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

Radar (SAR) satellites systematically acquire imagery that can be used for volcano monitoring, characterising magmatic systems and potentially forecasting eruptions on a global scale. However, exploiting the large dataset is limited by the need for manual inspection, meaning timely dissemination of information is challenging. Here we automatically process ~ 600,000 images of > 1000 volcanoes acquired by the Sentinel-1 satellite in a 5-year period (2015–2020) and use the dataset to demonstrate the applicability and limitations of machine learning for detecting deformation signals. Of the 16 volcanoes flagged most often, 5 experienced eruptions, 6 showed slow deformation, 2 had non-volcanic deformation and 3 had atmospheric artefacts. The detection threshold for the whole dataset is 5.9 cm, equivalent to a rate of 1.2 cm/year over the 5-year study period. We then use the large testing dataset to explore the effects of atmospheric conditions, land cover and signal characteristics on detectability and find that the performance of the machine learning algorithm is primarily limited by the quality of the available data, with poor coherence and slow signals being particularly challenging. The expanding dataset of systematically acquired, processed and flagged images will enable the quantitative analysis of volcanic monitoring signals on an unprecedented scale, but tailored processing will be needed for routine monitoring applications

The 2017 Noneruptive Unrest at the Caldera of Cerro Azul Volcano (Galápagos Islands) Revealed by InSAR Observations and Geodetic Modelling

An unrest event occurred at the Cerro Azul volcano, Galápagos Islands, South America, in March 2017, leading to significant surface deformation on the southern Isabela Island, without eruption or surface rupture. We collected single-look complex synthetic aperture radar (SAR) images sensed by the Sentinel-1A satellite, obtaining eight differential interferograms, of which four showed extensive surface displacement during the co-unrest period. Geodetic data indicated that the unrest continued from 18 March to 25 March, reaching a negative peak displacement of −32.9 cm in the caldera and a positive peak displacement of 41.8 cm on the south-east plain in the line-of-sight direction. A joint magma source deformation model, consisting of a Mogi source below the caldera and a sill source south-east of the caldera, was inverted by the Markov chain Monte Carlo method combined with the Metropolis–Hasting algorithm, acquiring the best fit with the four interferograms. The magma transport mechanism of the event was explained by magma overflowing from the compressive Mogi to the tensile sill source, resulting in the observed “∞”-shaped deformation fields. Additionally, we investigated previous events with eruption rifts and lava lakes in 1979, 1998, and 2008, and proposed a potential hazard of tectonic volcanic activity for further volcanic susceptibility research in the Cerro Azul area