[1]张多,韩逢庆.基于支持向量机和有序聚类的岩层识别[J].智能系统学报,2014,9(1):98-103.[doi:10.3969/j.issn.1673-4785.201304019]
ZHANG Duo,HAN Fengqing.Stratum identification based on the SVM and ordered cluster[J].CAAI Transactions on Intelligent Systems,2014,9(1):98-103.[doi:10.3969/j.issn.1673-4785.201304019]
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ZHANG Duo,HAN Fengqing.Stratum identification based on the SVM and ordered cluster[J].CAAI Transactions on Intelligent Systems,2014,9(1):98-103.[doi:10.3969/j.issn.1673-4785.201304019]
基于支持向量机和有序聚类的岩层识别
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
9
期数:
2014年第1期
页码:
98-103
栏目:
学术论文—机器学习
出版日期:
2014-02-25
- Title:
- Stratum identification based on the SVM and ordered cluster
- Keywords:
- stratum identification; support vector machine; ordered clustering; training samples; classifier
- 分类号:
- TP631
- 摘要:
- 由于支持向量机进行分类前需要先使用训练样本训练分类器, 而在岩层识别问题中没有训练样本, 针对此问题, 提出一种基于有序聚类的支持向量机岩层识别分类算法。首先利用有序聚类算法对经滤波和归一化后的测井数据进行初步分层, 然后根据初步分层结果获取训练样本, 最后用训练后的支持向量机分类器对测井数据进行第2次分层。应用该算法对选取的3口井的岩性进行自动识别, 并将该算法的识别结果与其他算法进行比较。仿真实验结果表明, 该算法具有较高的准确率, 每种岩层的平均准确率能达到85%, 解决了岩层识别前必须采用已知类别的数据对支持向量机进行训练的弊端。
- Abstract:
- The support vector machine (SVM) needs training samples to train itself before identifying stratum, while there are no training samples with stratum identification. Focusing on this problem, this paper puts forward a vector machine classifier based on the ordered clustering algorithm. Firstly, the ordered clustering algorithm is used to get preliminary layered logging data which have been filtered and normalized. Secondly, the training samples are obtained according to preliminary layered outcomes. Finally, the data are layered again by the trained SVM classifier. The algorithm is used to automatically identify the lithology of the selected three wells, and compared with the results of the other algorithms. The results of the simulation experiment show that the algorithm overcomes the drawbacks that the labeled data has to adopt when training SVM, and improves the accuracy of each stratum, reaching 85% on average.
- 参考文献/References:
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备注/Memo
收稿日期:2013-04-11。
基金项目:国家自然科学基金资助项目(51208538).
作者简介:韩逢庆,男,1968年生,重庆市工业与应用数学学会理事,重庆市运筹学学会理事。主要研究方向为机器学习、人工智能、小波理论及应用等,发表学术论文30余篇,其中被SCI、EI、ISTP检索20余篇。
通讯作者:张多,女,1988年生,硕士研究生,主要研究方向为机器学习、人工智能.E-mail:cqzhangd2012@163.com.
更新日期/Last Update:
1900-01-01