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Coherent noise regularly plagues seismic recordings, causing artefacts anduncertainties in products derived from down-the-line processing and imagingtasks. The outstanding capabilities of deep learning in denoising of naturaland medical images have recently spur a number of applications of neuralnetworks in the context of seismic data denoising. A limitation of the majorityof such methods is that the deep learning procedure is supervised and requiresclean (noise-free) data as a target for training the network. Blindspotnetworks were recently proposed to overcome this requirement, allowing trainingto be performed directly on the noisy field data as a powerful suppressor ofrandom noise. A careful adaptation of the blind-spot methodology allows for anextension to coherent noise suppression. In this work, we expand themethodology of blind-spot networks to create a blind-trace network thatsuccessfully removes trace-wise coherent noise. Through an extensive syntheticanalysis, we illustrate the denoising procedure's robustness to varying noiselevels, as well as varying numbers of noisy traces within shot gathers. It isshown that the network can accurately learn to suppress the noise when up to60% of the original traces are noisy. Furthermore, the proposed procedure isimplemented on the Stratton 3D field dataset and is shown to restore thepreviously corrupted direct arrivals. Our adaptation of the blind-spot networkfor self-supervised, trace-wise noise suppression could lead to other use-casessuch as the suppression of coherent noise arising from wellsite activity,passing vessels or nearby industrial activity.

Coherent noise suppression via a self-supervised blind-trace deep learning scheme