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  Automatic detection of volcanic unrest using blind source separation with a minimum spanning tree based stability analysis

GHOSH, B., Motagh, M., Haghshenas Haghighi, M., Stefanova Vassileva, M., Walter, T., Maghsudi, S. (2021): Automatic detection of volcanic unrest using blind source separation with a minimum spanning tree based stability analysis. - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 7771-7787.
https://doi.org/10.1109/JSTARS.2021.3097895

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 Creators:
GHOSH, BINAYAK1, Author              
Motagh, M.1, Author              
Haghshenas Haghighi, Mahmud1, Author              
Stefanova Vassileva, M.1, Author              
Walter, Thomas2, Author              
Maghsudi, Satareh3, Author
Affiliations:
11.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146028              
22.1 Physics of Earthquakes and Volcanoes, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146029              
3External Organizations, ou_persistent22              

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 Abstract: Repeated Synthetic Aperture Radar (SAR) acquisitions can be utilized to produce measurements of ground deformations and associated geohazards, such as it can be used to detect signs of volcanic unrest. Existing time series algorithms like Permanent Scatterer (PS) analysis and Small Baseline Subset (SBAS) are computationally demanding and cannot be applied in near real time to detect subtle, transient and precursory deformations. To overcome this problem, we have adapted a minimum spanning tree (MST) based spatial independent component analysis (ICA) method to automatically detect sources related to volcanic unrest from a time series of differential interferograms. For a synthetic dataset, we first utilize the algorithms capability to isolate signals of geophysical interest from atmospheric artifacts, topography and other noise signals, before monitoring the evolution of these signals through time in order to detect the onset of a period of volcanic unrest, in near real time.In this work we first demonstrate our approach on synthetic datasets having different signal strengths and temporal complexities. Second we demonstrate our approach on a couple of real datasets, one acquired in 2017-2019 over the Colima volcano, Mexico, showing the occurrence of previously unrecognized short-term deformation events and the other over Mt. Thorbjorn in Iceland acquired over 2020. This shows the strength of the deep learning application to InSAR data, and highlights that deformation events occurring without eruptions, which may have previously been undetected.

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 Dates: 2021-07-192021
 Publication Status: Finally published
 Pages: -
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 Identifiers: GFZPOF: p4 T3 Restless Earth
DOI: 10.1109/JSTARS.2021.3097895
OATYPE: Gold Open Access
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Title: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Source Genre: Journal, SCI, Scopus, oa ab 2020
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Pages: - Volume / Issue: 14 Sequence Number: - Start / End Page: 7771 - 7787 Identifier: CoNE: https://gfzpublic.gfz-potsdam.de/cone/journals/resource/journals213
Publisher: Institute of Electrical and Electronics Engineers (IEEE)