  Trace-wise noise is a type of noise often seen in seismic data, which is
characterized by vertical coherency and horizontal incoherency. Using
self-supervised deep learning to attenuate this type of noise, the conventional
blind-trace deep learning trains a network to blindly reconstruct each trace in
the data from its surrounding traces; it attenuates isolated trace-wise noise
but causes signal leakage in clean and noisy traces and reconstruction errors
next to each noisy trace. To reduce signal leakage and improve denoising, we
propose a new loss function and masking procedure in semi-blind-trace deep
learning. Our hybrid loss function has weighted active zones that cover masked
and non-masked traces. Therefore, the network is not blinded to clean traces
during their reconstruction. During training, we dynamically change the masks'
characteristics. The goal is to train the network to learn the characteristics
of the signal instead of noise. The proposed algorithm enables the designed
U-net to detect and attenuate trace-wise noise without having prior information
about the noise. A new hyperparameter of our method is the relative weight
between the masked and non-masked traces' contribution to the loss function.
Numerical experiments show that selecting a small value for this parameter is
enough to significantly decrease signal leakage. The proposed algorithm is
tested on synthetic and real off-shore and land datasets with different noises.
The results show the superb ability of the method to attenuate trace-wise noise
while preserving other events. An implementation of the proposed algorithm as a
Python code is also made available.

Geophysics