Using Wavelet Packet Denoising and BP Neural Network Based on GA Optimization for Transient Electromagnetic Inversion
Transient electromagnetic inversion is a complex nonlinear problem with high-dimensional non-convexity.The traditional BP neural network can effectively alleviate the over-fitting phenomenon for transient electromagnetic inversion.However,the BP method has the disadvantage of converges slowly and easily falls into local optimum.In order to solve these problems,an approach based on wavelet packet denoising(WPD)and genetic algorithm(GA)to optimize BP neural network(WPD-GA-BP)was proposed and applied to transient electromagnetic inversion.A wavelet packet denoising method based on hard threshold and Db13 was used to reduce noise signal from observed magnetic field data.And a sample collection strategy was proposed to remove redundant features.Additionally,the global GA algorithm was introduced to optimize the BP initial weight,which improved the learning ability and solution accuracy for BP.Finally,based on the 1-D transient electromagnetic forward theory with center loop source,a layered geoelectric model was established,and then inversion was performed after WPD processing,in which the inversion results by GA-BP algorithm were compared with that of the traditional Occam,BP,particle swarm optimization-BP(PSO-BP)and differential evolution-BP(DE-BP).The results of theoretical model and measured examples show that the proposed method is superior to others algorithm in the accuracy,stability and higher forward data fitting ability,which can be effectively applied to the inversion interpretation for electromagnetic exploration.
万方数据
维普
