Discriminating Deposit Types Using Chlorite Trace Elements Based on Machine Learning
In order to study whether chlorite trace elements can effectively classify different genetic deposit types,in this paper,2 928 trace element data of chlorite from 13 different deposits were collected,which belong to three distinct genetic types,including porphyry,skarn and epithermal deposits.Three different machine learning algorithms,including random forest,support vector machine(SVM)and artificial neural network,were used to establish classification models for the genetic types of deposits and analyze the importance of characteristics.The results show that the SVM model based on Ni,Cr,Co,Sr,V and Zn,6 trace elements have the best classification effect,the highest Kappa coefficient is 0.89,the weighted average score of precision,recall and F1 value reach 0.96,and Ni,V and Co are the three most critical discriminant elements.In this paper it fully confirms that the machine learning classification model based on chlorite mineral trace elements can discriminate deposit types and provide an important indicator for the rapid evaluation of regional scale prospecting.
chloritetrace elementgenetic type of depositmachine learningmineral deposits
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万方数据
