Gaddes, M orcid.org/0000-0003-4033-0568 (2018) Blind signal separation methods for InSAR: The potential to automatically detect and monitor signals of volcanic deformation. Journal of Geophysical Research: Solid Earth, 123 (11). pp. 10226-10251. ISSN 2169-9356
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018. American Geophysical Union. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | insar; ica; Independent component analysis; Volcano monitoring; Machine learning; Sentinel-1 |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Inst of Geophysics and Tectonics (IGT) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Oct 2018 10:25 |
Last Modified: | 01 May 2019 00:41 |
Status: | Published |
Publisher: | Wiley-Blackwell |
Identification Number: | 10.1029/2018JB016210 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:137894 |