Abstract
ABSTRACTFast 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.
Highlights
The subglacial environment exerts a substantial control over the flow dynamics of glaciers and ice masses (Bell, 2008; Siegert and others, 2018)
Dispersion curves are calculated from the seismic data using common midpoint cross-correlation (CMPCC) gathers (Hayashi and Suzuki, 2004) and the Multichannel Analysis of Surface Waves’ (MASW) method introduced by Park and others (1999)
This paper focuses exclusively on Rayleigh wave dispersion curves derived from active source seismology, with high-frequency sources and shallow depth penetration, MuLTI can be equivalently applied to dispersion curves from passive sources (e.g., Walter and others, 2014; Picotti and others, 2017) as the Geopsy forward modelling code, used in MuLTI, has the capability to model dispersion curves with frequencies
Summary
The subglacial environment exerts a substantial control over the flow dynamics of glaciers and ice masses (Bell, 2008; Siegert and others, 2018). A significant control on ice motion is whether ice is underlain by the hard or soft substrate, and whether the motion is governed by ice/sediment deformation (Hofstede and others, 2018) or sliding (Stearns and van der Veen, 2018) Such compositional variations are typically parameterised in predictive models by assuming a frictional stress coefficient (Christoffersen and others, 2014; Åkesson and others, 2017), recent work by Stearns and Van der Veen (2018) highlights the potentially greater influence of effective basal pressure. The hydrological properties of the subglacial environment, have a profound effect on glacial flow and require proper consideration in ice dynamic modelling (Kulessa and others, 2017; Siegert and others, 2018). Ground penetrating radar (GPR) methods are well suited to characterising englacial properties (e.g., Murray and others, 2007; Young Kim and others, 2010; Booth and others, 2013; Lindbäck and others, 2018), but glacier bed reflectivity and high attenuation within the ice column limits the utility of subglacial radar sampling
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