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.

通过对地震解释领域中深度学习的数据选择进行讨论，本文提出将解释不一致性作为一个有价值且直观的因素纳入选择训练集的过程中，发展出一种利用神经网络内部来建模解释不一致性的数据选择框架，命名为 ATLAS。综合实验结果表明，ATLAS 在地震解释领域中相比传统主动学习框架可提高 12% 的平均交叉联合。