# Detecting Volcano Deformation in InSAR using Deep learning

arXiv: https://arxiv.org/abs/1803.00380 · HTML: https://mlsanity.com/paper/1803.00380/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2018.

## Built on by (most cited first, 1 of 1)

- [On Board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery](https://mlsanity.com/paper/2106.15281/) (2021)

## Abstract (arXiv)

Globally 800 million people live within 100 km of a volcano and currently 1500 volcanoes are considered active, but half of these have no ground-based monitoring. Alternatively, satellite radar (InSAR) can be employed to observe volcanic ground deformation, which has shown a significant statistical link to eruptions. Modern satellites provide large coverage with high resolution signals, leading to huge amounts of data. The explosion in data has brought major challenges associated with timely dissemination of information and distinguishing volcano deformation patterns from noise, which currently relies on manual inspection. Moreover, volcano observatories still lack expertise to exploit satellite datasets, particularly in developing countries. This paper presents a novel approach to detect volcanic ground deformation automatically from wrapped-phase InSAR images. Convolutional neural networks (CNN) are employed to detect unusual patterns within the radar data.