Publication Date:
2023-11-24
Description:
〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉The inherent complexity of underground mining requires highly selective ore extraction and adaptive mine planning. Repeated geological face mapping and reinterpretation throughout mine life is therefore routine in underground mines. Hyperspectral imaging (HSI) has successfully been applied to enhance geological mapping in surface mining environments, but remains a largely unexplored opportunity in underground operations due to challenges associated with illumination, wet surfaces and data corrections. In this study, we propose a workflow that paves the way for the operational use of HSI in active underground mines. In a laboratory set‐up, we evaluated different hyperspectral sensors and lighting set‐ups as well as the effect of surface moisture. We then acquired hyperspectral data in an underground mine of the Zinnwald/Cínovec Sn‐W‐Li greisen‐type deposit in Germany. These data were corrected for illumination effects, back‐projected into three dimensions and then used to map mineral abundance and estimate Li content across the mine face. We validated the results with handheld laser‐induced breakdown spectroscopy. Despite remaining challenges, we hope this study will help establish hyperspectral sensors in the extractive industry as a means to increase the volume and efficiency of raw material supply, advance digitalisation, and reduce the environmental footprint and other risks associated with underground mining.〈/p〉
Description:
〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉This study proposes a workflow for using hyperspectral imaging for geological mapping in underground mining. The authors evaluated sensors and lighting set‐ups in a lab and acquired data in a German underground mine. The corrected data were used to map mineral abundance and estimate Li content, validated with laser‐induced breakdown spectroscopy. Despite challenges, this study aims to establish hyperspectral sensors in the extractive industry to increase raw material supply, advance digitalisation, and reduce environmental impact and mining risks.〈boxed-text position="anchor" content-type="graphic" id="phor12457-blkfxd-0001" xml:lang="en"〉〈graphic position="anchor" id="jats-graphic-1" xlink:href="urn:x-wiley:0031868X:media:phor12457:phor12457-toc-0001"〉
〈/graphic〉〈/boxed-text〉〈/p〉
Description:
https://doi.org/10.14278/rodare.2078
Description:
https://tinyurl.com/Zinnwald
Keywords:
ddc:622.1
;
hyperspectral
;
lithium
;
mineral mapping
;
point cloud
;
underground mining
Language:
English
Type:
doc-type:article
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