Abstract: Due to the complexity of geophysical exploration environment, the seismic data collected are often covered by random noise, therefore a convolution based cyclic generation adversarial network (CycleGAN) is introduced into seismic data denoising. The key of the network is to build a good structure of generator and discriminator and optimize the appropriate network parameters. The input data and label data of the network are generated each other to form a ring network structure, so the loss of cyclic consistency can be introduced to improve the accuracy of network parameter training. The non local neural network module is used as the residual link of CycleGAN generator to improve the feature extraction ability. Through the comparison of data visualization, peak signal-to-noise ratio and mean square error, the feasibility of this method is verified. The results show that compared with the general gan network and the residual network denoising effect, this method has better noise performance, higher peak signal-to-noise ratio and can clearer data imaging under different noise levels, which has a certain guiding significance for the actual production work.

Key words: non local neural network;  , cycle generation adversarial network;  , seismic data;  , random noise