  Seismic acquisition footprints appear as stably faint and dim structures and
emerge fully spatially coherent, causing inevitable damage to useful signals
during the suppression process. Various footprint removal methods, including
filtering and sparse representation (SR), have been reported to attain
promising results for surmounting this challenge. However, these methods, e.g.,
SR, rely solely on the handcrafted image priors of useful signals, which is
sometimes an unreasonable demand if complex geological structures are contained
in the given seismic data. As an alternative, this article proposes a footprint
removal network (dubbed FR-Net) for the unsupervised suppression of acquired
footprints without any assumptions regarding valuable signals. The key to the
FR-Net is to design a unidirectional total variation (UTV) model for footprint
acquisition according to the intrinsically directional property of noise. By
strongly regularizing a deep convolutional autoencoder (DCAE) using the UTV
model, our FR-Net transforms the DCAE from an entirely data-driven model to a
\textcolor{black}{prior-augmented} approach, inheriting the superiority of the
DCAE and our footprint model. Subsequently, the complete separation of the
footprint noise and useful signals is projected in an unsupervised manner,
specifically by optimizing the FR-Net via the backpropagation (BP) algorithm.
We provide qualitative and quantitative evaluations conducted on three
synthetic and field datasets, demonstrating that our FR-Net surpasses the
previous state-of-the-art (SOTA) methods.
