  Seismic data often undergoes severe noise due to environmental factors, which
seriously affects subsequent applications. Traditional hand-crafted denoisers
such as filters and regularizations utilize interpretable domain knowledge to
design generalizable denoising techniques, while their representation
capacities may be inferior to deep learning denoisers, which can learn complex
and representative denoising mappings from abundant training pairs. However,
due to the scarcity of high-quality training pairs, deep learning denoisers may
sustain some generalization issues over various scenarios. In this work, we
propose a self-supervised method that combines the capacities of deep denoiser
and the generalization abilities of hand-crafted regularization for seismic
data random noise attenuation. Specifically, we leverage the Self2Self (S2S)
learning framework with a trace-wise masking strategy for seismic data
denoising by solely using the observed noisy data. Parallelly, we suggest the
weighted total variation (WTV) to further capture the horizontal local smooth
structure of seismic data. Our method, dubbed as S2S-WTV, enjoys both high
representation abilities brought from the self-supervised deep network and good
generalization abilities of the hand-crafted WTV regularizer and the
self-supervised nature. Therefore, our method can more effectively and stably
remove the random noise and preserve the details and edges of the clean signal.
To tackle the S2S-WTV optimization model, we introduce an alternating direction
multiplier method (ADMM)-based algorithm. Extensive experiments on synthetic
and field noisy seismic data demonstrate the effectiveness of our method as
compared with state-of-the-art traditional and deep learning-based seismic data
denoising methods.
