Mineral quantification at deposit scale using drill-core hyperspectral data: A case study in the Iberian Pyrite Belt

Rosa, Roberto De La; Khodadadzadeh, Mahdi; Tuşa, Laura; Kirsch, Moritz; Gisbert, Guillem; Tornos, Fernando; Tolosana‐Delgado, Raimon; Gloaguen, Richard

doi:10.1016/j.oregeorev.2021.104514

Cited by 16 publications

(14 citation statements)

References 47 publications

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“…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].…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Plessis¹,

Gazley

Tay³

et al. 2021

Minerals

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Quantification of halloysite and kaolinite in clay deposits from X-ray diffraction (XRD) commonly requires extensive sample preparation to differentiate the two phyllosilicates. When assessing hundreds of samples for mineral resource estimations, XRD analyses may become unfeasible due to time and expense. Fourier transform infrared (FTIR) analysis is a fast and cost-effective method to discriminate between kaolinite and halloysite; however, few efforts have been made to use this technique for quantified analysis of these minerals. In this study, we trained machine- and deep-learning models on XRD data to predict the abundance of kaolinite and halloysite from FTIR, chemical composition, and brightness data. The case study is from the Cloud Nine kaolinite–halloysite deposit, Noombenberry Project, Western Australia. The residual clay deposit is hosted in the saprolitic and transition zone of the weathering profile above the basement granite on the southwestern portion of the Archean Yilgarn Craton. Compared with XRD quantification, the predicted models have an R2 of 0.97 for kaolinite and 0.96 for halloysite, demonstrating an excellent fit. Based on these results, we demonstrate that our methodology provides a cost-effective alternative to XRD to quantify kaolinite and halloysite abundances.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Plessis¹,

Gazley

Tay³

et al. 2021

Minerals

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“…Hyperspectral data can be used to derive quantitative mineral maps where appropriate training data is available (Tusa et al, 165 2020;De La Rosa et al, 2021) but the preparation of training samples usually takes days or weeks. This study focuses on spectral tools that can be rapidly applied for qualitative mapping and interpretation.…”

Section: Image Processing 160mentioning

confidence: 99%

Spectral characterisation of hydrothermal alteration associated with sediment-hosted Cu-Ag mineralisation in the Central European Kupferschiefer

Géring¹,

Kirsch

Thiele

et al. 2022

Preprint

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Abstract. The analysis of hydrothermal alteration in exploration drill cores allows to trace fluid-rock interaction processes, identify fluid flow paths and thus determine vectors in mineral systems. Hyperspectral imaging techniques are increasingly being employed to fill the scale gap between lab-based petrographic or geochemical analyses and the typical size of exploration targets. Hyperspectral imaging permits the rapid, cost-efficient and continuous characterisation of alteration mineralogy and texture along entire drill cores with a spatial sampling of a few millimetres. In this contribution, we present the results of an exploratory study on three mineralised drill cores from the Spremberg-Graustein Kupferschiefer-type Cu-Ag deposit in the Lusatia region of Germany. We demonstrate that hyperspectral imaging is a tool well-suited to recognize and track the effects of hydrothermal alteration associated with stratabound hydrothermal mineralization. Micro X-ray fluorescence spectrometry was used to validate the alteration mineral assemblages identified in hyperspectral data acquired in the visible, near, shortwave, mid-wave and long-wave infrared. Spectral features associated with the occurrence of iron oxide, kaolinite, sulphate, and carbonates were identified and mapped. We identify intensive hydrothermal alteration of the sandstones immediately below the Kupferschiefer horizon sensu stricto, spatially associated with or stratigraphically adjacent to Cu-Ag mineralisation. Importantly, we can clearly distinguish two mineralogically distinct styles of alteration (hematite and ferroan carbonate) that bracket high-grade Cu-Ag mineralisation. The occurrence of well crystalline kaolinite in the sandstone units is spatially and genetically related to Cu-Ag mineralisation. Proximal Fe-carbonate and kaolinite alteration have not previously been documented for the high-grade Cu-Ag deposits of the Central European Kupferschiefer. Hematite alteration is well known in Kupferschiefer-type ore deposits. It marks the flow path of oxidising, metal-bearing hydrothermal fluids towards the site of hydrothermal sulphide mineralization. Ferroan carbonate alteration in carbonate rocks located above the main mineralized zone, in contrast, is interpreted to mark hydrothermal fluid discharge from the mineralizing system. Although this study is limited to a small number of drill cores, our results suggest that hyperspectral imaging techniques may be used to identify vectors towards high-grade Cu-Ag mineralisation in Kupferschiefer-type mineral systems.

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“…Therefore, the implementation of spectroscopic techniques such as laser-induced breakdown spectroscopy (LIBS) along with hyperspectral imaging (HSI), has been increasingly evaluated for mineralogical and microchemical characterization in the mining industry. 10–15 Laser induced-breakdown spectroscopy is based on the generation of a transient plasma by the impact of a high-energy laser pulse (∼10 9 –10 11 W cm −1 ) on the surface of the sample, which provides a multi-elemental analysis with minimal sample preparation requirement. Once the matter breakdowns, the optical emission of atoms and ions is captured by an optical arrangement and directed to the spectrometer for the separation of the spectral emission lines.…”

Section: Introductionmentioning

confidence: 99%

“…18,19 Some of the main advantages of coupling HSI and LIBS techniques are their high-speed, spatial resolution, cost-effectiveness, micro-destructive performance, and the generation of a space-resolved data set that can be processed to obtain elemental and chemical images with wide dynamic range of concentration (from ppm to wt%). 10–16 μ-LIBS-based imaging and HSI generate a huge amount of data, which is in the order of thousands to millions of spectrums for the characterization of a small sample surface, e.g. , a few square centimeters.…”

Section: Introductionmentioning

confidence: 99%

Enhanced elemental and mineralogical imaging of Cu-mineralized rocks by coupling μ-LIBS and HSI

Sandoval-Muñoz

Velásquez

Álvarez

et al. 2022

J. Anal. At. Spectrom.

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Mineral quantification at deposit scale using drill-core hyperspectral data: A case study in the Iberian Pyrite Belt

Cited by 16 publications

References 47 publications

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

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

Spectral characterisation of hydrothermal alteration associated with sediment-hosted Cu-Ag mineralisation in the Central European Kupferschiefer

Enhanced elemental and mineralogical imaging of Cu-mineralized rocks by coupling μ-LIBS and HSI

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