A seismic random noise suppression method based on self-supervised deep learning and transfer learning

Random noise suppression is an essential task in the seismic data processing. In recent years deep learning methods have achieved superior results in seismic data denoising. However, obtaining clean data from field seismic data for training is challenging. Therefore, supervised deep learning denoising methods can only use synthetic datasets or field datasets constructed by conventional seismic denoising methods for training. Aiming at this problem, we proposed a self-supervised deep learning seismic denoising method based on Neighbor2Neighbor. This method only requires sampling the noisy data twice to train the denoising network without clean data. For the characteristics of seismic data, we designed a vertical neighbor subsample to make Neighbor2Neighbor more suitable for seismic data. In addition, to further improve the denoising effect in field data, we introduced a transfer learning strategy in our method. Numerical experiments demonstrated that our method outperformed both the conventional denoising seismic method and the supervised learning seismic denoising method after transfer learning.