Enhanced lithological interpretation from well logs plays a key role in geological resource exploration and mapping, as well as in geo-environmental modeling studies. Core and cutting information is useful for making sound interpretations of well logs; however, these are rarely collected at each depth due to high costs. Moreover, well log interpretation using traditional methods is constrained by poor borehole conditions. Traditional statistical methods are mostly linear, often failing to discriminate between lithology and rock facies, particularly when dealing with overlapping well log signals characterized by the structural and compositional variation of rock types. In this study, we develop multiple supervised and unsupervised machine learning algorithms to jointly analyze multivariate well log data from Integrated Ocean Drilling Program (IODP) expeditions 390 and 393 for enhanced lithological interpretations. Among the algorithms, Logistic Regression, Decision Trees, Gradient Boosting, Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP) neural network models, the Decision Tree and Gradient Boosting models outperformed the others, achieving an accuracy of 0.9950 and an F1-score of 0.9951. While unsupervised machine learning (ML) provides the foundation for cluster information that inherently supports the classification algorithm, supervised ML is applied to devise a data-driven lithology clustering mechanism for IODP datasets. The joint ML-based method developed here has the potential to be further explored for analyzing other well log datasets from the world's oceans.

翻译：基于测井数据的增强岩性解释在地质资源勘探与制图以及地质环境建模研究中起着关键作用。岩心和岩屑信息有助于对测井数据进行可靠解释，但由于成本高昂，这些数据很少在每个深度点采集。此外，使用传统方法的测井解释受限于不良的井眼条件。传统统计方法多为线性方法，往往难以区分岩性和岩相，尤其是在处理因岩石类型结构和成分变化而具有重叠特征的测井信号时。本研究开发了多种有监督和无监督机器学习算法，联合分析来自综合大洋钻探计划（IODP）390和393航次的多变量测井数据，以实现增强的岩性解释。在逻辑回归、决策树、梯度提升、支持向量机（SVM）、k近邻（KNN）和多层感知器（MLP）神经网络等算法中，决策树和梯度提升模型表现最佳，准确率达到0.9950，F1分数为0.9951。无监督机器学习为聚类信息提供了基础，这些信息本质上支持分类算法；而有监督机器学习则用于为IODP数据集设计数据驱动的岩性聚类机制。本文开发的联合机器学习方法有望进一步应用于分析全球其他海洋的测井数据集。