On the Green's function emergence from interferometry of seismic wave fields generated in high-melt glaciers: implications for passive imaging and monitoring

Abstract

Abstract. Ambient noise seismology has revolutionized seismic characterization of the Earth's crust from local to global scales. The estimate of Green's function (GF) between two receivers, representing the impulse response of elastic media, can be reconstructed via cross-correlation of the ambient noise seismograms. A homogenized wave field illuminating the propagation medium in all directions is a prerequisite for obtaining an accurate GF. For seismic data recorded on glaciers, this condition imposes strong limitations on GF convergence because of minimal seismic scattering in homogeneous ice and limitations in network coverage. We address this difficulty by investigating three patterns of seismic wave fields: a favorable distribution of icequakes and noise sources recorded on a dense array of 98 sensors on Glacier d'Argentière (France), a dominant noise source constituted by a moulin within a smaller seismic array on the Greenland Ice Sheet, and crevasse-generated scattering at Gornergletscher (Switzerland). In Glacier d'Argentière, surface melt routing through englacial channels produces turbulent water flow, creating sustained ambient seismic sources and thus favorable conditions for GF estimates. Analysis of the cross-correlation functions reveals non-equally distributed noise sources outside and within the recording network. The dense sampling of sensors allows for spatial averaging and accurate GF estimates when stacked on lines of receivers. The averaged GFs contain high-frequency (>30 Hz) direct and refracted P waves in addition to the fundamental mode of dispersive Rayleigh waves above 1 Hz. From seismic velocity measurements, we invert bed properties and depth profiles and map seismic anisotropy, which is likely introduced by crevassing. In Greenland, we employ an advanced preprocessing scheme which includes match-field processing and eigenspectral equalization of the cross spectra to remove the moulin source signature and reduce the effect of inhomogeneous wave fields on the GFs. At Gornergletscher, cross-correlations of icequake coda waves show evidence for homogenized incident directions of the scattered wave field. Optimization of coda correlation windows via a Bayesian inversion based on the GF cross coherency and symmetry further promotes the GF estimate convergence. This study presents new processing schemes on suitable array geometries for passive seismic imaging and monitoring of glaciers and ice sheets.

Stick‐Slip Tremor Beneath an Alpine Glacier

“…The nondetection of large-scale ice flow anomalies furthermore suggests that our stick-slip tremor is a local phenomenon of a spatially limited bed region ("sticky spot"), where strain is accumulated and released in the form of successive stick-slip failure. To test the hypothesis that the tremor episode investigated here was not only a fortuitous observation, analysis of more tremor signals is needed, ideally using networks with more sensors and larger apertures (Sergeant et al, 2020). Alternatively, deep borehole seismometers installed in the vicinity of the glacier bed may contain records of stick-slip tremors, whose signals are too weak to be detected at the surface.…”

Passive seismic recording of cryoseisms in Adventdalen, Svalbard

“…However, the low-velocity zone may also indicate the presence of unfrozen permafrost due to elevated salinity, recalling that permafrost is defined simply as ground that remains below 0 • C over at least 2 consecutive years, but does not imply that the ground is in fact frozen. This interpretation is supported by the high Poisson ratio of the lower layer, since according to Skvortsov et al (2014) a Poisson ratio of 0.45-0.46 represents a thresh-Figure 16. Spring field campaign best-qualitative-fit theoretical dispersion curves, based on a simple three-layer horizontal model (see Table 1).…”

Passive seismic recording of cryoseisms in Adventdalen, Svalbard

“…As a result we find it more effective to isolate and analyse specific transient microseismic events rather than attempting to recover the Green's function from ambient noise cross correlations as, e.g., Sergeant et al (2020) have done for passive 140 recordings on glaciers. The periodic microseismic signals are isolated from background random noise based on permutation entropy, a nonlinear statistical measure of randomness in a time series (Bandt and Pompe, 2002), that produces local minima for coherent signals embedded in noise.…”