  In recent years, deep neural networks have significantly impacted the seismic
interpretation process. Due to the simple implementation and low interpretation
costs, deep neural networks are an attractive component for the common
interpretation pipeline. However, neural networks are frequently met with
distrust due to their property of producing semantically incorrect outputs when
exposed to sections the model was not trained on. We address this issue by
explaining model behaviour and improving generalization properties through
example forgetting: First, we introduce a method that effectively relates
semantically malfunctioned predictions to their respectful positions within the
neural network representation manifold. More concrete, our method tracks how
models "forget" seismic reflections during training and establishes a
connection to the decision boundary proximity of the target class. Second, we
use our analysis technique to identify frequently forgotten regions within the
training volume and augment the training set with state-of-the-art style
transfer techniques from computer vision. We show that our method improves the
segmentation performance on underrepresented classes while significantly
reducing the forgotten regions in the F3 volume in the Netherlands.
