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Application of Self-Organizing Competitive Network in Lithologic Identification of the Logging Data

机译:自组织竞争网络在伐木数据岩性识别中的应用

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The geological information of logging data is very important for people to determine oil reserves and make the plan of exploitation. So it is essential to identify litho logy of the logging data. Neural network with self-organizing, self-learning and the ability of highly non-linear mapping has been widely used in the field of classification. It has achieved good results. Using self-organizing and self-learning ability of self-organizing neural network, this paper analyzes the factor of litho logic identification, establishes self-organizing competitive network model based on MATLAB. By comparing the two structures of basic competitive network and self-organizing competitive network we achieve litho logy classification. Experimental results show that it is feasible to identify litho logy of the logging data by self-organizing network model. It is a new method of litho logic identification and Its correct rate is high.
机译:伐木数据的地质信息对于人们来确定石油储备并使剥削计划非常重要。因此,必须识别日志记录数据的Litho Logy。具有自组织,自学和高度线性映射的能力的神经网络已被广泛应用于分类领域。它取得了良好的效果。本文采用自组织神经网络自组织神经网络的自学习能力,分析了立力逻辑识别的因素,建立了基于MATLAB的自组织竞争网络模型。通过比较基本竞争网络的两个结构和自组织竞争网络,我们实现了Litho Logy分类。实验结果表明,通过自组织网络模型确定测井数据的Litho Logy是可行的。这是一种新的Litho逻辑识别方法,其正确的速率很高。

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