Research ArticleCLIMATE CHANGE

Monitoring southwest Greenland’s ice sheet melt with ambient seismic noise

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Science Advances  06 May 2016:
Vol. 2, no. 5, e1501538
DOI: 10.1126/sciadv.1501538
  • Fig. 1 Ice mass balance and measured velocity variations.

    (A) Map of the ice mass changes over southwest Greenland in 2012. The stations used in this study are indicated by inverted triangles; the other Greenland stations of the Greenland Ice Sheet Monitoring Network (GLISN) are indicated by red dots. The colored lines between the stations show the dv/v averaged over the summer months June, July, and August (JJA). The black contours show the ice thickness at a 1000-m increment; the dashed black curve shows the contour used to integrate the ice mass changes from GRACE data. (B) Same as (A) for 2013. (C) Time series of the relative velocity variations. (D) Time series of the relative velocity variation uncertainties. The summer months are between dashed vertical lines.

  • Fig. 2 Velocity variation modeling.

    The thick red curve is the ice mass variation corrected from its quadratic trend and filtered in the 4- to 17-month period band. The green curve is the poroelastic modeling of dv/v based on the red curve. The black curve is the result of the viscoelastic modeling. The blue curve is the dv/v measurements averaged over all station pairs and filtered in the 4- to 17-month period, whereas the pale blue dots and the thin pale blue curves are the raw average dv/v measurements and the corresponding average uncertainties, respectively.

  • Fig. 3 Pore pressure diffusion in the Greenland crust through a till layer.

    The straight arrow indicates surface pressure and the wavy arrow indicates pore pressure diffusion; the thickness of the arrows indicates the amplitude of the pressure change. (A) Maximum pressure at Earth’s surface due to snow accumulation. The pore pressure change at depth is delayed by the bedrock and the till layer. The change is felt in the near surface and then starts to be felt at depth. (B) The maximum pore pressure change has reached the depth sampled by the seismic waves, and the relative velocity variation is therefore minimum. Meanwhile, surface pressure decreases as a result of ice melting and atmospheric pressure variations. (C) The surface pressure change reaches its minimum, followed by the pore pressure in the near surface. At depth, the pore pressure continues to decrease, whereas the velocity continues to increase. (D) Surface pressure starts to increase again. At depth, pore pressure is minimum, and relative velocity variation is maximum. Sections are not to scale.

  • Table 1 Parameters used in the dv/v modeling.
    ParameterSymbolValueReference
    Glaciostatic pressurePg1600 PaFrom data
    Ice areaSi6.5 × 1011 m2From data
    Gravitational accelerationg9.81 m/s2
    Pressure field wave numberk2π/(60 km)Jiang et al. (11)
    Depth of investigationz5 kmFrom data
    S-wave velocityVs3300 m/sKumar et al. (56)
    Vp/Vs ratioVp/Vs1.8Kumar et al. (56)
    Upper-crust densityρc2700 kg/m3Schmidt-Aursch and Jokat (57)
    P-wave velocityVp = Vs(Vp/Vs)5940 m/s
    Poisson’s ratioν0.2768
    Young’s modulusE7.5 × 1010 Pa
    Mantle viscosityη1021 Pa⋅s
    Viscoelastic relaxation timeT1011 s
    Lamé’s first parameterλ3.65 × 1010 Pa
    Shear modulusμ2.94 × 1010 Pa
    Murnaghan constantm−2.77 × 1016 PaFrom inversion
    Distance from the icex12.5 km
    Biot’s coefficientα0.7Tsai (34)
    Hydraulic diffusivity of the crustKc0.5 m2/sShapiro et al. (58)
    Angular frequencyω2π/(365 days)
    Till layer thicknesszt2.85 mFrom inversion
    Hydraulic diffusivity of tillKt5 × 10− 6 m2/sIverson et al. (38)

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/2/5/e1501538/DC1

    fig. S1. SNR of the reference correlations.

    fig. S2. Seismic noise spectrograms.

    fig. S3. Characteristics of the analysis window used to measure the velocity variations for each station pair.

    fig. S4. Viscoelastic modeling: Vertical distribution of stress due to the ice sheet load.

    fig. S5. Estimation of zt and m/μ.

    fig. S6. Influence of the number of correlations stacked.

    fig. S7. Influence of the symmetrization of the correlation on the dv/v measurements (ILULI-SFJ).

    fig. S8. Influence of the analysis-window length on the dv/v uncertainties.

    fig. S9. Effect of the analysis-window start time for the pair ILULI-NUUG (60-day stack, 0.1- to 0.3-Hz band, 300-s window).

    fig. S10. Example of doublet measurements for NRS-IVI and comparison with the stretching method.

    fig. S11. Example of doublet measurements for ILULI-SFJ and comparison with the stretching method.

  • Supplementary Materials

    This PDF file includes:

    • fig. S1. SNR of the reference correlations.
    • fig. S2. Seismic noise spectrograms.
    • fig. S3. Characteristics of the analysis window used to measure the velocity variations for each station pair.
    • fig. S4. Viscoelastic modeling: Vertical distribution of stress due to the ice sheet load.
    • fig. S5. Estimation of zt and m/μ.
    • fig. S6. Influence of the number of correlations stacked.
    • fig. S7. Influence of the symmetrization of the correlation on the dv/v measurements (ILULI-SFJ).
    • fig. S8. Influence of the analysis-window length on the dv/v uncertainties.
    • fig. S9. Effect of the analysis-window start time for the pair ILULI-NUUG (60-day stack, 0.1- to 0.3-Hz band, 300-s window).
    • fig. S10. Example of doublet measurements for NRS-IVI and comparison with the stretching method.
    • fig. S11. Example of doublet measurements for ILULI-SFJ and comparison with the stretching method.

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