SEM-based automated mineralogy (SEM-AM) and unsupervised machine learning studying the textural setting and elemental association of gold in the Rajapalot Au-Co area, northern Finland
SEM-based automated mineralogy (SEM-AM) techniques allow fast and effective way of studying the textural settings of gold in hydrothermal deposits. Unsupervised machine learning (e.g. self-organizing maps) is an intuitive way of processing multi-dimensional geochemical datasets in order to reveal...
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| Format: | Article |
| Language: | English |
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Geological Society of Finland
2021-12-01
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| Series: | Bulletin of the Geological Society of Finland |
| Subjects: | |
| Online Access: | https://www.geologinenseura.fi/sites/geologinenseura.fi/files/bulletin_vol93_2_129-154_ranta-etal.pdf |
| _version_ | 1819173481672081408 |
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| author | Jukka-Pekka Ranta Nick Cook Sabine Gilbricht |
| author_facet | Jukka-Pekka Ranta Nick Cook Sabine Gilbricht |
| author_sort | Jukka-Pekka Ranta |
| collection | DOAJ |
| description | SEM-based automated mineralogy (SEM-AM) techniques allow fast and effective way of
studying the textural settings of gold in hydrothermal deposits. Unsupervised machine
learning (e.g. self-organizing maps) is an intuitive way of processing multi-dimensional
geochemical datasets in order to reveal hidden patterns potentially represent different
mineralization stages. We combined these two methods for studying the relationship of
gold and cobalt within different prospects in a Paleoproterozoic gold-cobalt mineralized area known as Rajapalot. Gold is found as a texturally late phase, occurring in fractures of silicates and sulfides. Based on the elemental associations observed from the whole-rock geochemical dataset using self-organizing-maps, Co-only, Au-Co and Au associations can be inferred relating to either different mineralization stages or different fluid-host rock interactions. Also, the dominant mineralization-related alteration in different occurrences within the Rajapalot Au-Co prospects are reflected as elemental associations with gold in the geochemical data. Our study shows the effectiveness SEM-AM methods for studying distribution of valuable minerals. Unsupervised neural networks provide for easy and intuitive processing technique that can be validated with the mineralogical observations. |
| first_indexed | 2024-12-22T20:23:46Z |
| format | Article |
| id | doaj.art-df46d3f95981429e9fe14f339570d017 |
| institution | Directory Open Access Journal |
| issn | 0367-5211 1799-4632 |
| language | English |
| last_indexed | 2024-12-22T20:23:46Z |
| publishDate | 2021-12-01 |
| publisher | Geological Society of Finland |
| record_format | Article |
| series | Bulletin of the Geological Society of Finland |
| spelling | doaj.art-df46d3f95981429e9fe14f339570d0172022-12-21T18:13:47ZengGeological Society of FinlandBulletin of the Geological Society of Finland0367-52111799-46322021-12-0193212915410.17741/bgsf/93.2.003SEM-based automated mineralogy (SEM-AM) and unsupervised machine learning studying the textural setting and elemental association of gold in the Rajapalot Au-Co area, northern FinlandJukka-Pekka Ranta0Nick Cook1Sabine Gilbricht2Oulu Mining School, University of Oulu, FinlandMawson Gold Ltd., BC, CanadaTU-Bergakademia Freiberg, Freiberg, GermanySEM-based automated mineralogy (SEM-AM) techniques allow fast and effective way of studying the textural settings of gold in hydrothermal deposits. Unsupervised machine learning (e.g. self-organizing maps) is an intuitive way of processing multi-dimensional geochemical datasets in order to reveal hidden patterns potentially represent different mineralization stages. We combined these two methods for studying the relationship of gold and cobalt within different prospects in a Paleoproterozoic gold-cobalt mineralized area known as Rajapalot. Gold is found as a texturally late phase, occurring in fractures of silicates and sulfides. Based on the elemental associations observed from the whole-rock geochemical dataset using self-organizing-maps, Co-only, Au-Co and Au associations can be inferred relating to either different mineralization stages or different fluid-host rock interactions. Also, the dominant mineralization-related alteration in different occurrences within the Rajapalot Au-Co prospects are reflected as elemental associations with gold in the geochemical data. Our study shows the effectiveness SEM-AM methods for studying distribution of valuable minerals. Unsupervised neural networks provide for easy and intuitive processing technique that can be validated with the mineralogical observations.https://www.geologinenseura.fi/sites/geologinenseura.fi/files/bulletin_vol93_2_129-154_ranta-etal.pdfgoldcobaltsem-ammachine learningperäpohja beltfinlandpaleoproterozoic |
| spellingShingle | Jukka-Pekka Ranta Nick Cook Sabine Gilbricht SEM-based automated mineralogy (SEM-AM) and unsupervised machine learning studying the textural setting and elemental association of gold in the Rajapalot Au-Co area, northern Finland Bulletin of the Geological Society of Finland gold cobalt sem-am machine learning peräpohja belt finland paleoproterozoic |
| title | SEM-based automated mineralogy (SEM-AM) and unsupervised machine learning studying the textural setting and elemental association of gold in the Rajapalot Au-Co area, northern Finland |
| title_full | SEM-based automated mineralogy (SEM-AM) and unsupervised machine learning studying the textural setting and elemental association of gold in the Rajapalot Au-Co area, northern Finland |
| title_fullStr | SEM-based automated mineralogy (SEM-AM) and unsupervised machine learning studying the textural setting and elemental association of gold in the Rajapalot Au-Co area, northern Finland |
| title_full_unstemmed | SEM-based automated mineralogy (SEM-AM) and unsupervised machine learning studying the textural setting and elemental association of gold in the Rajapalot Au-Co area, northern Finland |
| title_short | SEM-based automated mineralogy (SEM-AM) and unsupervised machine learning studying the textural setting and elemental association of gold in the Rajapalot Au-Co area, northern Finland |
| title_sort | sem based automated mineralogy sem am and unsupervised machine learning studying the textural setting and elemental association of gold in the rajapalot au co area northern finland |
| topic | gold cobalt sem-am machine learning peräpohja belt finland paleoproterozoic |
| url | https://www.geologinenseura.fi/sites/geologinenseura.fi/files/bulletin_vol93_2_129-154_ranta-etal.pdf |
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