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Towards Optimization of Boosting Models for Formation Lithology Identification

机译：用于优化形成岩性识别的升压模型

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摘要

Lithology identification is an indispensable part in geological research and petroleum engineering study. In recent years, several mathematical approaches have been used to improve the accuracy of lithology classification. Based on our earlier work that assessed machine learning models on formation lithology classification, we optimize the boosting approaches to improve the classification ability of our boosting models with the data collected from the Daniudi gas field and Hangjinqi gas field. Three boosting models, namely, AdaBoost, Gradient Tree Boosting, and eXtreme Gradient Boosting, are evaluated with 5-fold cross validation. Regularization is applied to the Gradient Tree Boosting and eXtreme Gradient Boosting to avoid overfitting. After adapting the hyperparameter tuning approach on each boosting model to optimize the parameter set, we use stacking to combine the three optimized models to improve the classification accuracy. Results suggest that the optimized stacked boosting model has better performance concerning the evaluation matrix such as precision, recall, and f1 score compared with the single optimized boosting model. Confusion matrix also shows that the stacked model has better performance in distinguishing sandstone classes.
机译：岩性识别是地质研究和石油工程研究中不可或缺的一部分。近年来，已经采用了几种数学方法来提高岩性分类的准确性。根据我们早期的工作，评估机器学习模型的形成岩性分类，我们优化了提高促进模型的提升方法，并通过丹义天然气场和恒津奇气田收集的数据进行升级模型。三个升压模型，即Adaboost，梯度树提升和极端梯度提升，用5倍交叉验证进行评估。正规化应用于渐变树升压和极端渐变提升以避免过度装备。在调整每个升压模型上的超代表调谐方法以优化参数集后，我们使用堆叠来组合三种优化模型来提高分类精度。结果表明，与单优化升压模型相比，优化的堆叠升压模型具有更好的评估矩阵，如精度，召回和F1分数。混乱矩阵还表明，堆叠模型在区分砂岩类方面具有更好的性能。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第17期|5309852.1-5309852.13|共13页
  • 作者

    Xie Yunxin; Zhu Chenyang; Lu Yue; Zhu Zhengwei;

  • 作者单位

    Changzhou Univ Sch Petr Engn Changzhou 213100 Peoples R China;

    Univ Southampton Elect & Comp Sci Univ Rd Southampton SO17 1BJ Hants England;

    Univ Southampton Elect & Comp Sci Univ Rd Southampton SO17 1BJ Hants England;

    Changzhou Univ Sch Informat Sci & Engn Changzhou 213100 Peoples R China;

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