English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

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

Authors
/persons/resource/bghosh

GHOSH,  BINAYAK
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/motagh

Motagh,  M.
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/mahmud

Haghshenas Haghighi,  Mahmud
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/magda88

Stefanova Vassileva,  M.
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/twalter

Walter,  Thomas
2.1 Physics of Earthquakes and Volcanoes, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Maghsudi,  Satareh
External Organizations;

External Ressource
No external resources are shared
Fulltext (public)

5007410.pdf
(Publisher version), 13MB

Supplementary Material (public)
There is no public supplementary material available
Citation

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


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5007410
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.