Ground-truth-free Deep Learning for 3-D Seismic Denoising and Reconstruction with Channel Attention Mechanism

Abstract

Seismic denoising methods using supervised methods rely on a large number of high-quality paired training datasets to reach satisfactory performances. There are two ways to generate labels for network training: one is to simulate the synthetic data using the wave equation, and the other is to utilize denoised data obtained via conventional methods. However, using these labels will limit the networks' noise attenuation performance compared with using large volumes of noise-free data as labels. Here, we propose a ground-truth-free way for three-dimensional (3-D) seismic data processing. First, we use the 3-D patch scheme to divide the noisy seismic data into many fixed-size blocks and then flatten the obtained 3-D patches to expand the training set and capture more higher-order waveform characteristics from the input noisy data. Next, the obtained training dataset is sent into the proposed deep learning (DL) network, where the encoder blocks compress the feature map to extract the waveform features, and the decoder blocks reconstruct the denoised feature map. Notably, the convolutional bottleneck attention module (CBAM) and efficient channel attention (ECA) module are applied to guide the network to focus on signal fluctuation features with fewer network parameters. In addition, the concatenation mechanism is used to enable deep networks to reuse shallow-layer waveform features and mitigate overfitting during training. Finally, the unpatching scheme is used to reconstruct the denoised 3-D seismic data. Numerical experiments demonstrate that the proposed method outperforms benchmark approaches in terms of signal-to-noise ratio (SNR) improvement and useful signal preservation.