  Noise suppression in seismic data processing is a crucial research focus for
enhancing subsequent imaging and reservoir prediction. Deep learning has shown
promise in computer vision and holds significant potential for seismic data
processing. However, supervised learning, which relies on clean labels to train
network prediction models, faces challenges due to the unavailability of clean
labels for seismic exploration data. In contrast, self-supervised learning
substitutes traditional supervised learning with surrogate tasks by different
auxiliary means, exploiting internal input data information. Inspired by
Self2Self with Dropout, this paper presents a self-supervised learning-based
noise suppression method called Self-Supervised Deep Convolutional Networks
(SSDCN), specifically designed for Common Reflection Point (CRP) gathers. We
utilize pairs of Bernoulli-sampled instances of the input noisy image as
surrogate tasks to leverage its inherent structure. Furthermore, SSDCN
incorporates geological knowledge through the normal moveout correction
technique, which capitalizes on the approximately horizontal behavior and
strong self-similarity observed in useful signal events within CRP gathers. By
exploiting the discrepancy in self-similarity between the useful signals and
noise in CRP gathers, SSDCN effectively extracts self-similarity features
during training iterations, prioritizing the extraction of useful signals to
achieve noise suppression. Experimental results on synthetic and actual CRP
gathers demonstrate that SSDCN achieves high-fidelity noise suppression.
