the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Firn Seismic Anisotropy in the North East Greenland Ice Stream from Ambient Noise Surface Waves
Dimitri Zigone
Coen Hofstede
Andreas Fichtner
Joachim Rimpot
Sune Olander Rasmussen
Johannes Freitag
Abstract. We analyse ambient noise seismic data from 23 three-component seismic nodes to study firn velocity structure and seismic anisotropy near the EastGRIP camp along the Northeast Greenland Ice Stream (NEGIS). Using 9-component correlation tensors, we derive dispersion curves of Rayleigh and Love wave group velocities from 3 Hz to 40 Hz. These velocity distributions exhibit anisotropy along and across the flow. To assess these variations, we invert dispersion curves for shear wave velocities (Vsh and Vsv) in the top 150 m of NEGIS using a Markov Chain Monte Carlo approach. The reconstructed1-D shear velocity model reveals radial anisotropy in the firn, with Vsh 12 %–15 % greater than Vsv, peaking at the critical density (550 kg m–3). We combine density data from firn cores drilled in 2016 and 2018 to create a new density parameterisation for NEGIS, serving as a reference for our results. We link seismic anisotropy in the NEGIS to effective and intrinsic causes. Seasonal densification, wind crusts, and melt layers induce effective anisotropy, leading to faster Vsh waves. Changes in firn recrystalisation cause intrinsic anisotropy, altering the Vsv to Vsh ratio. We observe a shallower firn-ice transition across flow (≈ 50 m) compared to along flow (≈ 60 m), suggesting increased firn compaction due to the predominant wind direction and increased deformation towards the shear margin. We demonstrate that short-duration (nine-day minimum), passive, seismic deployments, and noise-based analysis can determine seismic anisotropy in firn, and reveal 2-D firn structure and variability.
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Emma Pearce et al.
Status: open (until 21 Dec 2023)
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CC1: 'Comment on egusphere-2023-2192', Hanbing Ai, 03 Dec 2023
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This paper is interesting and well-written. The authors analyzed ambient noise seismic data and picked the Rayleigh and Love wave group velocities in order to obtain the Vsv and Vsh structures. More interestingly, they obtained the radial anisotropy and analyzed the possible causes of the specific feature. I thus recommend publishing this work after the authors address some issues further:
Comment 1: Please check Line 40; the reference by Pearce et al. (2023) seems to have nothing to do with refraction data.
Comment 2: The core of this paper is the inversion of the picked Rayleigh and Love wave group velocities. As for the MCMC method, have you ever considered using the transdimensional MCMC method to solve the problem of priorly defining the layers? I mean, how to minimize the difference generated by models containing different layers?
Comment 3: The authors did not clearly explain the Vp and density models used for inverison, which makes it hard for readers to validate the results obtained. Table 1 only contains layer thicknesses, numbers, and Vs velocities.
Comment 4: How to convince readers that the retrieved difference between Vsv and Vsh is not caused by inversion uncertainty. I mean, if we perform the MCMC method multiple times, is the difference between Vsv and Vsh still the same or similar?
Comment 5: Please explain why the second-order information in Figures 5b and 5d ill-fitted the picked ones.
Comment 6: I suggest the authors perturb the velocity of Vsh and Vsv within 0~20 and 60~140, like 10%, to see whether the sensitivity exists or not (comparing the calculated group velocities).
Comment 7: I recommend the author calculate the Vp/Vs ratio to gain more insights (if possible).
Thank you.
Citation: https://doi.org/10.5194/egusphere-2023-2192-CC1 -
RC1: 'Comment on egusphere-2023-2192', Anonymous Referee #1, 04 Dec 2023
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Using ambient noise recording, the authors show seismic radial anisotropy of the firn layer in the North East Greenland Ice Stream. They pick the dispersion curves of Rayleigh and Love waves from the computed cross-correlations of ambient noise data and conduct 1D inversions. The difference between inverted Vsv and Vsh indicates the radial anisotropy of the target area. The results are similar to what we found in western Antarctica, although we used different inversion methods and focused on different areas. After reading the manuscript, I have the following concerns:
- The source of ambient noise. As you mentioned, the EastGRIP camp may provide the primary source for ambient noise recording. From Fig. 1c, it seems the incident noise are more parallel to Line 1 other than Line 5. Did you observe the difference between the computed crosscorrelations for Lines 1 and 5?
- Following the first question, the crosscorrelations shown in Fig3 have strong energies at zero lag. You mentioned the possibility of wind. I would like to know whether wind could cause such strong energy and whether this could affect the calculated Rayleigh or Love waves.
- In the inversion, you use group velocity dispersion curves. How about the phase velocity? The picking shown in Fig 5 is misleading as nondispersive body S waves have been picked at high freqs.
- The radial anisotropy below 60m shown in Fig 7 is reaching zero. Is this caused by the reduced sensitivity of surface waves?
- Fig 9 could be moved to the data processing section.
- A typo of 'fig. AA1' in line 225.
- Line 210, 'an averaged 2D velocity' I'm confused since I only saw 1D profiles.
Citation: https://doi.org/10.5194/egusphere-2023-2192-RC1
Emma Pearce et al.
Emma Pearce et al.
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