| 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 |
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| 引用本文： | 张野,李明超,韩帅,任秋兵,朱月琴.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[J].大地构造与成矿学,2020(2):183-191. |
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| 作者姓名： | 张野 李明超 韩帅 任秋兵 朱月琴 |
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| 作者单位： | 天津大学水利工程仿真与安全国家重点实验室;中国地质调查局发展研究中心;自然资源部地质信息技术重点实验室 |
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| 基金项目： | 天津市杰出青年科学基金项目(17JCJQJC44000);国家优秀青年科学基金项目(51622904);国家重点研发计划(2016YFC0600510)联合资助. |
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| 摘 要： | 现代金矿勘察主要是通过综合地球化学和地质测量等数字化方法对深部矿床进行研究,所需要的人力物力成本较高。而通过分析积累的金矿规格单元数据,可以建立金矿成矿情况与相关成矿元素含量之间的非线性关系,从已有的勘查数据中寻找金矿成矿的一般规律。本文基于与金矿相关的成矿元素含量数据,分别采用逻辑斯蒂回归、随机森林和决策树方法对原始数据和重采样数据进行训练,综合运用召回率、精确率和准确率对模型进行评价。通过对比发现,在训练和测试原始数据过程中,由于每组之间数据量的巨大差距,导致成矿数据被淹没;而在训练重采样数据过程中,随机森林在召回率和准确率方面均有较好的表现,分别达到了90.63%和70.78%;并最终分析了随机森林模型中不同分类边界对于金矿成矿情况预测结果的影响。利用不同的测量指标对模型进行评价分析,使模型更适用于金矿成矿预测,可有效地提高金矿勘察的效率。
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| 关 键 词： | 金矿床成矿预测 重采样 随机森林 召回率 精确率 分类边界 |
Machine Learning Methods Application in Gold Mineralization Prediction Based on Gold Unit Data |
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| ZHANG Ye,LI Mingchao,HAN Shuai,REN Qiubing,ZHU Yueqin.Machine Learning Methods Application in Gold Mineralization Prediction Based on Gold Unit Data[J].Geotectonica et Metallogenia,2020(2):183-191. |
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| Authors: | ZHANG Ye LI Mingchao HAN Shuai REN Qiubing ZHU Yueqin |
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| Institution: | (State Key Laboratory of Hydraulic Engineering Simulation and Safety,Tianjin University,Tianjin 300354,China;Development Research Center of China Geological Survey,Beijing 100037,China;Key Laboratory of Geological Information Technology,Ministry of Natural Resources,Beijing 100037,China) |
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| Abstract: | 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. |
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| Keywords: | gold mineralization prediction resample random forest recall precision classification boundary |
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