Large-scale demonstration of machine learning for the detection of volcanic deformation in Sentinel-1 satellite imagery

Juliet Biggs, Nantheera Anantrasirichai, F. Albino, Milan Lazecký, Yasser Maghsoudi

Bulletin of Volcanology 2022 article cited by 31

importance 7/10 partially solved Synthetic Aperture Radar (SAR) Applications and Techniques volcanology / hazard monitoring stage: research

Radar satellites systematically image over 1000 volcanoes, but detecting deformation signals requires manual inspection of huge datasets, delaying timely eruption information.

Timely detection of volcanic deformation can characterise magmatic systems and potentially forecast eruptions, protecting lives.

Machine learning was applied to automatically processed Sentinel-1 satellite images to detect volcanic deformation signals across a global five-year dataset.

~600,000 images of >1000 volcanoes processed (2015-2020); detection threshold 5.9 cm (1.2 cm/year); of 16 most-flagged volcanoes: 5 eruptions, 6 slow deformation, 2 non-volcanic, 3 atmospheric artefacts.

Demonstrates ML can screen global satellite data for volcanic deformation while exposing limitations such as atmospheric artefacts.

Venue Bulletin of Volcanology
Published 2022
Work type article
Authors Juliet Biggs, Nantheera Anantrasirichai, F. Albino, Milan Lazecký, Yasser Maghsoudi
Citations 31 (OpenAlex, at import time)
References 55
Model or method not stated in source
Organization not stated in source
Primary source https://doi.org/10.1007/s00445-022-01608-x
Open access hybrid
Keywords machine learning, volcanic deformation, Sentinel-1, SAR imagery, eruption detection
7 / 10

significant — thousands to millions affected

Abstract quantifies global scope: ~600,000 images of >1000 volcanoes over 5 years; timely deformation detection can characterise magmatic systems and potentially forecast eruptions, protecting lives, though routine use needs tailored processing.

Basis: stated. The source itself states the scale. Stated scale: >1000 volcanoes, ~600,000 Sentinel-1 images, 2015-2020. Method and limits.

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