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Quantitative Characterization of Gold Grain Shape Using Image analysis

delete2026-06-07
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OA
AI
A
Arnaud Back
L
L.P. Bédard
A
A. Barry
R
R. Girard
DOI：10.1016/j.acags.2026.100366delete
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Abstract

Abstract

En 中文
• Novel PCA-based shape analysis of gold grains using 73 cross-disciplinary descriptors • Large- and small-scale morphological groups reveal sedimentary process impacts on grains • Quantitative alternative to visual estimation of grain shape • Classification of gold grain shape for glaciated terrain gold exploration
Keywords:
gold exploration
quantitative descriptors
computer vision
statistical analysis
image processing
petrography
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Applied Computing and Geosciences cover
Applied Computing and Geosciences
IF:
3.2
Papers:
159
Citations:
375

Organization

Institut National de la Recherche Scientifique cover
Institut National de la Recherche Scientifique
Scholars:
190
Papers: 81
Citations: 6.9K
G
geosciences
Scholars:
101
Papers: 71
Citations: 0
U
Université du Québec
Scholars:
138
Papers: 63
Citations: 157
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