Application of machine learning to predict types of Pb-Zn deposits by using trace elemental characteristics of sphalerite
The trace elemental characteristics of sphalerite are crucial indicators for identifying the genetic types of Pb-Zn deposits.In this study,we identify the key control elements to distinguish between different genetic types of Pb-Zn deposits by using machine learning,and establish a generalized map of their identification based on the characteristics of their trace elements of sphalerite.To this end,we collected data on 12 trace elements(Cd,Mn,Ag,Cu,Pb,Sn,Ga,In,Sb,Co,Ge,and Fe;3 700 samples)in sphalerite from four genetic types of Pb-Zn deposits:Mississippi Valley,Volcanogenic Massive Sulfide,Sedimentary Exhalative,and Skarn deposits.We applied two machine learning-based models of classification-the support vector machine and random forest-to classify the elemental data,rank the importance of these characteristic elements,and identify the key controlling elements to distinguish between the genetic types of Pb-Zn deposits.The use of big data in conjunction with the machine learning-based techniques enabled the accurate identification of the different genetic types of Pb-Zn deposits based on the characteristics of the trace elements of sphalerite.The support vector machine and random forest-based models of classification achieved accuracies of 98.5％and 96.9％,respectively,on the test dataset.Moreover,we subjected the 12-dimensional elemental features to principal component analysis.This,in conjunction with statistical analysis,visualized dimension reduction,and the ranking of importance of the elements according to their features based on the random forest model,enabled us to identify six key elements(Mn,Ge,In,Co,Sb,and Ga)that can be used to distinguish among the four genetic types of Pb-Zn deposits within the 12 trace elements of sphalerite.Following this,we constructed a 2D ternary diagram of ln(Mn/Cd)-ln(Ge/Cd)-ln(Co/Cd),and 2D binary diagrams of ln(Mn)-ln(Sb),ln(Co)-ln(Ga),and ln(Mn)-ln(In/Ge)for sphalerite to distinguish among the four genetic types of Pb-Zn deposits.
sphaleritetrace elementsmachine learningbig data analysisPb-Zn deposits
万方数据
维普
