Factors contributing to metal endowment in the western Wabigoon and southern Abitibi subprovinces: A machine learning approach to Precambrian greenstone belts

Becki Montsion

doi:10.26182/yjfk-ka03

Factors contributing to metal endowment in the western Wabigoon and southern Abitibi subprovinces: A machine learning approach to Precambrian greenstone belts

Becki Montsion

School of Earth and Oceans

Research output: Thesis › Doctoral Thesis

177 Downloads (Pure)

Abstract

Development of techniques that spatially represent and rank geological features controlling MagmaticNi-Cu-PGE, Volcanogenic Massive Sulfide Cu-Zn-Pb-Ag(-Au), and Orogenic Au mineral system prospectivity in Archean greenstone belts near Timmins and Dryden, Ontario. The study summarizes current geological knowledge, applies statistical methods to enhance knowledge using geochemistry, and explores various techniques to map structural complexity, pre-deformation fluid path distances, rheological and chemical contrast, magmatic geochemistry, and hydrothermal alteration. Random forests importance ranking identifies factors controlling mineralization and explain contrasting orogenic Au endowment in the studied greenstone belts. Multi-disciplinary data and methods are needed to improve existing geological understanding and exploration strategy.

Original language	English
Qualification	Doctor of Philosophy
Awarding Institution	The University of Western Australia Laurentian University
Supervisors/Advisors	Lindsay, Mark, Supervisor Jessell, Mark, Supervisor Perrouty, Stéphane, Supervisor, External person Sherlock, Ross, Supervisor, External person
Award date	15 Aug 2023
DOIs	https://doi.org/10.26182/yjfk-ka03
Publication status	Unpublished - 2023

Access to Document

10.26182/yjfk-ka03

THESIS - DOCTOR OF PHILOSOPHY - MONTISON Rebecca Marie - 2023
This work is protected by Copyright. You may print or download ONE copy of this document for the purpose of your own non-commercial research or study. Any other use requires permission from the copyright owner. The Copyright Act requires you to attribute any copyright works you quote or paraphrase.

1 Article
1 Data article

Geological and geophysical data compilation for the western Wabigoon and southern Abitibi subprovinces of the Superior Province, Ontario, Canada
Montsion, R. M., Perrouty, S. & Frieman, B. M., Aug 2021, In: Data in Brief. 37, 107159.
Research output: Contribution to journal › Data article › peer-review

Open Access
2 Citations (Scopus)
Mapping structural complexity using geophysics: A new geostatistical approach applied to greenstone belts of the southern Superior Province, Canada
Montsion, R. M., Perrouty, S., Lindsay, M. D., Jessell, M. W. & Frieman, B. M., 5 Aug 2021, In: Tectonophysics. 812, 228889.
Research output: Contribution to journal › Article › peer-review

Open Access
14 Citations (Scopus)

Cite this

@phdthesis{01fa80951e784dc2a67ae14404a4faf7,

title = "Factors contributing to metal endowment in the western Wabigoon and southern Abitibi subprovinces: A machine learning approach to Precambrian greenstone belts",

abstract = "Development of techniques that spatially represent and rank geological features controlling MagmaticNi-Cu-PGE, Volcanogenic Massive Sulfide Cu-Zn-Pb-Ag(-Au), and Orogenic Au mineral system prospectivity in Archean greenstone belts near Timmins and Dryden, Ontario. The study summarizes current geological knowledge, applies statistical methods to enhance knowledge using geochemistry, and explores various techniques to map structural complexity, pre-deformation fluid path distances, rheological and chemical contrast, magmatic geochemistry, and hydrothermal alteration. Random forests importance ranking identifies factors controlling mineralization and explain contrasting orogenic Au endowment in the studied greenstone belts. Multi-disciplinary data and methods are needed to improve existing geological understanding and exploration strategy.",

keywords = "Mineral exploration, Machine learning, Geochemical classification, Structural complexity, Magmatic Ni-Cu-PGE, Volcanogenic Massive Sulfide Cu-Zn-Pb-Ag(-Au), Orogenic Au",

author = "Becki Montsion",

year = "2023",

doi = "10.26182/yjfk-ka03",

language = "English",

school = "The University of Western Australia, Laurentian University",

}

TY - BOOK

T1 - Factors contributing to metal endowment in the western Wabigoon and southern Abitibi subprovinces: A machine learning approach to Precambrian greenstone belts

AU - Montsion, Becki

PY - 2023

Y1 - 2023

N2 - Development of techniques that spatially represent and rank geological features controlling MagmaticNi-Cu-PGE, Volcanogenic Massive Sulfide Cu-Zn-Pb-Ag(-Au), and Orogenic Au mineral system prospectivity in Archean greenstone belts near Timmins and Dryden, Ontario. The study summarizes current geological knowledge, applies statistical methods to enhance knowledge using geochemistry, and explores various techniques to map structural complexity, pre-deformation fluid path distances, rheological and chemical contrast, magmatic geochemistry, and hydrothermal alteration. Random forests importance ranking identifies factors controlling mineralization and explain contrasting orogenic Au endowment in the studied greenstone belts. Multi-disciplinary data and methods are needed to improve existing geological understanding and exploration strategy.

AB - Development of techniques that spatially represent and rank geological features controlling MagmaticNi-Cu-PGE, Volcanogenic Massive Sulfide Cu-Zn-Pb-Ag(-Au), and Orogenic Au mineral system prospectivity in Archean greenstone belts near Timmins and Dryden, Ontario. The study summarizes current geological knowledge, applies statistical methods to enhance knowledge using geochemistry, and explores various techniques to map structural complexity, pre-deformation fluid path distances, rheological and chemical contrast, magmatic geochemistry, and hydrothermal alteration. Random forests importance ranking identifies factors controlling mineralization and explain contrasting orogenic Au endowment in the studied greenstone belts. Multi-disciplinary data and methods are needed to improve existing geological understanding and exploration strategy.

KW - Mineral exploration

KW - Machine learning

KW - Geochemical classification

KW - Structural complexity

KW - Magmatic Ni-Cu-PGE

KW - Volcanogenic Massive Sulfide Cu-Zn-Pb-Ag(-Au)

KW - Orogenic Au

U2 - 10.26182/yjfk-ka03

DO - 10.26182/yjfk-ka03

M3 - Doctoral Thesis

ER -

Factors contributing to metal endowment in the western Wabigoon and southern Abitibi subprovinces: A machine learning approach to Precambrian greenstone belts

Abstract

Access to Document

Fingerprint

Research output

Geological and geophysical data compilation for the western Wabigoon and southern Abitibi subprovinces of the Superior Province, Ontario, Canada

Mapping structural complexity using geophysics: A new geostatistical approach applied to greenstone belts of the southern Superior Province, Canada

Cite this