Towards a fully data-driven prospectivity mapping methodology:A case study of the Southeastern Churchill Province,Québec and Labrador  

作　　者：Steven E.Zhang Julie E.Bourdeau Glen T.Nwaila David Corrigan 

机构地区：[1]SmartMin Limited,39 Kiewiet Street,Helikon Park,1759,South Africa [2]Geological Survey of Canada,601 Booth Street,Ottawa,Ontario,K1A 0E8,Canada [3]School of Geosciences,University of the Witwatersrand,1 Jan Smuts Ave,Johannesburg,2000,South Africa [4]Lynn Street,Gatineau,Quebec,J9H 1B5,Canada

出　　处：《Artificial Intelligence in Geosciences》2021年第1期128-147,共20页地学人工智能（英文）

摘　　要：Mineral exploration campaigns are financially risky.Several state-of-the-art methods have been developed to mitigate the risk,including predictive modelling of mineral prospectivity using principal component analysis(PCA)and geographic information systems(GIS).The PCA and GIS approach is currently considered acceptable for generating mineral exploration targets.However,some of its limitations are the dependence on sample stoichiometry(e.g.,the existence of minerals),the necessity of log-ratio transformations when dealing with compositional data,and manual interpretation and use of principal components to enhance potential geochemical anomalies for prospectivity mapping.In this study,we generalize the fundamental ideas behind the PCA and GIS approach by developing a new data-driven approach using ML.We showcase a new workflow capable of generating either intermediate evidence layers or final prospectivity maps that depict major regional geochemical anomalies using multi-element geochemical data from Southeastern Churchill Province(Quebec and Labrador),Canada.The region is known for its REEs endowment and the data were gathered for prospectivity mapping.A comparison with the established multivariate hybrid data-and knowledge-based approach revealed that on a roughly comparable basis of the amount of manual effort,our new data-driven procedure can much more accurately identify geochemical anomalies in both univariate and multivariate applications.The results of our prospectivity mapping corroborate with the ground truth or known geological anomalies in the studied region.These findings have potentially wider implications on exploration target generation,where project risks(financial,environmental,political,etc.)and geochemical anomalies must be quantified using robust and effective datadriven approaches.In addition,our methodology is more replicable and objective,as manual geoscientific interpretation is not required during the detection of geochemical anomalies.

关 键 词：ML Mineral prospectivity mapping Principal component analysis Geochemical anomaly REES 

分 类 号：F42[经济管理—产业经济]