地质与勘探2026，Vol.62Issue(3)：613-623,11.DOI:10.12134/j.dzykt.2026.03.014
基于机器学习的黔北铝土矿石小体重回归模型研究
Regression Model for Small Volumetric Weight of Bauxite Ore in Northern Guizhou Based on Machine Learning
摘要
Abstract
Small volumetric weight is an important parameter for estimating bauxite resource reserves and is of great significance for bauxite exploration.In northern Guizhou,the traditional measurement of small volumetric weight of bauxite ores involves sampling,processing and testing,which is often time-consuming and cumbersome due to the phased implementation of projects.Based on previously accumulated analytical test data of small volumetric weight of ore,Al2O3,SiO2,and Fe2O3,this paper proposes an interpretable regression model for small volumetric weight of bauxite ore based on the Random Forest(RF)algorithm.The Synthetic Minority Over-sampling Technique(SMOTE)can effectively address the issue of too few data in certain categories,enabling the model to better fit these data.The Shapley Additive Explanations(SHAP)method provides a unified measure for interpreting the RF regression model and enhances model interpretability.SHAP analysis indicates that SiO2 contributes the most to the small volumetric weight of bauxite ores.The Multiple Linear Regression(MLR)model achieves a Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)of 0.1296 and 0.0974 on the test set,respectively,while the RF regression model achieves an RMSE and MAE of 0.0917 and 0.0672,respectively.The comparative results demonstrate that the RF regression model is more stable and performs better.Furthermore,the predicted average value(2.87 g/cm3)of the RF regression model on the validation set(the Xinmu-Yanxi deposit)differs from the adopted value of the deposit(2.82 g/cm3)by 0.05 g/cm3,with an error rate of only 1.8%,indicating high prediction accuracy.This suggests that the RF regression model can be used for estimating small volumetric weight of bauxite ores in bauxite exploration in northern Guizhou.关键词
铝土矿/小体重/多元线性回归/随机森林/合成少数类过采样技术(SMOTE)/沙普利可加性解释方法(SHAP)/黔北Key words
bauxite/small volumetric weight/multiple linear regression(MLR)/random forest(RF)/Synthetic Minority Over-sampling Technique(SMOTE)/Shapley Additive Explanations(SHAP)/northern Guizhou Province分类
天文与地球科学引用本文复制引用
梁小糠,孙国涛,石再平,杨仕江,蔡小勤,李金玉,吴显飞..基于机器学习的黔北铝土矿石小体重回归模型研究[J].地质与勘探,2026,62(3):613-623,11.基金项目
贵州省基础研究计划一般项目(编号:黔科合基础-ZK[2023]一般064)资助. （编号:黔科合基础-ZK[2023]一般064）
