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Development and application of feature engineered geological layers for ranking magmatic,volcanogenic,and orogenic system components in Archean greenstone belts

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Geoscience Frontiers 2024年第2期15卷 251-273页

作者： R.M.Montsion s.perrouty M.D.Lindsay M.W.Jessell R.sherlock Mineral Exploration Research Centre Harquail School of Earth SciencesGoodman School of MinesLaurentian UniversitySudburyOntario P3E 2C6Canada Centre for Exploration Targeting School of Earth SciencesThe University of Western Australia35 Stirling HighwayCrawley 6009Australia Commonwealth scientific and Industrial Research Organization Mineral Resources26 Dick Perry AveKensingtonWA 6151Australia ARC Centre for Data Analytics for Resources and Environments(DARE) Perth and SydneyAustralia

Geologically representative feature engineering is a crucial component in geoscientific applications of machine *** commonly applied feature engineering techniques used to produce input variables for machine learning apply geological knowledge to generic data science techniques,which can lead to ambiguity,geological oversimplification,and/or compounding subjective *** that utilize minimally processed input variables attempt to overcome these issues,but often lead to convoluted and uninterpretable *** address these challenges,new and enhanced feature engineering methods were developed by combining geological knowledge,understanding of data limitations,and a variety of data science *** include non-Euclidean fluid pre-deformation path distance,rheological and chemical contrast,geologically constrained interpolation of characteristic host rock geochemistry,interpolation of mobile element gain/loss,assemblages,magnetic intensity,structural complexity,host rock physical *** methods were applied to compiled open-source and new field observations from Archean greenstone terranes in the Abitibi and western Wabigoon sub-provinces of the superior Province near Timmins and Dryden,Ontario,*** feature maps represent conceptually significant components in magmatic,volcanogenic,and orogenic mineral systems.A comparison of ranked feature importance from random forests to conceptual mineral system models show that the feature maps adequately represent system components,with a few exceptions attributed to biased training data or limited constraint *** study also highlights the shared importance of several highly ranked features for the three mineral systems,indicating that spatially related mineral systems exploit the same features when *** feature importance when classifying orogenic Au mineralization in Timmins and Dryden provides insights into the possible cause of contrasting endowment be

关键词： Machine learning Random forests Mineral systems Magmatic Ni-Cu-PGE Volcanogenic Massive sulfide(VMs)Cu-Zn-Pb-Ag(-Au) Orogenic Au Abitibi Wabigoon

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Distribution and U-Pb Ages of Newly Recognized Regional-scale Dyke swarms of the Leo Man Craton

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Acta Geologica sinica(English Edition) 2016年第S1期90卷 29-页

作者： L.BARATOUX M.W.JEssELL U.sODERLUND R.E.ERNsT M.BENOIT s.NABA C.COURNEDE s.perrouty V.METELKA D.YATTE D.P.DIALLO P.M.NDIAYE D.BARATOUX GET IRDCNRSOMP Institut Fondamental d'Afrique Noire Cheikh Anta Diop Dakar CET The University of Western Australia Department of Geology Lund University Department of Earth sciences Carleton University Universitéde Ouagadougou Aix Marseille Univ CNRSCEREGE Western University Earth Sciences Department Universite Cheikh Anta Diop

Over 20 sets of dolerite dykes crosscutting Paleoproterozoic basement in West Africa were distinguished via the interpretation of regional and high resolution magnetic airborne data of West African Craton

关键词： Pb West

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