Abstract:

In view of the problem that the actual seismic data is interfered by a large amount of random noise and it is difficult to obtain paired noise-free data, this study proposes a random noise suppression method of seismic data based on CycleGAN(Cycle Generative Adversarial Network) to obtain high-quality seismic data. The residual network is introduced into the generative network of cyclic generative adversarial network, and the training speed of the network is accelerated by jumping connection, and the convolution layer in the residual block is expanded and the structure of the residual block is enhanced to obtain the sample features better. Experiments are conducted with synthetic data and actual data respectively, and evaluation indexes such as SNR(Signal to Noise Ratio) and MSE(Mean Square Error) are used to verify the denoising effect. The results show that compared with CNN, the SNR, MSE and PSNR(Peak Signal-to-Noise Ratio) of the proposed method increased by 0.59 dB, 23.72 and 2.81 dB respectively, in the synthetic data experiment. In the actual data experiment, the increase is 4.63 dB, 1.13 and 0.77 dB, respectively, and the training time is reduced by about 58%.

Key words: seismic data, random noise, denoising, generative adversarial network, CycleGAN, image processing, convolutional neural network, deep learning