Application of improved AlexNet for seismic data denoising

We propose an improved AlexNet network model, to address the problems of low denoising performance of traditional LeNet-5 neural networks in removing random noise from seismic data. The network retains the original eight-layer calculation depth and uses ReLU as the Activation function to reduce the convolution core and the number of nodes in the convolution layer, thus obtaining higher noise feature extraction accuracy. The network trains the network with 10000 seismic data, tests the network with 1000 data, and optimizes the network. Experiments were conducted using a wide range of Marousi2 seismic data, and the results showed that the proposed network has good denoising performance. Compared with traditional wavelet algorithms, SVD, and LeNet-5 networks, experimental results show that the proposed network can achieve higher PSNR and SNR values and has better seismic data denoising performance compared to the above networks.