  Noise suppression is an essential step in any seismic processing workflow. A
portion of this noise, particularly in land datasets, presents itself as random
noise. In recent years, neural networks have been successfully used to denoise
seismic data in a supervised fashion. However, supervised learning always comes
with the often unachievable requirement of having noisy-clean data pairs for
training. Using blind-spot networks, we redefine the denoising task as a
self-supervised procedure where the network uses the surrounding noisy samples
to estimate the noise-free value of a central sample. Based on the assumption
that noise is statistically independent between samples, the network struggles
to predict the noise component of the sample due to its randomnicity, whilst
the signal component is accurately predicted due to its spatio-temporal
coherency. Illustrated on synthetic examples, the blind-spot network is shown
to be an efficient denoiser of seismic data contaminated by random noise with
minimal damage to the signal; therefore, providing improvements in both the
image domain and down-the-line tasks, such as inversion. To conclude the study,
the suggested approach is applied to field data and the results are compared
with two commonly used random denoising techniques: FX-deconvolution and
Curvelet transform. By demonstrating that blind-spot networks are an efficient
suppressor of random noise, we believe this is just the beginning of utilising
self-supervised learning in seismic applications.
