Abstract Specific computational tools assist geologists in identifying and sorting lithologies in well surveys and reducing operational costs and practical working time. This allows for the management of professional output, the efficient interpretation of data, and completion of scientific research on data collected in geologically distinct regions. Machine learning methods and applications integrate large sets of information with the goal of efficient pattern recognition and the capability of leveraging accurate decision making. The objective of this study is to apply machine learning methods to the supervised classification of lithologies using multivariate log parameter data from offshore wells from the International Ocean Discovery Program (IODP). According to the analysis of the lithologies proposed in the IODP Expeditions and for the application of our methods, the lithologies were divided into four groups. The IODP Expeditions were organized into four templates for better results in analyzing the set of expeditions and practical application of the methods. The templates were submitted to training, validation, and testing by multilayer perceptron (MLP), decision tree, random forest, and support vector machine (SVM) methods. The evaluation was randomly divided into training (70%), validation (10%), and testing (20%) using the classification methods as an evaluation of the results. In the results, it was observed that Template1 (IODP Expedition 362) obtained better results with the MLP method, Template2 (IODP Expeditions 354, 355, and 359) and Template3 (IODP Expeditions 354, 355, 359, and 362) obtained better results with the random forest method with greater than 80.00% accuracy. For cross-validation, the random forest method performed well in all scenarios. In the practical template, the G2 group obtained a better result with the MLP method with an average accuracy above 85.00%. It is expected that machine learning methods can help improve the study of geology with accurate and rapid answers related to interpreting collected data in different study regions.

中文翻译：

---

使用地球物理数据评估岩性分类的机器学习方法

摘要 特定的计算工具可帮助地质学家在井调查中识别和分类岩性并降低运营成本和实际工作时间。这允许管理专业输出、有效解释数据以及完成对在地质不同区域收集的数据的科学研究。机器学习方法和应用程序将大量信息与高效模式识别和利用准确决策的能力相结合。本研究的目的是使用来自国际海洋发现计划 (IODP) 的海上井的多元测井参数数据，将机器学习方法应用于岩性的监督分类。根据对 IODP 远征中提出的岩性的分析和我们方法的应用，将岩性分为四组。IODP 远征被组织成四个模板，以便在分析一组远征和方法的实际应用方面取得更好的结果。模板通过多层感知器 (MLP)、决策树、随机森林和支持向量机 (SVM) 方法进行训练、验证和测试。使用分类方法将评估随机分为训练（70%）、验证（10%）和测试（20%）作为结果的评估。在结果中，观察到 Template1（IODP Expedition 362）使用 MLP 方法获得了更好的结果，Template2（IODP Expeditions 354、355 和 359）和 Template3（IODP Expeditions 354、355、359，和 362) 使用随机森林方法获得了更好的结果，准确率大于 80.00%。对于交叉验证，随机森林方法在所有场景中都表现良好。在实际模板中，G2组用MLP方法获得了较好的结果，平均准确率在85.00%以上。预计机器学习方法可以通过与解释不同研究区域收集到的数据相关的准确和快速的答案来帮助改进地质研究。