基于金矿规格单元数据的机器学习方法在成矿建模分析中的应用

It is significant to integrate geochemical and geological survey to search deep gold orebody in modern gold mineralization prospecting. However, geological survey is laborious and expensive. Therefore, it is beneficial to study the results of the previous geological survey and find the regular pattern in the massive and complex gold deposit data. In this research, Logistic regression, Random forest and Decision tree are applied to train the model using raw data and resampling data. Recall, precision and accuracy are also used to evaluate model performance. The confusion matrix is selected to visualize the performance of different models. After comparison, it is found that the gold mineralization data cannot be identified because of the huge imbalance between the two groups of data. The test data are going to be predicted into the group with huge data. Random Forest has a good performance on the resampling data, recall and accuracy are 90.63% and 70.78%, respectively. The influence of the different classification boundaries is also discussed. According to the different requirements, the values of the different classification boundaries can be chosen. Evaluating with different measures may improve the adequacy of the model for gold mineralization prediction and improve the efficiency of the gold survey.