Automatic detection of volcanic unrest using blind source separation with a minimum spanning tree based stability analysis
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 (...
Published in: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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ftgfzpotsdam:oai:gfzpublic.gfz-potsdam.de:item_5007410 2023-05-15T16:51:02+02:00 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 application/pdf https://gfzpublic.gfz-potsdam.de/pubman/item/item_5007410 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5007410_2/component/file_5007557/5007410.pdf unknown info:eu-repo/semantics/altIdentifier/doi/10.1109/JSTARS.2021.3097895 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5007410 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5007410_2/component/file_5007557/5007410.pdf info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ CC-BY IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing info:eu-repo/semantics/article 2021 ftgfzpotsdam https://doi.org/10.1109/JSTARS.2021.3097895 2022-09-14T05:57:51Z 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. Article in Journal/Newspaper Iceland GFZpublic (German Research Centre for Geosciences, Helmholtz-Zentrum Potsdam) IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 1 1 |
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Open Polar |
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GFZpublic (German Research Centre for Geosciences, Helmholtz-Zentrum Potsdam) |
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ftgfzpotsdam |
language |
unknown |
description |
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. |
format |
Article in Journal/Newspaper |
author |
GHOSH, B. Motagh, M. Haghshenas Haghighi, M. Stefanova Vassileva, M. Walter, T. Maghsudi, S. |
spellingShingle |
GHOSH, B. Motagh, M. Haghshenas Haghighi, M. Stefanova Vassileva, M. Walter, T. Maghsudi, S. Automatic detection of volcanic unrest using blind source separation with a minimum spanning tree based stability analysis |
author_facet |
GHOSH, B. Motagh, M. Haghshenas Haghighi, M. Stefanova Vassileva, M. Walter, T. Maghsudi, S. |
author_sort |
GHOSH, B. |
title |
Automatic detection of volcanic unrest using blind source separation with a minimum spanning tree based stability analysis |
title_short |
Automatic detection of volcanic unrest using blind source separation with a minimum spanning tree based stability analysis |
title_full |
Automatic detection of volcanic unrest using blind source separation with a minimum spanning tree based stability analysis |
title_fullStr |
Automatic detection of volcanic unrest using blind source separation with a minimum spanning tree based stability analysis |
title_full_unstemmed |
Automatic detection of volcanic unrest using blind source separation with a minimum spanning tree based stability analysis |
title_sort |
automatic detection of volcanic unrest using blind source separation with a minimum spanning tree based stability analysis |
publishDate |
2021 |
url |
https://gfzpublic.gfz-potsdam.de/pubman/item/item_5007410 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5007410_2/component/file_5007557/5007410.pdf |
genre |
Iceland |
genre_facet |
Iceland |
op_source |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1109/JSTARS.2021.3097895 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5007410 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5007410_2/component/file_5007557/5007410.pdf |
op_rights |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1109/JSTARS.2021.3097895 |
container_title |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
container_start_page |
1 |
op_container_end_page |
1 |
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1766041150805770240 |