Machine learning-based prediction of trace element concentrations using data from the Karoo large igneous province and its application in prospectivity mapping

In this study,we present a machine learning-based method to predict trace element concentrations from major and minor element concentration data using a legacy lithogeochemical database of magmatic rocks from the Karoo large igneous province(Gondwana Supercontinent).Wedemonstrate that a variety of trace elements,including most of the lanthanides,chalcophile,lithophile,and siderophile elements,can be predicted with excellent accuracy.This finding reveals that there are reliable,high-dimensional elemental associations that can be used to predict trace elements in a range of plutonic and volcanic rocks.Since the major and minor elements are used as predictors,prediction performance can be used as a direct proxy for geochemical anomalies.As such,our proposed method is suitable for prospective exploration by identifying anomalous trace element concentrations.Compared to multivariate compositional data analysis methods,the new method does not rely on assumptions of stoichiometric combinations of elements in the data to discover geochemical anomalies.Because we do not use multivariate compositional data analysis techniques(e.g.principal component analysis and combined use of major,minor and trace elements data),we also show that log-ratio transforms do not increase the performance of the proposed approach and are unnecessary for algorithms that are not spatially aware in the feature space.Therefore,we demonstrate that high-dimensional elemental associations can be modelled in an automated manner through a data-driven approach and without assumptions of stoichiometry within the data.The approach proposed in this study can be used as a replacement method to the multivariate compositional data analysis technique that is used for prospectivity mapping,or be used as a pre-processor to reduce the detection of false geochemical anomalies,particularly where the data is of variable quality.

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

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.