应用核Fisher判别技术预测油气储集层
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摘要
核机器学习算法是近几年发展起来的一类新的非线性技术,核Fisher判别分析是其中之一。核Fisher判别分析是经典Fisher线性判别基于核函数的非线性推广,并在实际资料的分类中取得明显效果。本文简化了核Fisher判别分析的计算过程,并将其用于油气储集层横向预测。两个实际资料的计算结果表明,在油气储集层横向预测中,核Fisher判别技术的性能优于Fisher线性判别、模糊模式识别和反向传播人工神经网络。
The kernel-based machine learning algorithms proposed in last few years are a novel class of non-linear techniques, one of which is kernel Fisher discriminanting analysis. The kernel Fisher discriminant analyses is the non-linear generalization of classic Fisher linear discriminant based on the kernel functions and obtains the better results in the classification problem of real data. The paper introduced a lateral prediction approach of hydrocarbon reservoir based on kernel Fisher discriminanting analysis. The computational results of field data show that the performance of kernel Fisher discriminant is superior to those of Fisher linear discriminant, fuzzy pattern recognition and backward propagation neural networks in lateral prediction of hydrocarbon reservoir.
The kernel-based machine learning algorithms proposed in last few years are a novel class of non-linear techniques, one of which is kernel Fisher discriminanting analysis. The kernel Fisher discriminant analyses is the non-linear generalization of classic Fisher linear discriminant based on the kernel functions and obtains the better results in the classification problem of real data. The paper introduced a lateral prediction approach of hydrocarbon reservoir based on kernel Fisher discriminanting analysis. The computational results of field data show that the performance of kernel Fisher discriminant is superior to those of Fisher linear discriminant, fuzzy pattern recognition and backward propagation neural networks in lateral prediction of hydrocarbon reservoir.
引文
[1]边肇祺,阎平凡等.模式识别.清华大学出版社,1987
[2] 边肇祺,张学工等.模式识别（第二版）.清华大学出版 社,2000
[3] 蔡煜东,宫家文等.应用人工神经网络方法预测油气. 石油地球物理勘探,1993,28（5）:634～638
[4] 陈永仁,庞福民.储层横向预测油气的一种模式识别 法.石油物探,1992,31（4）:70～79
[5]钱绍新.应用模式识别方法预测油气储集层.地球物 理学报,1992,35（5）:630～636
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[7] 许建华,蔡瑞.有监督SOM神经网络在油气预测中 的应用.石油物探,1998,37（1）:71～76
[8]张学工译.统计学习理论的本质（第二版）.清华大学 出版社,2000
[9]张学工.关于统计学习理论与支持向量机.自动化学 报,2000,26（1）:32～44。
[10] Duda R O and Hart P E. Pattern classification and scene analysis. New York: John Wiley &. Sons, 1973
[11] Mika S,Ratsch G.Weston J.Scholkopf B and Muller K -R. Fisher discriminant analysis with kernels. Neural Networks for Signal Processing IX. New York: IEEE Press, 1999,41-48
[12] Theodoridis S and Koutroumbas K. Pattern recognition.San Diego:Academic Press,1999
[13] Tou J T and Gonzadez R C. Pattern recognition principles. Reading: Addison-Wesley, 1974
[14] Cortes C and Vapnik V N. Support vector networks. Machine Learning,1995, 20(3):273~297
[15] Vapnik V N. Statistical learning theory. New York; Wiley, 1998
[2] 边肇祺,张学工等.模式识别（第二版）.清华大学出版 社,2000
[3] 蔡煜东,宫家文等.应用人工神经网络方法预测油气. 石油地球物理勘探,1993,28（5）:634～638
[4] 陈永仁,庞福民.储层横向预测油气的一种模式识别 法.石油物探,1992,31（4）:70～79
[5]钱绍新.应用模式识别方法预测油气储集层.地球物 理学报,1992,35（5）:630～636
[6]肖辞源,朱白文.综合多种地震信息预测油气富集区 的模糊数学方法.石油地球物理勘探,1990,25（2）: 191～200
[7] 许建华,蔡瑞.有监督SOM神经网络在油气预测中 的应用.石油物探,1998,37（1）:71～76
[8]张学工译.统计学习理论的本质（第二版）.清华大学 出版社,2000
[9]张学工.关于统计学习理论与支持向量机.自动化学 报,2000,26（1）:32～44。
[10] Duda R O and Hart P E. Pattern classification and scene analysis. New York: John Wiley &. Sons, 1973
[11] Mika S,Ratsch G.Weston J.Scholkopf B and Muller K -R. Fisher discriminant analysis with kernels. Neural Networks for Signal Processing IX. New York: IEEE Press, 1999,41-48
[12] Theodoridis S and Koutroumbas K. Pattern recognition.San Diego:Academic Press,1999
[13] Tou J T and Gonzadez R C. Pattern recognition principles. Reading: Addison-Wesley, 1974
[14] Cortes C and Vapnik V N. Support vector networks. Machine Learning,1995, 20(3):273~297
[15] Vapnik V N. Statistical learning theory. New York; Wiley, 1998
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