The latest generation of synthetic aperture radar satellites produce measurements of ground deformation at the majority of the world's subaerial active volcanoes and can be used to detect signs of volcanic unrest. We present an automatic detection algorithm that uses these data to automatically warn when deformation at a volcano departs from the background. We demonstrate our approach on synthetic data sets and the unrest leading to the 2018 eruption of Sierra Negra (Galapagos). Our algorithm encompasses spatial independent component analysis and uses a significantly improved version of the ICASO algorithm, which we term ICASAR, to robustly perform spatial independent component analysis. We use ICASAR to isolate signals of geophysical interest from atmospheric signals, before monitoring the evolution of these signals through time in order to detect the onset of a period of volcanic unrest.

There are some 1,500 volcanoes with the potential to erupt, but most are not instrumentally monitored. However, routine acquisition by the Sentinel‐1 satellites now fulfils the requirements needed for interferometric synthetic aperture radar (InSAR) to progress from a retrospective analysis tool to one used for near‐real‐time monitoring globally. However, global monitoring produces vast quantities of data, and consequently, an automatic detection algorithm is therefore required that is able to identify signs of new deformation, or changes in rate, in a time series of interferograms. On the basis that much of the signal contained in a time series of interferograms can be considered as a linear mixture of several latent sources, we explore the use of blind source separation methods to address this issue. We consider principal component analysis and independent component analysis (ICA) which have previously been applied to InSAR data and nonnegative matrix factorization which has not. Our systematic analysis of the three methods shows ICA to be best suited for most applications with InSAR data. However, care must be taken in the dimension reduction step of ICA not to remove important smaller magnitude signals. We apply ICA to the 2015 Wolf Volcano eruption (Galapagos Archipelago, Ecuador) and automatically isolate three signals, which are broadly similar to those manually identified in other studies. Finally, we develop a prototype detection algorithm based on ICA to identify the onset of the eruption.

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