  Noise in seismic data arises from numerous sources and is continually
evolving. The use of supervised deep learning procedures for denoising of
seismic datasets often results in poor performance: this is due to the lack of
noise-free field data to act as training targets and the large difference in
characteristics between synthetic and field datasets. Self-supervised,
blind-spot networks typically overcome these limitation by training directly on
the raw, noisy data. However, such networks often rely on a random noise
assumption, and their denoising capabilities quickly decrease in the presence
of even minimally-correlated noise. Extending from blind-spots to blind-masks
can efficiently suppress coherent noise along a specific direction, but it
cannot adapt to the ever-changing properties of noise. To preempt the network's
ability to predict the signal and reduce its opportunity to learn the noise
properties, we propose an initial, supervised training of the network on a
frugally-generated synthetic dataset prior to fine-tuning in a self-supervised
manner on the field dataset of interest. Considering the change in peak
signal-to-noise ratio, as well as the volume of noise reduced and signal
leakage observed, we illustrate the clear benefit in initialising the
self-supervised network with the weights from a supervised base-training. This
is further supported by a test on a field dataset where the fine-tuned network
strikes the best balance between signal preservation and noise reduction.
Finally, the use of the unrealistic, frugally-generated synthetic dataset for
the supervised base-training includes a number of benefits: minimal prior
geological knowledge is required, substantially reduced computational cost for
the dataset generation, and a reduced requirement of re-training the network
should recording conditions change, to name a few.
