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Comparative application study of SVM and BP algorithms in oil and gas identification

2013-09-19
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In the field of oil and gas exploration, when using logging data to classify and identify oil and gas layers, the use of traditional methods has certain limitations. This paper uses the support vector machine (SVM) method in the data mining classification algorithm and applies it to the identification of oil and gas layers in the Tarim Basin in Xinjiang. In the experiment, the support vector machine algorithm and the BP neural network algorithm were used for comparative testing. The results show that the oil and gas layer identification model established by the support vector machine algorithm has higher recognition test performance, which reflects the superiority of the support vector machine in dealing with multi-class classification problems.

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