A two-stage seismic data denoising network based on deep learning

Seismic data with a high signal-to-noise ratio is beneficial in the inversion and interpretation. Thus, denoising is an indispensable step in the seismic data processing. Traditional denoising methods based on prior knowledge are susceptible to the influence of the hypothesis model and parameters. In contrast, deep learning-based denoising methods can extract deep features from the data autonomously and generate a sophisticated denoising model through adaptive learning. However, these methods generally learn a specific model for each noise level, which results in poor representation ability and suboptimal denoising efficacy when applied to seismic data with different noise levels. To address this issue, we propose a denoising method based on a two-stage convolutional neural network (TSCNN). The TSCNN comprises an estimation subnet (ES) and a denoising subnet (DS). The ES employs a multilayer CNN to estimate noise levels, and the DS performs noise suppression on noisy seismic data based on the ES estimation of the noise distribution. In addition, attention mechanisms are implemented in the proposed network to efficiently extract noise information hidden in complex backgrounds. The TSCNN also adopts the L₁ loss function to enhance the generalization ability and denoising outcome of the model, and a residual learning scheme is utilized to solve the problem of network degradations. Experimental results demonstrate that the proposed method can preserve event features more accurately and outperforms existing methods in terms of signal-to-noise ratio and generalization ability.