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Mineral Exploration in the Glaciated Terrains: Advanced Geochemical and Mineralogical...
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Mineral Exploration in the Glaciated Terrains: Advanced Geochemical and Mineralogical Techniques in the Analysis of Glacial Sediments and Processes

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Exploration Methods and Applications".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 4339

Special Issue Editors

Prof. Dr. Pertti Sarala

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Guest Editor
Oulu Mining School, University of Oulu, P.O. Box 3000, 90014 Oulu, Finland
Interests: applied geochemistry; mineral exploration; surface geochemical methods; glacial geology
Dr. Isabelle McMartin

E-Mail Website
Guest Editor
Geological Survey of Canada, Ottawa, ON K1A 0E8, Canada
Interests: glacial geology; glacial geomorphology; applied geochemistry; Laurentide Ice Sheet history
Dr. Marja Lehtonen

E-Mail Website
Guest Editor
Geological Survey of Finland, 02150 Espoo, Finland
Interests: applied mineralogy; mineral exploration; indicator minerals; quaternary geology

Special Issue Information

Dear Colleagues,

Till geochemistry and heavy mineralogical research have been long-standing and traditional tools in mineral exploration in glaciated terrains. Although these traditional methods are still largely in use, many advanced techniques, such as surface geochemical methods, on-site geochemical and mineralogical analyzers, automated indicator mineral-identification techniques, isotope methods, statistical data processing and prospectivity modelling have significantly improved the use of glacially transported sediments and soils as a part of the exploration process. Together with an improved understanding of the formation processes of glacigenic deposits and the migration of elements during secondary dispersion, these techniques offer novel opportunities for facing the increasing challenges of exploration.

This Special Issue welcomes all type of papers on geochemical and heavy/indicator mineralogical research used in the mineral exploration of different commodities in the glaciated terrains. In particular, papers dealing with new, advanced research methods and analytical techniques are preferred.

Prof. Dr. Pertti Sarala
Dr. Isabelle McMartin
Dr. Marja Lehtonen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Minerals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • geochemistry 
  • till 
  • heavy minerals 
  • indicator minerals 
  • surface geochemical methods 
  • secondary dispersion 
  • geochemical anomaly 
  • statistical analysis 
  • prospecting 
  • weathered bedrock

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Published Papers (2 papers)

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Research

30 pages, 11950 KiB  
Article
Till Geochemistry as a Vector to Metasomatic Iron and Alkali-Calcic Systems and Associated Deposits in the Great Bear Magmatic Zone, Northwest Territories, Canada
by Philippe X. Normandeau, Isabelle McMartin and Louise Corriveau
Minerals 2024, 14(6), 547; https://doi.org/10.3390/min14060547 - 26 May 2024
Cited by 1 | Viewed by 1506
Abstract
Recent advances in the characterization of metasomatic iron and alkali-calcic (MIAC) systems with associated iron-oxide apatite (IOA) prospects and iron-oxide–copper–gold (IOCG) and metasomatic cobalt deposits of the Great Bear magmatic zone were used to determine if the geochemistry of glacial sediments can unveil [...] Read more.
Recent advances in the characterization of metasomatic iron and alkali-calcic (MIAC) systems with associated iron-oxide apatite (IOA) prospects and iron-oxide–copper–gold (IOCG) and metasomatic cobalt deposits of the Great Bear magmatic zone were used to determine if the geochemistry of glacial sediments can unveil pathfinder elements indicative of mineralization and associated alteration. Analysis of variance within bedrock lithogeochemical (n = 707 samples) and till geochemical datasets (n = 92 samples) are compared. Results show that Fe, Co, Ni, Cu, As, Mo, Bi, La, Th, U, and W were identified as potential vectoring elements in different fractions of till due to their anomalous concentrations down-ice of various mineralized outcrops within the study area. For instance, Fe, Co, Cu, and Mo were established as the most useful vectoring elements in the locally derived till (<2 km down-ice) near the Sue Dianne IOCG deposit, and Fe, Co, Ni, Cu, Mo, W, Bi, and U near the Fab IOCG prospect. At the Sue Dianne deposit, the ratios of near-total (4-acid digestion) versus partial (modified aqua regia digestion) concentrations in the silt + clay-sized till fraction (<0.063 mm) for both La and Th reflect the mineralization alteration signature and define a more consistent dispersal train from mineralization compared to element concentrations mapped alone. Additional testing in an area of continuous till cover near an isolated point source is recommended to further develop the elemental ratio method for exploration of MIAC systems. Full article
(This article belongs to the Special Issue Mineral Exploration in the Glaciated Terrains: Advanced Geochemical and Mineralogical Techniques in the Analysis of Glacial Sediments and Processes)
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28 pages, 10647 KiB  
Article
From Rocks to Pixels: A Protocol for Reproducible Mineral Imaging and its Applications in Machine Learning
by Arnaud L. Back, L. Paul Bédard, Julien Maitre and Kévin Bouchard
Minerals 2024, 14(1), 51; https://doi.org/10.3390/min14010051 - 30 Dec 2023
Viewed by 2147
Abstract
Identifying minerals is essential for geology, mineral exploration, engineering, and environmental sciences. Recent advances in machine learning have illustrated its potential as a fast, cost-effective, and reliable tool for identifying minerals from photographs or photomicrographs. However, in the recent literature, few studies have [...] Read more.
Identifying minerals is essential for geology, mineral exploration, engineering, and environmental sciences. Recent advances in machine learning have illustrated its potential as a fast, cost-effective, and reliable tool for identifying minerals from photographs or photomicrographs. However, in the recent literature, few studies have been dedicated to image acquisition. Machine learning generally requires reproducible, high-quality data to perform complicated tasks such as mineral identification to avoid common pitfalls. In this paper, we propose a practical image acquisition protocol for optical microscopes. This protocol focuses on ensuring reproducibility and enhancing image quality. To favor reproducibility, we detail dealing with camera errors, using reference color gauges, and establishing experimental parameters such as the external light source and temperature. For image enhancement, we explain the importance of lighting and its impact on machine learning precision, selection of the objective, and white balance calibration. In addition, we trialed the protocol on heavy mineral concentrate from till samples (20 species) with a typical deep learning model and it revealed that minor lighting modification (<5% difference in one channel) significantly increased misclassification rates: kyanite from 6.4% to 24.9% and monazite from 6.5% to 42.9%. Full article
(This article belongs to the Special Issue Mineral Exploration in the Glaciated Terrains: Advanced Geochemical and Mineralogical Techniques in the Analysis of Glacial Sediments and Processes)
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