【摘要】 为了安全高效地开采矿产资源，提出一种AVOA-XGBoost模型来预测岩爆烈度．依据初步选取的6个评价指标收集了326个岩爆案例，并采用Boruta算法和合成少数类过采样技术(SMOTE)进行特征筛选和解决类不平衡问题．经过预处理后的数据集通过分层抽样被划分为训练集(80%)和测试集(20%)，分别用于训练和测试模型，结果表明：非洲秃鹰优化算法(AVOA)可以高效地确定XGBoost算法的超参数；与现有的智能模型相比，该模型的准确率优异，Kappa系数为0.92，且较单一的XGBoost模型表现出更优的收敛速度；对特征的重要性分析发现岩石的弹性能量指数对模型的贡献最大．最后，将模型应用于三山岛金矿工程案例，验证了AVOA-XGBoost模型在岩爆预测中的有效性和实用性．更多还原

【Abstract】 To extract mineral resources safely and efficiently,it is necessary to study rockburst prediction.Therefore,an AVOAXGBoost model was proposed for rockburst intensity prediction. Based on the initial selection of six evaluation indicators,326rockburst cases were collected,and the Boruta algorithm and synthetic minority over-sampling technique(SMOTE) were used to filter features and solve the class imbalance problem. The pre-processed dataset was divided into the training set(80%) and the testing set(20%) by stratified sampling for training and testing the model respectively.Based on the results,it can be easily shown that African vultures optimization algorithm(AVOA) can efficiently determine the hyperparameters of the extreme gradient boosting(XGBoost) algorithm. Compared to existing intelligent models,the model shows excellent accuracy,and it has the Kappa coefficient of 0.92,and it shows better convergence speed than the single XGBoost. The important analysis of the features shows that the elastic energy index of rocks contributed the most to the model.Finally,the model was applied to the Sanshandao gold mine project case to verify the effectiveness and applicability of AVOA-XGBoost in rockburst prediction.更多还原