# Self-Supervised Annotation of Seismic Images using Latent Space Factorization

arXiv: https://arxiv.org/abs/2009.04631 · HTML: https://mlsanity.com/paper/2009.04631/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2020.

## Built on by (most cited first, 3 of 3)

- [Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentation](https://mlsanity.com/paper/2206.08158/) (2022)
- [A Large-scale Benchmark on Geological Fault Delineation Models: Domain Shift, Training Dynamics, Generalizability, Evaluation and Inferential Behavior](https://mlsanity.com/paper/2505.08585/) (2025)
- [Explainable Machine Learning for Hydrocarbon Prospect Risking](https://mlsanity.com/paper/2212.07563/) (2022)

## Abstract (arXiv)

Annotating seismic data is expensive, laborious and subjective due to the number of years required for seismic interpreters to attain proficiency in interpretation. In this paper, we develop a framework to automate annotating pixels of a seismic image to delineate geological structural elements given image-level labels assigned to each image. Our framework factorizes the latent space of a deep encoder-decoder network by projecting the latent space to learned sub-spaces. Using constraints in the pixel space, the seismic image is further factorized to reveal confidence values on pixels associated with the geological element of interest. Details of the annotated image are provided for analysis and qualitative comparison is made with similar frameworks.