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《Progress In Geophysics》 2002-02
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Physical Restriction on Model Selection of Neural Network in Dual-Lateral Log Inversion

DING Zhu 1,2 YANG Chang chun 1 TAO Hong gen 2 DENG Gang 3 CHEN Guo hua 2 MA En jun 2 (1. Institute of Geology and Geophysics, Chinese Academy of Sciences, Bejing 100101, China; 2. Well Logging Company of Daqing Petroleum Administrati  
Neural network is a intelligent system which is widely used in pattern recognition, data processing and function fitting. We adopted this method in the inversion of formation resistivity from dual lateral logs in this paper. In order to overcome the limition of overfitting and generalization in the application of nueral network which cannot be resovlved based on the available neural network theory, the variation of the response of dual lateral logs with the invasion radius is analyzed so that the law behind the change can be applied to help the selection of neural network structure during the learning. The prediction error should be enlarged with the increasing of invasion radius. This principle is used flowing the conventional cross validation method. The results show that this new restriction on the model selection of neural network will make the neural network method practicable in the inversion of formation resistivity.
【Fund】: 中国科学院知识创新工程重大项目 (KZCX1 Y0 1)
【CateGory Index】: P631.8
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