  Achieving desirable receiver sampling in ocean bottom acquisition is often
not possible because of cost considerations. Assuming adequate source sampling
is available, which is achievable by virtue of reciprocity and the use of
modern randomized (simultaneous-source) marine acquisition technology, we are
in a position to train convolutional neural networks (CNNs) to bring the
receiver sampling to the same spatial grid as the dense source sampling. To
accomplish this task, we form training pairs consisting of densely sampled data
and artificially subsampled data using a reciprocity argument and the
assumption that the source-site sampling is dense. While this approach has
successfully been used on the recovery monochromatic frequency slices, its
application in practice calls for wavefield reconstruction of time-domain data.
Despite having the option to parallelize, the overall costs of this approach
can become prohibitive if we decide to carry out the training and recovery
independently for each frequency. Because different frequency slices share
information, we propose the use the method of transfer training to make our
approach computationally more efficient by warm starting the training with CNN
weights obtained from a neighboring frequency slices. If the two neighboring
frequency slices share information, we would expect the training to improve and
converge faster. Our aim is to prove this principle by carrying a series of
carefully selected experiments on a relatively large-scale five-dimensional
data synthetic data volume associated with wide-azimuth 3D ocean bottom node
acquisition. From these experiments, we observe that by transfer training we
are able t significantly speedup in the training, specially at relatively
higher frequencies where consecutive frequency slices are more correlated.

Geophysics;Machine Learning;Image and Video Processing