Blind Signal Separation Methods for InSAR: The Potential to Automatically Detect and Monitor Signals of Volcanic Deformation. Issue 11 (16th November 2018)

• Record Type: Journal Article
• Authors: Gaddes, M. E.; Hooper, A.; Bagnardi, M.; Inman, H.; Albino, F.
• Is Part Of: Journal of geophysical research. Volume 123:Issue 11(2018)
• Page Start: 10, 226
• Page End: 10, 251
• Publication Date: 2018-11-16
• Subjects: InSAR -- ICA -- independent component analysis -- volcano monitoring -- machine learning -- Sentinel‐1; Geomagnetism -- Periodicals; Geochemistry -- Periodicals; Geophysics -- Periodicals; Earth sciences -- Periodicals; 551.1
• DOI: 10.1029/2018JB016210
• Languages: English
• ISSNs: 2169-9313

Abstract

Abstract: 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. Plain Language Summary: Through monitoring volcanoes, we can detect signs that they may be likely to erupt in the future, yet many of the world's volcanoes remain unmonitored. However, the latest generation of RADAR satellites produces regular measurements of ground displacement around the majority of the world's volcanoes, and these measurements can be used to detect signs of unrest at many previously unmonitored volcanoes. As the volume of data produced in this undertaking is vast, an automatic detection algorithm is required to detect these signals of interest. In this work, we present methods to separate signals indicative of volcanic unrest from other nuisance signals and to then monitor the geophysically important signals to detect if a volcano has entered a period of unrest. Key Points: Independent component analysis (ICA) is the best of a suite of blind signal separation methods for use with InSAR data Independent component analysis can be used to automatically isolate signals of geophysical interest during the 2015 Wolf Volcano eruption Through monitoring the changes in the signals recovered by ICA, signs of unrest at a volcano can be automatically detected