Seismic Data Random Noise Attenuation Using Visible Blind Spot Self-Supervised Learning

Due to various reasons, seismic data are often inevitably affected by noise. Therefore, random noise suppression of seismic data is a key step for seismic data processing workflow. Recently, deep learning method has performed well in seismic data denoising. In this study, we propose a self-supervised deep learning seismic data noise attenuation method. We introduced an effective Blind2Unblind (B2U) denoising framework, which can complete denoising using only a single noisy seismic data. Use a mask mapper with global awareness, which can sample all pixels at the blind spots on noisy data and map them to a same channel. At the same time, a re-visible loss function is used to train the network, which can optimize all blind spots, mitigating the information loss and retaining more details of geological structure. The denoising experiments on synthetic and field data show that our method has achieved superior results compared with previous work.