卷积神经网络在AVA反演应用中影响因素研究OA北大核心CSTPCD

Starting from the AVA(amplitude-versus-angle)inversion based on convolutional neural networks(CNNs),by generating extensive datasets for network training,we analyze the effects of different hyperparameters on prediction outcomes,identify the optimal settings,and establish guidelines for hyperparameter adjustment.By comparing the prediction perform-ance of three distinct CNN models,we identify the optimal network architecture for prestack parameter inversion,and propose a workflow for elastic parameter prediction using CNN applied to pre-stack angle-domain gathers.Numerical examples dem-onstrate that the prediction accuracy of elastic parameters is the highest when using the constructed training data set,the se-lected CNN architecture,and the optimized hyperparameters.