  In recent years, self-supervised procedures have advanced the field of
seismic noise attenuation, due to not requiring a massive amount of clean
labeled data in the training stage, an unobtainable requirement for seismic
data. However, current self-supervised methods usually suppress simple noise
types, such as random and trace-wise noise, instead of the complicated, aliased
ground roll. Here, we propose an adaptation of a self-supervised procedure,
namely, blind-fan networks, to remove aliased ground roll within seismic shot
gathers without any requirement for clean data. The self-supervised denoising
procedure is implemented by designing a noise mask with a predefined direction
to avoid the coherency of the ground roll being learned by the network while
predicting one pixel's value. Numerical experiments on synthetic and field
seismic data demonstrate that our method can effectively attenuate aliased
ground roll.
