基于EM和GMM的朴素贝叶斯岩性识别
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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.
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.
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.
引文
1周志华.机器学习.北京:清华大学出版社,2016. 147-154.
2彭兴媛,刘琼荪.不同类变量下属性聚类的朴素贝叶斯分类算法.计算机应用,2011,31(11):3072-3074.
3金展,范晶,陈峰,等.基于朴素贝叶斯和支持向量机的自适应垃圾短信过滤系统.计算机应用,2008, 28(3):714-718.
4李晶辉,张小刚,陈华,等.一种改进隐朴素贝叶斯算法的研究.小型微型计算机系统,2013, 34(7):1654-1658.[doi:10.3969/j.issn. 1000-1220.2013.07.041]
5王玮,陈恩红,王煦法.基于贝叶斯方法的知识发现.小型微型计算机系统,2000, 21(7):703-705.[doi:10.3969/j.issn.1000-1220.2000.07.009]
6秦锋,任诗流,程泽凯,等.基于属性加权的朴素贝叶斯分类算法.计算机工程与应用,2008, 44(6):107-109.[doi:10.3778/j.issn.1002-8331.2008.06.033]
7钟金琴,辜丽川,檀结庆,等.基于分裂EM算法的GMM参数估计.计算机工程与应用,2012, 48(34):28-32,59.[doi:10.3778/j.issn.1002-8331.1206-0419]
8徐定杰,沈忱,沈锋.混合高斯分布的变分贝叶斯学习参数估计.上海交通大学学报,2013,47(7):1119-1125.
9 Hobolth A, Jensen JL. Statistical inference in evolutionary models of DNA sequences via the EM algorithm. Statistical Applications in Genetics and Molecular Biology, 2005, 4(1):18.
10 Taheri S, Mammadov M. Learning the naive Bayes classifier with optimization models. International Journal of Applied Mathematics and Computer Science, 2013, 23(4):787-795.[doi:10.2478/amcs-2013-0059]
11 Jiang LX, Wang DH, Cai ZH, et al. Survey of improving naive Bayes for classification. Proceedings of the 3rd International Conference on Advanced Data Mining and Applications. Harbin, China. 2007. 134-145.
12袁照威,段正军,张春雨,等.基于马尔科夫概率模型的碳酸盐岩储集层测井岩性解释.新疆石油地质,2017, 38(1):96-102.
13高世臣,张丹.多参数概率融合法在叠前地震储层预测中的应用--以苏里格气田苏194区块为例.油气地质与采收率,2015, 22(6):61-67.[doi:10.3969/j.issn.1009-9603.2015.06.011]
14 Theodoridis S, Koutroumbas K.模式识别.李晶皎,王爱侠,王骄,译.北京:电子工业出版社,2010:8-10.
2彭兴媛,刘琼荪.不同类变量下属性聚类的朴素贝叶斯分类算法.计算机应用,2011,31(11):3072-3074.
3金展,范晶,陈峰,等.基于朴素贝叶斯和支持向量机的自适应垃圾短信过滤系统.计算机应用,2008, 28(3):714-718.
4李晶辉,张小刚,陈华,等.一种改进隐朴素贝叶斯算法的研究.小型微型计算机系统,2013, 34(7):1654-1658.[doi:10.3969/j.issn. 1000-1220.2013.07.041]
5王玮,陈恩红,王煦法.基于贝叶斯方法的知识发现.小型微型计算机系统,2000, 21(7):703-705.[doi:10.3969/j.issn.1000-1220.2000.07.009]
6秦锋,任诗流,程泽凯,等.基于属性加权的朴素贝叶斯分类算法.计算机工程与应用,2008, 44(6):107-109.[doi:10.3778/j.issn.1002-8331.2008.06.033]
7钟金琴,辜丽川,檀结庆,等.基于分裂EM算法的GMM参数估计.计算机工程与应用,2012, 48(34):28-32,59.[doi:10.3778/j.issn.1002-8331.1206-0419]
8徐定杰,沈忱,沈锋.混合高斯分布的变分贝叶斯学习参数估计.上海交通大学学报,2013,47(7):1119-1125.
9 Hobolth A, Jensen JL. Statistical inference in evolutionary models of DNA sequences via the EM algorithm. Statistical Applications in Genetics and Molecular Biology, 2005, 4(1):18.
10 Taheri S, Mammadov M. Learning the naive Bayes classifier with optimization models. International Journal of Applied Mathematics and Computer Science, 2013, 23(4):787-795.[doi:10.2478/amcs-2013-0059]
11 Jiang LX, Wang DH, Cai ZH, et al. Survey of improving naive Bayes for classification. Proceedings of the 3rd International Conference on Advanced Data Mining and Applications. Harbin, China. 2007. 134-145.
12袁照威,段正军,张春雨,等.基于马尔科夫概率模型的碳酸盐岩储集层测井岩性解释.新疆石油地质,2017, 38(1):96-102.
13高世臣,张丹.多参数概率融合法在叠前地震储层预测中的应用--以苏里格气田苏194区块为例.油气地质与采收率,2015, 22(6):61-67.[doi:10.3969/j.issn.1009-9603.2015.06.011]
14 Theodoridis S, Koutroumbas K.模式识别.李晶皎,王爱侠,王骄,译.北京:电子工业出版社,2010:8-10.
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