Abstract: As the environment of geophysical exploration becomes more complex, and it is limited by acquisition and processing technology which results in low resolution and signal-to-noise ratio of post-stack seismic data.Therefore, how to enhance its resolution while realizing noise attenuation is a non-negligible problem. A design called DESRGAN(Depth-Enhanced Super-Resolution Generative Adversarial Network) is proposed, which is intended to be applied to the task of super-resolution reconstruction of seismic data. DESRGAN uses a LRDB(Lightweight Residual Dense Block) as the base unit to improve the efficiency and stability of the training
process, passes through channel attention in the deep feature extraction phase to increase the focus on important features and performs an up-sampling operation using pixel reorganization instead of interpolation to take into account the spatial relationship between pixels. Experimental results on synthetic and field data show that the network can reconstruct the synthetic data as the test set and it is well generalized to the field data. Compared with classic GAN(Generative Adversarial Network) and CNN(Convolutional Neural Network), the reconstructed results are visually clearer, and have higher peak signal-to-noise ratio and structural similarity in quantitative analysis.

Key words: seismic data, resolution, signal-to-noise ratio, depth-enhanced super-resolution generative adversarial network