基于非均衡数据的ADASYN-CatBoost测井岩性智能识别——以胶西北招贤金矿床为例  

ADASYN-CatBoost Method for Intelligent Identification of Logging LithologyConsidering Unbalanced Data:A Case Study of Zhaoxian Gold Deposit inNorthwestern Jiaodong Peninsula

作　　者：许方颖 邹艳红[1,2] 易卓炜 杨福强 毛先成[1,2] XU Fangying;ZOU Yanhong;YI Zhuowei;YANG Fuqiang;MAO Xiancheng(Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring,Ministry of Education,Central South University,Changsha 410083,Hunan,China;School of Geosciences and Info-Physics,Central South University,Changsha 410083,Hunan,China)

机构地区：[1]中南大学有色金属成矿预测与地质环境监测教育部重点实验室,湖南长沙410083 [2]中南大学地球科学与信息物理学院,湖南长沙410083

出　　处：《黄金科学技术》2023年第5期721-735,共15页Gold Science and Technology

基　　金：国家自然科学基金项目“断裂控制热液蚀变及其成矿过程动力学计算模拟——以胶东焦家式金矿为例”(编号:41872249)、“矿床时空结构定量表征与智能理解”(编号:42030809);湖南省科技创新计划项目“关键金属资源勘查创新团队”(编号:2021RC4055)联合资助。

摘　　要：快速准确地识别覆盖区下伏地层与岩体,对于金属矿山地质找矿工作具有重要意义。针对矿床地层与岩体中复杂岩性分布的多样性和非均衡性,考虑测井响应特征与岩性之间的强非线性关系,提出了一种基于ADASYN非均衡数据处理和CatBoost机器学习的测井岩性智能识别方法。首先,利用ADASYN算法处理非均衡测井样本数据,根据小类样本加权分布生成合成样本;然后,采用CatBoost算法结合网格搜索以及十折交叉验证建立最优岩性识别模型;最后,通过模型输出的特征重要性及部分依赖图对岩性分类结果进行解译。以胶西北招贤金矿床实例测井数据为基础,针对10类岩性进行识别和解译分析,模型评价结果表明:测试集上的精确率、召回率和F1分数分别达到98.21%、98.20%和98.20%。将CatBoost岩性分类与GBDT、LightGBM算法进行对比,结果表明CatBoost分类效果最优,且均优于样本数据未均衡化处理的岩性识别效果。通过与实例录井剖面岩芯岩性进行对比,验证了模型分类结果的有效性。Logging lithology identification is helpful to quickly and accurately identify the underlying strata and rock mass in the overburden area,which is of great significance to the geological prospecting exploration of metal mines.Based on the actual logging data of the Zhaoxian gold deposit in the northwest of Jiaodong Peninsula,this paper combined machine learning methods to research on intelligent identification of lithology.In view of the diversity and non-equilibrium of lithology distribution of complex rock formations in the deposit,considering the strong non-linear relationship between logging response and lithology,this paper proposed an intelligent identification method for logging lithology based on ADASYN imbalanced data processing and CatBoost machine learning.Firstly,the ADASYN algorithm was used to process the unbalanced logging sample data and generate synthetic samples according to the weighted distribution of small class samples.Then,the CatBoost algorithm was used to construct a machine learning model between logging characteristic and lithology.The validation curve was used to determine the hyperparametric grid search range of the model.Parameters were optimized by combining grid search with grid search and 10-fold cross validation to establish the optimal lithology classification model.Finally,the performance of the model was evaluated by indices such as accuracy,recall and F1 score on the test set,while the results of the lithology classification were interpreted by the model output of the feature importance and the partial dependence map.An example was given on the logging data from the Zhaoxian gold deposit in northwest Jiaodong peninsula,the lithology identification and interpretation analysis were conducted on 10 types of lithologies based on sample data equalisation.The model evaluation results show that the accuracy,recall and F1 score on the test set reached 98.21%,98.20%and 98.20%,respectively.CatBoost lithology classification was compared with GBDT and LightGBM algorithms,and the results

关 键 词：岩性识别 ADASYN-CatBoost 测井 非均衡数据 机器学习 招贤金矿床 

分 类 号：P631.81[天文地球—地质矿产勘探]