Detecting Volcano Deformation in InSAR using Deep learning

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 (Biggs, et al., 2014). Modern satellites provide large coverage with high resolution signals, leading to huge amounts of data. For example, the Sentinel-1 satellite allows us to acquire images of each of the world’s volcanoes on a routine basis. It has a repeat cycle of 12 days and acquires data with a 250-km swath at a 5 m by 20 m spatial resolution (single look). This data is generated with greater than 10 TB per day or about 2 PB collected between 2014 and June 2017 (Fernández, et al., 2017). 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.