A deep convolutional encoder-decoder neural network in assisting seismic horizon tracking

TL;DR

This paper introduces a deep convolutional encoder-decoder neural network that automates seismic horizon tracking in 3D seismic data, significantly reducing manual effort and improving accuracy.

Contribution

It presents a novel end-to-end semantic segmentation approach for automatic seismic horizon tracking, capable of handling multiple horizons simultaneously.

Findings

The neural network accurately tracks multiple horizons.

The method outperforms manual interpretation in efficiency.

The approach is robust across different seismic datasets.

Abstract

Seismic horizons are geologically significant surfaces that can be used for building geology structure and stratigraphy models. However, horizon tracking in 3D seismic data is a time-consuming and challenging problem. Relief human from the tedious seismic interpretation is one of the hot research topics. We proposed a novel automatically seismic horizon tracking method by using a deep convolutional neural network. We employ a state-of-art end-to-end semantic segmentation method to track the seismic horizons automatically. Experiment result shows that our proposed neural network can automatically track multiple horizons simultaneously. We validate the effectiveness and robustness of our proposed method by comparing automatically tracked horizons with manually picked horizons.