基于遗传算法优化的BP神经网络在密度界面反演中的应用

doi:10.13278/j.cnki.jjuese.201702301

Abstract

Abstract: The method of BP neural network has achieved good results in the inversion of 2D density interface, however, the converging speed and inversion accuracy would decrease when it is used to inverse 3D density interface due to more complicated model and more parameters. Genetic algorithm is used to optimize the process of choosing weights and thresholds of BP network in this paper in order to improve inversion results. Then a better network model is obtained and this model will be utilized in the inversion of density interface model. This method could increase inversion accuracy as well as reduce calculation time, and better inversion results would be achieved. At last the method is utilized to inverse the depth of Moho in some region in France and the application effect is good. It is illustrated that BP neural network based on genetic algorithm has benign application value and research prospect in the inversion of density interface.

Key words: BP neural network, genetic algorithm, inversion of density interface, network training, optimization

CLC Number:

P312.1

Zhang Dailei, Huang Danian, Zhang Chong. Application of BP Neural Network Based on Genetic Algorithm in the Inversion of Density Interface[J].Journal of Jilin University(Earth Science Edition), 2017, 47(2): 580-588.

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Application of BP Neural Network Based on Genetic Algorithm in the Inversion of Density Interface

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