# Seismic facies recognition based on prestack data using deep convolutional autoencoder

arXiv: https://arxiv.org/abs/1704.02446 · HTML: https://mlsanity.com/paper/1704.02446/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2017.

## Built on by (most cited first, 5 of 5)

- [Man-recon: manifold learning for reconstruction with deep autoencoder for smart seismic interpretation](https://mlsanity.com/paper/2212.07568/) (2022)
- [Example Forgetting: A Novel Approach to Explain and Interpret Deep Neural Networks in Seismic Interpretation](https://mlsanity.com/paper/2302.14644/) (2023)
- [Self-Supervised Delineation of Geological Structures using Orthogonal Latent Space Projection](https://mlsanity.com/paper/2108.09605/) (2021)
- [A convolutional neural network approach to deblending seismic data](https://mlsanity.com/paper/2409.07930/) (2024)
- [Effective Data Selection for Seismic Interpretation through Disagreement](https://mlsanity.com/paper/2406.05149/) (2024)

## Abstract (arXiv)

Prestack seismic data carries much useful information that can help us find more complex atypical reservoirs. Therefore, we are increasingly inclined to use prestack seismic data for seis- mic facies recognition. However, due to the inclusion of ex- cessive redundancy, effective feature extraction from prestack seismic data becomes critical. In this paper, we consider seis- mic facies recognition based on prestack data as an image clus- tering problem in computer vision (CV) by thinking of each prestack seismic gather as a picture. We propose a convo- lutional autoencoder (CAE) network for deep feature learn- ing from prestack seismic data, which is more effective than principal component analysis (PCA) in redundancy removing and valid information extraction. Then, using conventional classification or clustering techniques (e.g. K-means or self- organizing maps) on the extracted features, we can achieve seismic facies recognition. We applied our method to the prestack data from physical model and LZB region. The re- sult shows that our approach is superior to the conventionals.