  Seismic processing often requires suppressing multiples that appear when
collecting data. To tackle these artifacts, practitioners usually rely on Radon
transform-based algorithms as post-migration gather conditioning. However, such
traditional approaches are both time-consuming and parameter-dependent, making
them fairly complex. In this work, we present a deep learning-based alternative
that provides competitive results, while reducing its usage's complexity, and
hence democratizing its applicability. We observe an excellent performance of
our network when inferring complex field data, despite the fact of being solely
trained on synthetics. Furthermore, extensive experiments show that our
proposal can preserve the inherent characteristics of the data, avoiding
undesired over-smoothed results, while removing the multiples. Finally, we
conduct an in-depth analysis of the model, where we pinpoint the effects of the
main hyperparameters with physical events. To the best of our knowledge, this
study pioneers the unboxing of neural networks for the demultiple process,
helping the user to gain insights into the inside running of the network.
