Example Forgetting: A Novel Approach to Explain and Interpret Deep Neural Networks in Seismic Interpretation
This addresses distrust in neural networks for seismic interpretation practitioners by enhancing interpretability and generalization, though it appears incremental as it builds on existing style transfer techniques.
The paper tackles the problem of deep neural networks producing semantically incorrect outputs in seismic interpretation when exposed to untrained sections by introducing example forgetting to explain model behavior and improve generalization. The method improves segmentation performance on underrepresented classes and significantly reduces forgotten regions in the F3 volume in the Netherlands.
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.