# Neural-networks for geophysicists and their application to seismic data interpretation

## TL;DR

This paper introduces neural networks for seismic data interpretation, demonstrating their effectiveness in tasks like lithology interpolation and horizon tracking on real field data, aimed at geophysicists familiar with geophysical modeling.

## Contribution

It provides an accessible introduction to neural networks for geophysicists and demonstrates their practical utility in seismic interpretation tasks using real datasets.

## Key findings

- Neural networks can accurately interpret seismic images.
- Application to field data shows practical benefits.
- Neural methods outperform traditional techniques in certain tasks.

## Abstract

Neural-networks have seen a surge of interest for the interpretation of seismic images during the last few years. Network-based learning methods can provide fast and accurate automatic interpretation, provided there are sufficiently many training labels. We provide an introduction to the field aimed at geophysicists that are familiar with the framework of forward modeling and inversion. We explain the similarities and differences between deep networks to other geophysical inverse problems and show their utility in solving problems such as lithology interpolation between wells, horizon tracking and segmentation of seismic images. The benefits of our approach are demonstrated on field data from the Sea of Ireland and the North Sea.