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:2019,28(6):38-44
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基于EM和GMM的朴素贝叶斯岩性识别
(1.中国地质大学(北京) 数理学院, 北京 100083;2.中国石油长庆油田公司第四采气厂, 西安 710016;3.中国地质大学(北京) 地球物理与信息技术学院, 北京 100083)
Naive Bayesian Lithology Recognition Based on EM and GMM
(1.School of Science, China University of Geosciences, Beijing 100083, China;2.Fourth Gas Production Plant, Petro China Changqing, Xi'an 710016, China;3.School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China)
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投稿时间:2018-11-10    修订日期:2018-12-04
中文摘要: 朴素贝叶斯分类器可以应用于岩性识别.该算法常使用高斯分布来拟合连续属性的概率分布,但是对于复杂的测井数据,高斯分布的拟合效果欠佳.针对该问题,提出基于EM算法的混合高斯概率密度估计.实验选取苏东41-33区块下古气井的测井数据作为训练样本,并选取44-45号井数据作为测试样本.实验采用基于EM算法的混合高斯模型来对测井数据变量进行概率密度估计,并将其应用到朴素贝叶斯分类器中进行岩性识别,最后用高斯分布函数的拟合效果作为对比.结果表明混合高斯模型具有更好的拟合效果,对于朴素贝叶斯分类器进行岩性识别的性能有不错的提升.
Abstract:Naive Bayesian classifier can be applied to lithologic identification. The Gaussian distribution is often used to fit the probability distribution of continuous attributes, but it is not effective for complex logging data. To solve this problem, a hybrid Gaussian probability density estimation based on EM algorithm is proposed. Logging data of the lower ancient gas Wells in the block 41-33 of Sudong are selected as training samples, and data of 44-45 Wells are selected as test samples. The experiment uses the mixed Gaussian model based on EM algorithm, to estimate the probability density of logging data variables at first, and then applies it to the Naive Bayes classifier for the lithology identification. Finally, the fitting effect of the single Gaussian distribution function was used as the comparison. The results reveal that the mixed Gaussian model has a better fitting effect and the performance of the Naive Bayes classifier for the lithology identification could be improved through this way.
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赵铭,金大权,张艳,高世臣,仲婷婷.基于EM和GMM的朴素贝叶斯岩性识别.计算机系统应用,2019,28(6):38-44
ZHAO Ming,JIN Da-Quan,ZHANG Yan,GAO Shi-Chen,ZHONG Ting-Ting.Naive Bayesian Lithology Recognition Based on EM and GMM.COMPUTER SYSTEMS APPLICATIONS,2019,28(6):38-44

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