A Lightweight Multi-Scale Network With High-Frequency Learning for Seismic Data Denoising

Facing complex geological conditions, the acquired seismic data are often seriously contaminated by noise. How to recover seismic signals in a strong noise background remains a challenging issue. In this paper, we propose a Lightweight Cascaded Multi-Scale (LCMS) network for seismic data denoising, which efficiently and effectively mines seismic signals from low Signal-to-Noise Ratio (SNR) data while reducing computational costs. The network comprises a series of LCMS residual blocks to fully explore the multi-scale dense features of seismic data, in which each layer directly accesses the features of all previous layers to implement a continuous memory mechanism, not only enhancing feature utilization but also reducing redundant computations. The architecture of the network is optimized to achieve a compact structure, ensuring that it requires fewer parameters and computational resources compared to traditional deep networks, thus enabling faster processing without sacrificing performance. Furthermore, high-frequency information mainly presents seismic reflection details, and the lack of high frequencies affects the bandwidth of seismic data. To this end, we employ the nonsubsampled shearlet transform (NSST) to acquire high-frequency prior information and integrate it into the network, which guides the network to focus more on the learning of high-frequency features. The qualitative and quantitative performance evaluations are conducted on synthetic data and real seismic data. Compared with the SOTA methods, our method achieves higher Peak Signal-to-Noise Ratio (PSNR) values and fewer parameters. When applied to real seismic data, our method efficiently recovers more effective signals of seismic data.