Removing random noise and improving the resolution of seismic data using deep-learning transformers

Abstract

Post-stack data are susceptible to noise interference and have low resolution, which impacts the accuracy and efficiency of subsequent seismic data interpretation. To address this issue, we propose a deep learning approach called Seis-SUnet, which achieves simultaneous random noise suppression and super-resolution reconstruction of seismic data. First, the Conv-Swin-Block is designed to utilize ordinary convolution and Swin transformer to capture the long-distance dependencies in the spatial location of seismic data, enabling the network to comprehensively comprehend the overall structure of seismic data. Second, to address the problem of weakening the effective signal during network mapping, we use a hybrid training strategy of L1 loss, edge loss and multi-scale structural similarity loss. The edge loss function directs the network training to focus more on the high-frequency information at the edges of seismic data by amplifying the weight. Additionally, the verification of synthetic and field seismic datasets confirms that Seis-SUnet can effectively improve the signal-to-noise ratio and resolution of seismic data. By comparing it with traditional methods and two deep learning reconstruction methods, experimental results demonstrate that Seis-SUnet excels in removing random noise, preserving the continuity of rock layers and maintaining faults as well as being strong robustness.