An ICPSO-RBFNN nonlinear inversion for electrical resistivity imaging  

作　　者：江沸菠 戴前伟 董莉 

机构地区：[1]School of Geosciences and Info-Physics, Central South University [2]College of Physics and Information Science, Hunan Normal University [3]Department of Information Science and Engineering, Hunan International Economics University

出　　处：《Journal of Central South University》2016年第8期2129-2138,共10页中南大学学报（英文版）

基　　金：Project(41374118)supported by the National Natural Science Foundation,China;Project(20120162110015)supported by Research Fund for the Doctoral Program of Higher Education,China;Project(2015M580700)supported by the China Postdoctoral Science Foundation,China;Project(2016JJ3086)supported by the Hunan Provincial Natural Science Foundation,China;Project(2015JC3067)supported by the Hunan Provincial Science and Technology Program,China;Project(15B138)supported by the Scientific Research Fund of Hunan Provincial Education Department,China

摘　　要：To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information criterion(IC) and particle swarm optimization(PSO) is presented. In the proposed method, IC is applied to obtain the hidden layer structure by calculating the optimal IC value automatically and PSO algorithm is used to optimize the centers and widths of the radial basis functions in the hidden layer. Meanwhile, impacts of different information criteria to the inversion results are compared, and an implementation of the proposed ICPSO algorithm is given. The optimized neural network has one hidden layer with 261 nodes selected by AKAIKE's information criterion(AIC) and it is trained on 32 data sets and tested on another 8 synthetic data sets. Two complex synthetic examples are used to verify the feasibility and effectiveness of the proposed method with two learning stages. The results show that the proposed method has better performance and higher imaging quality than three-layer and four-layer back propagation neural networks(BPNNs) and traditional least square(LS) inversion.To improve the global search ability and imaging quality of electrical resistivity imaging（ERI） inversion, a two-stage learning ICPSO algorithm of radial basis function neural network（RBFNN） based on information criterion（IC） and particle swarm optimization（PSO） is presented. In the proposed method, IC is applied to obtain the hidden layer structure by calculating the optimal IC value automatically and PSO algorithm is used to optimize the centers and widths of the radial basis functions in the hidden layer. Meanwhile, impacts of different information criteria to the inversion results are compared, and an implementation of the proposed ICPSO algorithm is given. The optimized neural network has one hidden layer with 261 nodes selected by AKAIKE＇s information criterion（AIC） and it is trained on 32 data sets and tested on another 8 synthetic data sets. Two complex synthetic examples are used to verify the feasibility and effectiveness of the proposed method with two learning stages. The results show that the proposed method has better performance and higher imaging quality than three-layer and four-layer back propagation neural networks（BPNNs） and traditional least square（LS） inversion.

关 键 词：electrical resistivity imaging nonlinear inversion information criterion（IC） radial basis function neural network（RBFNN） particle swarm optimization（PSO） 

分 类 号：P631.322[天文地球—地质矿产勘探] TP183[天文地球—地质学]