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

作　　者：Steven E.Zhang Glen T.Nwaila Julie E.Bourdeau Lewis D.Ashwal 

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

出　　处：《Artificial Intelligence in Geosciences》2021年第1期60-75,共16页地学人工智能（英文）

摘　　要：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.

关 键 词：Machine learning Predictive modelling Compositional data Prospectivity mapping Anomaly detection Karoo igneous province 

分 类 号：F42[经济管理—产业经济]