SwePub
Sök i LIBRIS databas

  Utökad sökning

onr:"swepub:oai:DiVA.org:ltu-85847"
 

Sökning: onr:"swepub:oai:DiVA.org:ltu-85847" > Multi-Scale X-Ray C...

Multi-Scale X-Ray Computed Tomography Analysis to Aid Automated Mineralogy in Ore Geology Research

Warlo, Mathis (författare)
Luleå tekniska universitet,Geovetenskap och miljöteknik
Bark, Glenn (författare)
Luleå tekniska universitet,Geovetenskap och miljöteknik
Wanhainen, Christina (författare)
Luleå tekniska universitet,Geovetenskap och miljöteknik
visa fler...
Butcher, Alan R. (författare)
Geological Survey of Finland (GTK), Espoo, Finland
Forsberg, Fredrik (författare)
Luleå tekniska universitet,Strömningslära och experimentell mekanik
Lycksam, Henrik (författare)
Luleå tekniska universitet,Strömningslära och experimentell mekanik
Kuva, Jukka (författare)
Geological Survey of Finland (GTK), Espoo, Finland
visa färre...
 (creator_code:org_t)
2021-12-10
2021
Engelska.
Ingår i: Frontiers in Earth Science. - : Frontiers Media S.A.. - 2296-6463. ; 9
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • Ore characterization is crucial for efficient and profitable production of mineral products from an ore deposit. Analysis is typically performed at various scales (meter to microns) in a sequential fashion, where sample volume is reduced with increasing spatial resolution due to the increasing costs and run times of analysis. Thus, at higher resolution, sampling and data quality become increasingly important to represent the entire ore deposit. In particular, trace metal mineral characterization requires high-resolution analysis, due to the typical very fine grain sizes (sub-millimeter) of trace metal minerals. Automated Mineralogy (AM) is a key technique in the mining industry to quantify process-relevant mineral parameters in ore samples. Yet the limitation to two-dimensional analysis of flat sample surfaces constrains the sampling volume, introduces an undesired stereological error, and makes spatial interpretation of textures and structures difficult. X-ray computed tomography (XCT) allows three-dimensional imaging of rock samples based on the x-ray linear attenuation of the constituting minerals. Minerals are visually differentiated though not chemically classified. In this study, decimeter to millimeter large ore samples were analyzed at resolutions from 45 to 1 μm by AM and XCT to investigate the potential of multi-scale correlative analysis between the two techniques. Mineralization styles of Au, Bi-minerals, scheelite, and molybdenite were studied. Results show that AM can aid segmentation (mineralogical classification) of the XCT data, and vice versa, that XCT can guide (sub-)sampling (e.g., for heavy trace minerals) for AM analysis and provide three-dimensional context to the two-dimensional quantitative AM data. XCT is particularly strong for multi-scale analysis, increasingly higher resolution scans of progressively smaller volumes (e.g., by mini-coring), while preserving spatial reference between (sub-)samples. However, results also reveal challenges and limitations with the segmentation of the XCT data and the data integration of AM and XCT, particularly for quantitative analysis, due to their different functionalities. In this study, no stereological error could be quantified as no proper grain separation of the segmented XCT data was performed. Yet, some well-separated grains exhibit a potential stereological effect. Overall, the integration of AM with XCT improves the output of both techniques and thereby ore characterization in general. 

Ämnesord

NATURVETENSKAP  -- Geovetenskap och miljövetenskap -- Multidisciplinär geovetenskap (hsv//swe)
NATURAL SCIENCES  -- Earth and Related Environmental Sciences -- Geosciences, Multidisciplinary (hsv//eng)

Nyckelord

x-ray computed tomography
automated mineralogy
mineral segmentation
ore
trace metals
Malmgeologi
Ore Geology
Strömningslära
Fluid Mechanics
Experimental Mechanics
Experimentell mekanik

Publikations- och innehållstyp

ref (ämneskategori)
art (ämneskategori)

Hitta via bibliotek

Till lärosätets databas

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy