  Post-stack seismic profiles are images reflecting containing geological
structures which provides a critical foundation for understanding the
distribution of oil and gas resources. However, due to the limitations of
seismic acquisition equipment and data collecting geometry, the post-stack
profiles suffer from low resolution and strong noise issues, which severely
affects subsequent seismic interpretation. To better enhance the spatial
resolution and signal-to-noise ratio of post-seismic profiles, a multi-scale
attention encoder-decoder network based on generative adversarial network
(MAE-GAN) is proposed. This method improves the resolution of post-stack
profiles, and effectively suppresses noises and recovers weak signals as well.
A multi-scale residual module is proposed to extract geological features under
different receptive fields. At the same time, an attention module is designed
to further guide the network to focus on important feature information.
Additionally, to better recover the global and local information of post-stack
profiles, an adversarial network based on a Markov discriminator is proposed.
Finally, by introducing an edge information preservation loss function, the
conventional loss function of the Generative Adversarial Network is improved,
which enables better recovery of the edge information of the original
post-stack profiles. Experimental results on simulated and field post-stack
profiles demonstrate that the proposed MAE-GAN method outperforms two advanced
convolutional neural network-based methods in noise suppression and weak signal
recovery. Furthermore, the profiles reconstructed by the MAE-GAN method
preserve more geological structures.
