Self-Supervised Seismic Random Noise Attenuation With Spatial Attention From a Single Section.

Seismic data denoising has gained much attention from scholars as a crucial part of seismic data processing. With the development of deep learning technology, numerous algorithms well employed in natural image denoising have been used for seismic data denoising. However, seismic data are crucially an array of geophone vibration signals, which have numerous unique structural properties compared with natural images. Thus, employing the traditional image algorithms rather than those developed specifically for seismic data is inadequate to extract all seismic features. In addition, compared with natural images, it is difficult to acquire noise-free ground truth, which restricts the use of supervised deep learning approaches in seismic data denoising. To this end, a self-supervised intertrace seismic data denoising network (STSNet) that requires only one seismic section for random noise attenuation is proposed. Furthermore, we adopt a single-trace vibration signal as the basic unit and fully consider the characteristics of seismic signals. This is the first time to introduce a self-learning spatial attention mechanism among seismic traces to focus on the noise components, which prompts the network's fitting performance. Different comparative experiments demonstrate that our approach can achieve exceptional denoising performance even if only observing a single seismic section. In addition, the ablation experiments also confirm the efficiency of spatial attention.