Copernicus Sentinel-1 to help create global volcano monitoring system

TeSpace-borne technology has long been used to spot the tell-tale signs that can help to predict when volcanoes will reawaken. Now scientists have combined machine learning with more than half-a-million Copernicus Sentinel-1 images, with the aim of forecasting eruptions on a global scale.
Copernicus Sentinel-1 is one of many remote sensing missions monitoring volcanic processes. The C-band SAR instrument it hosts collects observations day and night and in all weather conditions. The images produced can be studied by experts to identify changes in the shape of volcanoes and land.
Building on research has demonstrated the potential of machine learning techniques for spotting volcanic deformation, the scientists of Bristol University used a collection of Copernicus Sentinel-1 images, from 2015 to 2020, to further explore the success and limitations of this research.
A three-step approach was used to analyse the dataset. 1) Copernicus Sentinel-1 SAR images were processed into surface displacement maps – called interferograms – which were grouped together by location, so deformation could be observed over time, generating 592,224 interferograms, highlighting 1084 volcanoes.
2) A pre-existing deep learning algorithm was used to classify the images and flag volcanoes that showed deformations.Given the much greater risk posed by false negatives – which could cause a potential eruption to be missed – a conservative detection threshold was used in the analysis.
3) The researchers used a series of tools to inspect each volcano reported by the deep learning algorithm, to confirm the presence of deformation or volcanic activity. Across the whole dataset, the detection threshold for deformation was 5.9 cm, equivalent to a rate of 1.2 cm per year over the five-year study period.
In addition, the researchers noted the need to develop tailored processing strategies for regions with dense vegetation or snow cover, as well as atmospheric corrections using global weather models to reduce false positives, in future research.
Fabien Albino, remote sensing scientist and co-author of the research, said, “This study is the first to demonstrate the powerful combination of automatically processed satellite data and machine learning on a large global dataset, to detect volcanic deformation.”
The algorithms presented in this analysis have been adapted to run in an open-access web portal that enables volcanologists to search quickly through flagged deformation signals.
“The ultimate goal is to develop a real-time monitoring and warning system that will be used in combination with other sources of information to support evacuations and mitigation efforts.”
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