Gaddes, ME orcid.org/0000-0003-4033-0568, Hooper, A orcid.org/0000-0003-4244-6652 and Bagnardi, M orcid.org/0000-0002-4315-0944 (2019) Using Machine Learning to Automatically Detect Volcanic Unrest in a Time Series of Interferograms. Journal of Geophysical Research: Solid Earth, 124 (11). pp. 12304-12322. ISSN 2169-9356
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019, American Geophysical Union. All Rights Reserved. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | sICA; volcano monitoring; InSAR; Sentinel‐1; ICASAR; FastIC |
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: | 27 Nov 2019 15:01 |
Last Modified: | 13 Mar 2020 01:39 |
Status: | Published |
Publisher: | American Geophysical Union |
Identification Number: | 10.1029/2019jb017519 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:153919 |