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Conference Paper

Bayesian surface wave dispersion inversion of glaciated environments

Authors

Lanteri,  Ariane
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;

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

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

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

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

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

Zigone,  Dimitri
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

Lanteri, A., Gebraad, L., Zunino, A., Klaasen, S., Jonsdottir, K., Hofstede, C., Eisen, O., Zigone, D., Fichtner, A. (2023): Bayesian surface wave dispersion inversion of glaciated environments, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-1912


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5017627
Abstract
We present a probabilistic approach to the inversion of surface wave dispersion data from glacial environments. This is intended to (i) assess non-linearity and non-uniqueness, and (ii) properly quantify resolution and trade-offs. For this, we use seismic data from Distributed Acoustic Sensing (DAS) experiments deployed on the Vatnajökull ice sheet located on Grímsvötn volcano in Iceland, and the Northeast Greenland Ice Stream (NEGIS). Our method is based on a regularisation-free Bayesian inference approach, implemented using a Hamiltonian Monte Carlo (HMC) algorithm. Exploiting derivative information for efficient sampling of high-dimensional model spaces, HMC approximates the posterior probability densities of all model parameters. Applied specifically to multi-mode surface wave dispersion measurements, HMC yields probabilistic models of 1-D anisotropic stratified media parameterised in terms of the P-wave velocities Vpv and Vph, the S-wave velocities Vsv and Vsh, the anisotropy parameter η, and density ρ. The benefits of this approach, not only from a glaciological perspective, include regularisation-free estimates of firn and ice properties, models that are not a priori biased by the exclusion of all parameters except S-wave speed, and some level of direct access to the vertical density profile.