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Volcano monitoring and modelling using Distributed Acoustic Sensing (DAS) on a subglacial volcano: Grímsvötn, Iceland

Authors

Klaasen,  Sara
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Thrastarson,  Solvi
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Cubuk-Sabuncu,  Yesim
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Jónsdóttir,  Kristín
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Gebraad,  Lars
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Noe,  Sebastian
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Fichtner,  Andreas
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Citation

Klaasen, S., Thrastarson, S., Cubuk-Sabuncu, Y., Jónsdóttir, K., Gebraad, L., Noe, S., Fichtner, A. (2023): Volcano monitoring and modelling using Distributed Acoustic Sensing (DAS) on a subglacial volcano: Grímsvötn, Iceland, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-1385


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5017216
Abstract
We present the results of an experiment with Distributed Acoustic Sensing (DAS) on Grímsvötn in Iceland, and the potential of Full Waveform Inversion with DAS. We deployed a 12 km long fibre-optic cable for one month (May 2021) on Grímsvötn, Iceland’s most active volcano, which is covered by the large Vatnajökull glacier. The cable was trenched 50 cm into the ice, following the caldera rim and ending near the central point of the caldera on top of a subglacial lake. We have discovered previously undetected levels of seismicity using an automated earthquake detection algorithm that is based on image processing techniques. We identified first arrival times with an automated cross-correlation based algorithm, developed specifically for complex and local events recorded with DAS. The first arrival times, combined with a probabilistic interpretation and the Hamiltonian Monte Carlo algorithm, yield event locations, their respective uncertainties, and effective velocities along ray paths, even in the absence of a detailed velocity model. Finally, we complete our initial catalogue with local magnitudes, which reveals nearly 2000 events in total, of which ~1% was detected by the local network. This local microseismicity shows spatio-temporal clusters, with active parts of the caldera fault. We use this catalogue as a starting point for simulations of the complex environment, with the aim of going towards Full Waveform Inversion with DAS data. We show the current challenges and progress towards inverting the data for both subsurface structures, and source characteristics.