LAUSR

Over the last decades, seismic random noise attenuation has been dominated by transform-based denoising methods over the last decades. However, these methods usually need to estimate the noise level and select an optimal transformation in advance, and they may generate some artifacts in the denoising result (e.g., nonsmooth edges and pseudo-Gibbs phenomena). To overcome these disadvantages, we trained a deep convolutional neural network (CNN) with residual learning for seismic data denoising. We used synthetic seismic data for network training rather than seismic images, and we adopted a method to preprocess the seismic data before it was inputted in the network to help network training. We demonstrate the performance of the deep CNN in seismic random noise attenuation based on the synthetic seismic data. Results of numerical experiments show that our network adaptively and effectively suppresses noise of different levels and exhibits a competitive performance in comparison with the traditional transform-based methods.