Using glacier seismicity for phase velocity measurements and Green's function retrieval

High-melt areas of glaciers and ice sheets foster a rich spectrum of ambient seismicity. These signals not only shed light on source mechanisms (e.g. englacial fracturing, water flow, iceberg detachment, basal motion) but also carry information about seismic wave propagation within glacier ice. Here, we present two approaches to measure and potentially monitor phase velocities of high-frequency seismic waves (≥1 Hz) using naturally occurring glacier seismicity. These two approaches were developed for data recorded by on-ice seasonal seismic networks on the Greenland Ice Sheet and a Swiss Alpine glacier. The Greenland data set consists of continuous seismograms, dominated by long-term tremor-like signals of englacial water flow, whereas the Alpine data were collected in triggered mode producing 1-2 s long records that include fracture events within the ice (‘icequakes'). We use a matched-field processing technique to retrieve frequency-dependent phase velocity measurements for the Greenland data. In principle, this phase dispersion relationship can be inverted for ice sheet thickness and bed properties. For these Greenland data, inversion of the dispersion curve yields a bedrock depth of 541 m, which may be too small by as much as 35 per cent. We suggest that the discrepancy is due to lateral changes in ice sheet depth and bed properties beneath the network, which may cause unaccounted mixing of surface wave modes in the dispersion curve. The Swiss Alpine icequake records, on the other hand, allow for reconstruction of the impulse response between two seismometers. The direct and scattered wave fields from the vast numbers of icequake records (tens of thousands per month) can be used to measure small changes in englacial velocities and thus monitor structural changes within the ic

Seismic noise interferometry reveals transverse drainage configuration beneath the surging Bering Glacier

Subglacial drainage systems are known to critically control ice flows, but their spatial configuration and temporal evolution are poorly constrained due to inaccessibility. Here we report a 12‐year‐long monitoring of the drainage underneath Bering Glacier, Alaska, by correlating ambient noise recorded at two seismic stations on the sides of the glacier. We find that the seismic surface waves traveling across Bering Glacier slowed down by 1–2% during its latest 2008–2011 surge, likely due to the switch of the subglacial drainage from a channelized system to a distributed system. In contrast to current models, the relative amplitude of velocity reductions for Rayleigh and Love waves requires the distributed drainage to be highly anisotropic and aligned perpendicular to the ice flow direction. We infer that the subglacial water flow is mainly through a network of transverse basal crevasses during surges and thus can sustain the high water pressure and ice flow speed

Characterisation of the subglacial environment using geophysical constrained Bayesian inversion techniques

An accurate characterization of the inaccessible subglacial environment is key to accurately modelling the dynamic behaviour of ice sheets and glaciers, crucial for predicting sea-level rise. The composition and water content of subglacial material can be inferred from measurements of shear wave velocity (Vs) and bulk electrical resistivity (R), themselves derived from Rayleigh wave dispersion curves and transient electromagnetic (TEM) soundings. Conventional Rayleigh wave and TEM inversions can suffer from poor resolution and non-uniqueness. In this thesis, I present a novel constrained inversion methodology which applies a Markov chain Monte Carlo implementation of Bayesian inversion to produce probability distributions of geophysical parameters. MuLTI (Multimodal Layered Transdimensional Inversion) is used to derive Vs from Rayleigh wave dispersion curves, and its TEM variant, MuLTI-TEM, for evaluating bulk electrical resistivity. The methodologies can include independent depth constraints, drawn from external data sources (e.g., boreholes or other geophysical data), which significantly improves the resolution compared to conventional unconstrained inversions. Compared to such inversions, synthetic studies suggested that MuLTI reduces the error between the true and best-fit models by a factor of 10, and reduces the vertically averaged spread of the Vs distribution twofold, based on the 95% credible intervals. MuLTI and MuLTI-TEM were applied to derive Vs and R profiles from seismic and TEM electromagnetic data acquired on the terminus of the Norwegian glacier Midtdalsbreen. Three subglacial material classifications were determined: sediment (Vs 1600 m/s, R > 500 Ωm) and weathered/fractured bedrock containing saline water (Vs > 1900 m/s, R < 50 Ωm). These algorithms offer a step-change in our ability to resolve and quantify the uncertainties in subsurface inversions, and show promise for constraining the properties of subglacial aquifers beneath Antarctic ice masses. MuLTI and MuLTITEM have both been made publicly available via GitHub to motivate users, in the cryosphere and other environmental settings, for continued advancement