evaluated the pollution by Nemerow indexing; applied X-ray diffraction (XRD), mid-infrared (MIR) spectral analysis, and inductively coupled plasma mass spectrometry (ICP-MS); and carefully observed the shift behavior of the MIR and NIR features. Then, the NIR bands were assigned to MIR bands according to the vibration behavior. Furthermore, the relationships between the NIR band positions and the six heavy metal cations as well as the Nemerow index were investigated via multiregression and simple linear correlation methods. Finally, the relationship obtained from the experiments was analyzed using the physical and chemical mechanisms of the heavy metal cations in the clay minerals. These findings may benefit the application of NIR and remote sensing techniques for detecting heavy-metal-polluted soils. Full article
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