3D random noise attenuation based on unsupervised deep learning network

Random noises contaminate seismic signals and reduce the signal-to-noise ratio of seismic data, which will affect subsequent seismic data processing. A denoising method based on supervised deep learning usually requires a large number of labels to train the network, but it is very challenging to make noise-free labels using observed seismic data. To attenuate random noises and extract useful signals from multi-dimensional seismic data, we propose an end-to-end neural network based on unsupervised deep learning, which consists of a fully connected module, an encoder module and a decoder module. A skip connection similar to a residual structure is added between the encoder and decoder to improve the performance of denoising. To strengthen the network further, a data enhancement method is used to segment large-scale multi-dimensional noisy seismic data into a large number of small-scale one-dimensional data for iteration. Appropriate slicing and sliding sizes for data enhancement could improve the calculation efficiency and denoising effect of the network. The application to synthetic data and actual data acquired in Bohai oilfield shows that the proposed method is better than traditional denoising techniques in random noise attenuation and signal extraction.