  Over the last few years, Convolutional Neural Networks (CNNs) were
successfully adopted in numerous domains to solve various image-related tasks,
ranging from simple classification to fine borders annotation. Tracking seismic
horizons is no different, and there are a lot of papers proposing the usage of
such models to avoid time-consuming hand-picking. Unfortunately, most of them
are (i) either trained on synthetic data, which can't fully represent the
complexity of subterranean structures, (ii) trained and tested on the same
cube, or (iii) lack reproducibility and precise descriptions of the
model-building process. With all that in mind, the main contribution of this
paper is an open-sourced research of applying binary segmentation approach to
the task of horizon detection on multiple real seismic cubes with a focus on
inter-cube generalization of the predictive model.
