| The forming environment of volcanic rocks is complex, and the lithology of volcanic rocks in a certain area
may be mainly composed of two or three types, which leads to serious imbalance of core data of different lithology. The
existing lithology identification methods are not effective in dealing with unbalanced samples among classes. To solve
these problems, a volcanic rock lithology identification method based on ADASYN-GS-XGBOOST hybrid model is
proposed. The unbalanced samples are processed by ADASYN oversampling algorithm to obtain a new sample set, and
then XGBOOST is used as the base classifier to classify the samples. The ADASYN-GS-XGBOOST hybrid lithology
identification model is established by using Grid Search to optimize the parameters of the model. The results of the
hybrid model training are compared with those of K nearest neighbor, naive Bayes, random forest, XGBOOST and
SMOTE-GS-XGBOOST algorithms. The results show that the model based on ADASYN-GS-XGBOOST algorithm
has the best identification effect. This method overcomes the problem that existing lithology identification methods can
not effectively solve the problem of unbalanced samples, and greatly improves the accuracy of lithology identification
of volcanic rocks. |
