| 基于支持向量机的测井岩性识别在松散沉积物调查中的应用研究 |
| 投稿时间：2020-10-30 修订日期：2021-01-29 点此下载全文 |
| 引用本文：岳永东,渠洪杰,谭春亮,等.基于支持向量机的测井岩性识别在松散沉积物调查中的应用研究[J].钻探工程,2021,48(4):29-36.YUE Yongdong,QU Hongjie,TAN Chunliang,et al. Application of logging lithology identification based on support vector machines in unconsolidated sediment investigation[J]. Drilling Engineering, 2021,48(4):29-36. |
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| 基金项目:中国地质调查局地质调查项目“锡林郭勒盟-通辽地区基础地质调查”（编号：DD20190021）、“地质矿产勘查钻探技术升级与应用示范”（编号：20211345） |
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| 中文摘要:砂砾质松散沉积物粒径组成复杂，钻探取心率低，导致对沉积相和相界面的判别准确度不高。本文结合岩心和测井资料，建立了松散沉积层基于支持向量机的测井数据岩性识别模型，分析了训练集样本数量对模型识别准确率的影响，并与BP神经网络模型进行了对比。结果表明，支持向量机模型的岩性识别准确率高，且对训练样本需求量低，可以有效地弥补钻孔取心率不足的问题，并降低钻探施工成本。在松散沉积物调查中，利用基于支持向量机的测井岩性识别模型自动识别沉积序列具有可行性，是实现绿色勘查的有益尝试。 |
| 中文关键词:支持向量机 松散沉积物 岩性识别 钻探取心 测井 绿色勘查 |
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| Application of logging lithology identification based on support vector machines in unconsolidated sediment investigation |
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| Abstract:The accuracy of sedimentary facies and facies interface identification is not high due to the complex grain size composition and low core recovery in sandy sediment. In this paper， a lithology identification model based on the support vector machine （SVM） is established based on core and logging data， and the influence of the training set size on the identification accuracy of the model is analyzed and compared with the BP neural network model. The results show that the SVM model has high accuracy of lithology identification and low demand for training samples， which can effectively make up for the insufficient core recovery and reduce the drilling cost. It is feasible to use the logging lithology identification model based on SVM to identify the sedimentary sequence automatically in unconsolidated sediment investigation， which is a beneficial attempt to implement green exploration. |
| keywords:SVM unconsolidated sediment lithology identification coring logging green exploration |
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