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Article overview
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Coherent noise suppression via a self-supervised blind-trace deep learning scheme | | Sixiu Liu
; Claire Birnie
; Tariq Alkhalifah
; | | Date: |
1 Jun 2022 | | Abstract: | Coherent noise regularly plagues seismic recordings, causing artefacts and
uncertainties in products derived from down-the-line processing and imaging
tasks. The outstanding capabilities of deep learning in denoising of natural
and medical images have recently spur a number of applications of neural
networks in the context of seismic data denoising. A limitation of the majority
of such methods is that the deep learning procedure is supervised and requires
clean (noise-free) data as a target for training the network. Blindspot
networks were recently proposed to overcome this requirement, allowing training
to be performed directly on the noisy field data as a powerful suppressor of
random noise. A careful adaptation of the blind-spot methodology allows for an
extension to coherent noise suppression. In this work, we expand the
methodology of blind-spot networks to create a blind-trace network that
successfully removes trace-wise coherent noise. Through an extensive synthetic
analysis, we illustrate the denoising procedure’s robustness to varying noise
levels, as well as varying numbers of noisy traces within shot gathers. It is
shown that the network can accurately learn to suppress the noise when up to
60% of the original traces are noisy. Furthermore, the proposed procedure is
implemented on the Stratton 3D field dataset and is shown to restore the
previously corrupted direct arrivals. Our adaptation of the blind-spot network
for self-supervised, trace-wise noise suppression could lead to other use-cases
such as the suppression of coherent noise arising from wellsite activity,
passing vessels or nearby industrial activity. | | Source: | arXiv, 2206.00301 | | Services: | Forum | Review | PDF | Favorites | |
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