# Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentation

arXiv: https://arxiv.org/abs/2206.08158 · HTML: https://mlsanity.com/paper/2206.08158/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 16 Jun 2022.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Keystroke Dynamics as Part of Lifelogging](https://mlsanity.com/paper/2010.10144/) (2020)
- [Positional Contrastive Learning for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2106.09157/) (2021)
- [A Machine Learning Benchmark for Facies Classification](https://mlsanity.com/paper/1901.07659/) (2019)
- [Semi-supervised Sequence Modeling for Elastic Impedance Inversion](https://mlsanity.com/paper/1908.07849/) (2019)
- [The role of visual saliency in the automation of seismic interpretation](https://mlsanity.com/paper/1812.11960/) (2018)
- [Spatiotemporal Modeling of Seismic Images for Acoustic Impedance Estimation](https://mlsanity.com/paper/2006.15472/) (2020)
- [Joint Learning for Seismic Inversion: An Acoustic Impedance Estimation Case Study](https://mlsanity.com/paper/2006.15474/) (2020)
- [Self-Supervised Annotation of Seismic Images using Latent Space Factorization](https://mlsanity.com/paper/2009.04631/) (2020)
- [Self-Supervised Delineation of Geological Structures using Orthogonal Latent Space Projection](https://mlsanity.com/paper/2108.09605/) (2021)
- [A novel attention model for salient structure detection in seismic volumes](https://mlsanity.com/paper/2201.06174/) (2022)

## Built on by (most cited first, 12 of 12)

- [OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics](https://mlsanity.com/paper/2209.11195/) (2022)
- [Clinically Labeled Contrastive Learning for OCT Biomarker Classification](https://mlsanity.com/paper/2305.15154/) (2023)
- [HEX: Hierarchical Emergence Exploitation in Self-Supervised Algorithms](https://mlsanity.com/paper/2410.23200/) (2024)
- [Exploiting the Distortion-Semantic Interaction in Fisheye Data](https://mlsanity.com/paper/2305.00079/) (2023)
- [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)
- [AdaDim: Dimensionality Adaptation for SSL Representational Dynamics](https://mlsanity.com/paper/2505.12576/) (2025)
- [TrajPRed: Trajectory Prediction with Region-based Relation Learning](https://mlsanity.com/paper/2404.06971/) (2024)
- [Taxes Are All You Need: Integration of Taxonomical Hierarchy Relationships into the Contrastive Loss](https://mlsanity.com/paper/2406.06848/) (2024)
- [CRACKS: Crowdsourcing Resources for Analysis and Categorization of Key Subsurface faults](https://mlsanity.com/paper/2408.11185/) (2024)
- [Explainable Machine Learning for Hydrocarbon Prospect Risking](https://mlsanity.com/paper/2212.07563/) (2022)
- [CONSS: Contrastive Learning Approach for Semi-Supervised Seismic Facies Classification](https://mlsanity.com/paper/2210.04776/) (2022)
- [Effective Data Selection for Seismic Interpretation through Disagreement](https://mlsanity.com/paper/2406.05149/) (2024)

## Abstract (arXiv)

In seismic interpretation, pixel-level labels of various rock structures can be time-consuming and expensive to obtain. As a result, there oftentimes exists a non-trivial quantity of unlabeled data that is left unused simply because traditional deep learning methods rely on access to fully labeled volumes. To rectify this problem, contrastive learning approaches have been proposed that use a self-supervised methodology in order to learn useful representations from unlabeled data. However, traditional contrastive learning approaches are based on assumptions from the domain of natural images that do not make use of seismic context. In order to incorporate this context within contrastive learning, we propose a novel positive pair selection strategy based on the position of slices within a seismic volume. We show that the learnt representations from our method out-perform a state of the art contrastive learning methodology in a semantic segmentation task.