地质通报2025，Vol.44Issue(12)：2249-2265,17.DOI:10.12097/gbc.2025.05.054
基于集成学习算法的冻土区水合物地层岩性识别方法
Lithological classification for gas hydrate reservoirs in permafrost areas based on ensemble learning
摘要
Abstract
[Objective]The accurate identification of stratigraphic lithology plays a critical role in delineating gas hydrate-bearing layers.This study applies ensemble learning methods to establish correlations between logging data and gas hydrate stratigraphic lithology,exploring techniques for precise prediction of stratigraphic lithology in gas hydrate-bearing layers within permafrost regions.Conventional well-logging lithology identification methods suffer from limitations such as heavy reliance on expert knowledge,high subjectivity,and poor reproducibility.Moreover,these traditional approaches lack adaptability,often requiring the reconstruction of interpretation models when applied to new regions,which impedes timely application.[Methods]This study focuses on gas hydrate reservoirs in the permafrost regions of the Qinghai-Xizang Plateau,where an ADASYN-XGBoost ensemble learning model is developed for lithology identification.The performance of this model is compared with that of several non-optimized machine learning algorithms,including Extreme Gradient Boosting(XGBoost),Random Forest(RF),K-Nearest Neighbors(KNN),Gradient Boosting Decision Tree(GBDT),and Support Vector Machine(SVM).[Results]The results indicate that the ADASYN-XGBoost model achieves the highest lithology identification accuracy of 97.8％for gas hydrate formations in permafrost regions,significantly surpassing the accuracy rates obtained by the XGBoost,RF,KNN,GBDT,and SVM models.[Conclusions]The ensemble learning-based lithology identification model proposed in this study offers a theoretical foundation and technical support for addressing the challenges associated with lithological classification of gas hydrate reservoirs in permafrost regions.关键词
XGBoost/ADASYN/集成学习/冻土区/水合物/岩性识别/青藏高原Key words
XGBoost/ADASYN/ensemble learning/gas hydrate/permafrost region/lithology identification/Qinghai-Xizang Plateau分类
天文与地球科学引用本文复制引用
肖昆,徐艺宸,邹长春,卢振权,李红星,胡旭东..基于集成学习算法的冻土区水合物地层岩性识别方法[J].地质通报,2025,44(12):2249-2265,17.基金项目
江西省自然科学基金项目《基于机器学习的江西地区铀矿层测井识别方法研究》(编号:20232BAB203072)、江西省主要学科学术和技术带头人培养计划《江西地区页岩气储层岩石地球物理响应特征数值模拟研究》(编号:20204BCJ23027)、国家自然科学基金项目《祁连山冻土区孔隙型天然气水合物储层电阻率模型及饱和度评价方法研究》(批准号:42404150)Supported by Natural Science Foundation of Jiangxi Province(No.20232BAB203072),Academic and Technical Leader Training Program of Jiangxi Province(No.20204BCJ23027)and National Natural Science Foundation of China(No.42404150) （编号:20232BAB203072）
