← Back to Sessions

Bayesian Surface Wave Dispersion Data Inversion of Glaciated Environments

Description: 

We present a probabilistic approach to the inversion of surface wave dispersion data from glacial environments for various material parameters. This is intended to (i) investigate the non-linearity of the problem and the non-uniqueness of the solution, and (ii) properly quantify uncertainties and trade-offs. The method is based on an approximation to a 1-D stratified model of the medium under investigation. We then apply this approach to 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). We use a Bayesian inference approach, implemented with a Hamiltonian Monte Carlo (HMC) algorithm. Exploiting derivative information for efficient sampling of high-dimensional model spaces, HMC approximates the posterior probability density with a collection of sampled models. Applied specifically to multi-mode surface wave dispersion measurements, HMC yields an ensemble of 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. Besides estimating the properties of the medium, and in contrast to deterministic approaches, this method allows statistical studies to be carried out on the recovered models, such as exploiting the parameter statistical moments for uncertainty quantification, or assessing non-linearity by examining trade-offs between parameters.

Session: Applications and Discoveries in Cryoseismology Across Spatial and Temporal Scales [Poster Session]

Type: Poster

Date: 5/2/2024

Presentation Time: 08:00 AM (local time)

Presenting Author: Ariane

Student Presenter: Yes

Invited Presentation: 

Authors