# A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets

arXiv: https://arxiv.org/abs/1905.07286 · HTML: https://mlsanity.com/paper/1905.07286/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2019.

## Built on by (most cited first, 13 of 13)

- [Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science](https://mlsanity.com/paper/2303.00652/) (2023)
- [When Geoscience Meets Foundation Models: Towards General Geoscience Artificial Intelligence System](https://mlsanity.com/paper/2309.06799/) (2023)
- [On Board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery](https://mlsanity.com/paper/2106.15281/) (2021)
- [Deep Learning Techniques for Atmospheric Turbulence Removal: A Review](https://mlsanity.com/paper/2409.14587/) (2024)
- [Deep Learning-based Damage Mapping with InSAR Coherence Time Series](https://mlsanity.com/paper/2105.11544/) (2021)
- [Self-supervised Contrastive Learning for Volcanic Unrest Detection](https://mlsanity.com/paper/2202.04030/) (2022)
- [End-to-end LSTM based estimation of volcano event epicenter localization](https://mlsanity.com/paper/2110.14594/) (2021)
- [Learning from Synthetic InSAR with Vision Transformers: The case of volcanic unrest detection](https://mlsanity.com/paper/2201.03016/) (2022)
- [Anomaly detection for the identification of volcanic unrest in satellite imagery](https://mlsanity.com/paper/2405.18487/) (2024)
- [PDIWS: Thermal Imaging Dataset for Person Detection in Intrusion Warning Systems](https://mlsanity.com/paper/2302.13293/) (2023)
- [Thalia: A Global, Multi-Modal Dataset for Volcanic Activity Monitoring](https://mlsanity.com/paper/2505.17782/) (2025)
- [UnwrapDiff: A Conditional Diffusion Model for InSAR Phase Unwrapping](https://mlsanity.com/paper/2512.04749/) (2025)
- [An InSAR Phase Unwrapping Framework for Large-scale and Complex Events](https://mlsanity.com/paper/2603.21378/) (2026)

## Abstract (arXiv)

Satellites enable widespread, regional or global surveillance of volcanoes and can provide the first indication of volcanic unrest or eruption. Here we consider Interferometric Synthetic Aperture Radar (InSAR), which can be employed to detect surface deformation with a strong statistical link to eruption. The ability of machine learning to automatically identify signals of interest in these large InSAR datasets has already been demonstrated, but data-driven techniques, such as convolutional neutral networks (CNN) require balanced training datasets of positive and negative signals to effectively differentiate between real deformation and noise. As only a small proportion of volcanoes are deforming and atmospheric noise is ubiquitous, the use of machine learning for detecting volcanic unrest is more challenging. In this paper, we address this problem using synthetic interferograms to train the AlexNet. The synthetic interferograms are composed of 3 parts: 1) deformation patterns based on a Monte Carlo selection of parameters for analytic forward models, 2) stratified atmospheric effects derived from weather models and 3) turbulent atmospheric effects based on statistical simulations of correlated noise. The AlexNet architecture trained with synthetic data outperforms that trained using real interferograms alone, based on classification accuracy and positive predictive value (PPV). However, the models used to generate the synthetic signals are a simplification of the natural processes, so we retrain the CNN with a combined dataset consisting of synthetic models and selected real examples, achieving a final PPV of 82%. Although applying atmospheric corrections to the entire dataset is computationally expensive, it is relatively simple to apply them to the small subset of positive results. This further improves the detection performance without a significant increase in computational burden.