# Automatic Seismic Salt Interpretation with Deep Convolutional Neural Networks

## TL;DR

This paper demonstrates that CNN-based models, specifically U-Net with ResNet, can accurately delineate salt bodies in seismic data, reducing manual effort and bias in interpretation.

## Contribution

The study introduces a CNN framework with network adjustments like ELU, Lovász-Softmax, and stratified K-fold cross-validation for high-precision salt body delineation.

## Key findings

- CNN achieves high agreement with manual interpretations
- Network adjustments improve prediction accuracy
- Effective in areas with weak seismic reflections

## Abstract

One of the most crucial tasks in seismic reflection imaging is to identify the salt bodies with high precision. Traditionally, this is accomplished by visually picking the salt/sediment boundaries, which requires a great amount of manual work and may introduce systematic bias. With recent progress of deep learning algorithm and growing computational power, a great deal of efforts have been made to replace human effort with machine power in salt body interpretation. Currently, the method of Convolutional neural networks (CNN) is revolutionizing the computer vision field and has been a hot topic in the image analysis. In this paper, the benefits of CNN-based classification are demonstrated by using a state-of-art network structure U-Net, along with the residual learning framework ResNet, to delineate salt body with high precision. Network adjustments, including the Exponential Linear Units (ELU) activation function, the Lov\'{a}sz-Softmax loss function, and stratified $K$-fold cross-validation, have been deployed to further improve the prediction accuracy. The preliminary result using SEG Advanced Modeling (SEAM) data shows good agreement between the predicted salt body and manually interpreted salt body, especially in areas with weak reflections. This indicates the great potential of applying CNN for salt-related interpretations.