# Self-supervised Contrastive Learning for Volcanic Unrest Detection

arXiv: https://arxiv.org/abs/2202.04030 · HTML: https://mlsanity.com/paper/2202.04030/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [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)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets](https://mlsanity.com/paper/1905.07286/) (2019)

## Built on by (most cited first, 4 of 4)

- [Hephaestus: A large scale multitask dataset towards InSAR understanding](https://mlsanity.com/paper/2204.09435/) (2022)
- [Mitigating Urban-Rural Disparities in Contrastive Representation Learning with Satellite Imagery](https://mlsanity.com/paper/2211.08672/) (2022)
- [Anomaly detection for the identification of volcanic unrest in satellite imagery](https://mlsanity.com/paper/2405.18487/) (2024)
- [Thalia: A Global, Multi-Modal Dataset for Volcanic Activity Monitoring](https://mlsanity.com/paper/2505.17782/) (2025)

## Abstract (arXiv)

Ground deformation measured from Interferometric Synthetic Aperture Radar (InSAR) data is considered a sign of volcanic unrest, statistically linked to a volcanic eruption. Recent studies have shown the potential of using Sentinel-1 InSAR data and supervised deep learning (DL) methods for the detection of volcanic deformation signals, towards global volcanic hazard mitigation. However, detection accuracy is compromised from the lack of labelled data and class imbalance. To overcome this, synthetic data are typically used for finetuning DL models pre-trained on the ImageNet dataset. This approach suffers from poor generalisation on real InSAR data. This letter proposes the use of self-supervised contrastive learning to learn quality visual representations hidden in unlabeled InSAR data. Our approach, based on the SimCLR framework, provides a solution that does not require a specialized architecture nor a large labelled or synthetic dataset. We show that our self-supervised pipeline achieves higher accuracy with respect to the state-of-the-art methods, and shows excellent generalisation even for out-of-distribution test data. Finally, we showcase the effectiveness of our approach for detecting the unrest episodes preceding the recent Icelandic Fagradalsfjall volcanic eruption.