# Example Forgetting: A Novel Approach to Explain and Interpret Deep Neural Networks in Seismic Interpretation

## TL;DR

This paper introduces a novel method called example forgetting to explain and improve deep neural network performance in seismic interpretation, addressing issues of trust and generalization by analyzing model forgetting and augmenting training data.

## Contribution

It presents a new technique to relate model mispredictions to the neural network's representation and enhances generalization through targeted data augmentation.

## Key findings

- Improved segmentation accuracy on underrepresented classes.
- Reduced forgotten regions in seismic volume.
- Enhanced understanding of model behavior during training.

## Abstract

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.