Subglacial sediment distribution from constrained seismic inversion, using MuLTI software: examples from Midtdalsbreen, Norway

Abstract

Fast ice flow is associated with the deformation of subglacial sediment. Seismic shear velocities, Vs, increase with the rigidity of material and hence can be used to distinguish soft sediment from hard bedrock substrates. Depth profiles of Vs can be obtained from inversions of Rayleigh wave dispersion curves, from passive or active-sources, but these can be highly ambiguous and lack depth sensitivity. Our novel Bayesian transdimensional algorithm, MuLTI, circumvents these issues by adding independent depth constraints to the inversion, also allowing comprehensive uncertainty analysis. We apply MuLTI to the inversion of a Rayleigh wave dataset, acquired using active-source (Multichannel Analysis of Surface Waves) techniques, to characterise sediment distribution beneath the frontal margin of Midtdalsbreen, an outlet of Norway's Hardangerjøkulen ice cap. Ice thickness (0-20 m) is constrained using co-located GPR data. Outputs from MuLTI suggest that partly-frozen sediment (Vs 500-1000 m s −1 ), overlying bedrock (Vs 2000-2500 m s −1 ), is present in patches with a thickness of ∼4 m, although this approaches the resolvable limit of our Rayleigh wave frequencies (14-100 Hz). Uncertainties immediately beneath the glacier bed are <280 m s −1 , implying that MuLTI cannot only distinguish bedrock and sediment substrates but does so with an accuracy sufficient for resolving variations in sediment properties.

Characterisation of subglacial water using a constrained transdimensional Bayesian Time Domain Electromagnetic Inversion

“…Finally, we show a combined 3D interpretation of previous shear wave inversions (Killingbeck et al, 2019) and results output from MuLTI-TEM, and suggest the development of a joint resistivity-Vs depth-constrained inversion strategy.…”

“…GPR, seismic and TEM surveys were performed around and over the glacier front ( substrate of unknown subglacial material. This layered interpretation is based on the interpretation of the GPR dataset, which also suggest that the snow and ice layers show little variation in any of lines A, B and C. Killingbeck et al (2019) used MuLTI to jointly interpret the seismic and GPR data, defining regions of partially frozen sediment and hard bedrock based on subglacial shear wave velocities (Vs < 1000 m/s for the former, > 2000 m/s for the latter. Fig.…”

“…The initial interpretation of the seismic data was presented in Killingbeck et al (2019). This study excluded certain phase velocities on the grounds that they were too high (Fig.A6); but this merit re-evaluation when compared with the colocated observation of high resistivity.…”

“…This method is adapted from a transdimensional Bayesian framework, termed 'MuLTI' and described in Killingbeck et al (2018), originally applied to characterise subglacial sediment distribution from seismic surface wave data (Killingbeck et al, 2019). Here, we explore a similar concept for the TEM method.…”

“…Since the glacier bed represents a transition in both dielectric constant and electrical resistivity, the GPR depth constraint can be used directly in the TEM inversion. Recent results from Killingbeck et al (2019), interpret the Midtdalsbreen subsurface, from seismic shear wave velocity (Vs) profiles, as local accumulations of soft material (partially frozen glacial till) and permafrost overlying bedrock.…”