Self-supervised CNN with coordinate attention and time-frequency hybrid loss for seismic data reconstruction

Deep learning methods for high-precision seismic data reconstruction are typically constrained by the reliance on high-quality labeled datasets and the inherent limitations of time-domain loss functions in capturing frequency features. This study introduces a self-supervised multi-layer wavelet convolutional neural network (MWCNN) enhanced with the coordinate attention (CA) module, termed as CMWCNN, for high-precision seismic data reconstruction. The self-supervised mechanism is incorporated through dynamic random masking, which generates pseudo input-label pairs from incomplete seismic data to diversify the training datasets. The CA module, which extracts spatial location details via global pooling in horizontal and vertical directions, is integrated to guide CMWCNN to accurately capture spatial features within regions of missing traces. Multi-level discrete wavelet transform (DWT) is employed to preserve multi-scale frequency features, enhancing the network’s ability to reconstruct fine details. Furthermore, a novel time-frequency hybrid loss function is proposed for CMWCNN training, referred to as TF‑CMWCNN. This loss function simultaneously constrains multi-scale frequency features and time-domain characteristics to effectively enhance the network’s robustness. Experimental results demonstrate that the proposed CMWCNN achieves superior reconstruction accuracy and computational efficiency compared to ResNet, U-Net++, and MWCNN. Additionally, compared to conventional time loss functions, the proposed hybrid loss improves CMWCNN performance by approximately 12 dB in signal-to-noise ratio (SNR) and peak signal-to-noise ratio (PSNR).