Rock type discrimination by using trace elements of apatite based on the machine learning
The machine learning techniques have been applied rapidly to deal with issues/problems in the field of Earth Sciences in recent years.This article aims to employ machine learning methods to investigate whether trace elements of magmatic apatites can be used to accurately discriminate different types of magmatic rocks.Apatite is a significant mineral with extensive applications in geological exploration and development of mineral resources.However,contents of trace elements of apatites in different types of rocks are relative significantly different.In this paper,we have collected data of trace elements of 3720 magmatic apatite samples from 68 literatures and have introduced two machine learning-based rapid rock type discrimination methods including the Random Forest(RF)and Artificial Neural Network(ANN).The results show that the rock type classification accuracy of the Random Forest model is 93.7％,with contents of Lu,MnO,and Ce,and La/Yb values being four indicators that had the greatest impact on the model;The rock type classification accuracy of the Artificial Neural Network model is 89.7％,which is slightly lower than that of the Random Forest model,with contents of Lu,Ce,and MnO,and Gd/Yb values being four indicators that had the greatest impact on the model.This study has discriminated types of magmatic rocks by using machine learning techniques for dealing with data of trace elements of apatites.Its achievements have not only offered an efficient approach for mineral exploration using data of apatites,but also demonstrated the practical application of machine learning techniques in geology.This machine learning-based rock type discrimination method is expected to provide new ideas and methods for future researches in fields of rock type identification and mineral resource exploration.
random forest algorithmartificial neural network algorithmapatitetrace elementsrock types
万方数据
维普
