  This paper presents a discussion on data selection for deep learning in the
field of seismic interpretation. In order to achieve a robust generalization to
the target volume, it is crucial to identify the specific samples are the most
informative to the training process. The selection of the training set from a
target volume is a critical factor in determining the effectiveness of the deep
learning algorithm for interpreting seismic volumes. This paper proposes the
inclusion of interpretation disagreement as a valuable and intuitive factor in
the process of selecting training sets. The development of a novel data
selection framework is inspired by established practices in seismic
interpretation. The framework we have developed utilizes representation shifts
to effectively model interpretation disagreement within neural networks.
Additionally, it incorporates the disagreement measure to enhance attention
towards geologically interesting regions throughout the data selection
workflow. By combining this approach with active learning, a well-known machine
learning paradigm for data selection, we arrive at a comprehensive and
innovative framework for training set selection in seismic interpretation. In
addition, we offer a specific implementation of our proposed framework, which
we have named ATLAS. This implementation serves as a means for data selection.
In this study, we present the results of our comprehensive experiments, which
clearly indicate that ATLAS consistently surpasses traditional active learning
frameworks in the field of seismic interpretation. Our findings reveal that
ATLAS achieves improvements of up to 12% in mean intersection-over-union.
