1-D Multi-Parameter Inversion Based on Deep Neural Network for Geophysical
Bingyang Liang, Shengpeng Yang, Yuanguo Zhou, Simiao Yu, Yubin Gong · 2023
In this paper, a new inversion method is proposed to reconstruction of conductivity and layer thickness of layered media based on machine learning deep neural networks. A full connection deep neural network with 10 hidden layers is used. Training data are generated by Dyadic Green's function of layered media. The hidden layer of the network uses LeakyReLU function as the activation function. Numerical examples of true value of model are provided to benchmark the performance of the proposed methods. The results show that the DNN method can reconstruct the conductivity and layer thickness parameters of the layered model, and the reconstruction results show that the BP neural network has better convergence and accuracy. For the trained network, multi parameter model results can be reconstructed within 0.01s.