Load Love numbers and Green's functions for elastic Earth models PREM, iasp91, ak135, and modified models with refined crustal structure from Crust 2.0

Wang, Hansheng; Xiang, Lu; Jia, Lulu; Jiang, Liming; Wang, Zhiyong; Hu, Bo; Gao, Peng

doi:10.1016/j.cageo.2012.06.022

Cited by 157 publications

(125 citation statements)

References 17 publications

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“…The Green's functions based on PREM‐iso, AK135, PREM‐HB, PREM‐trans, and STW105 are shown in Figure . Due to its thicker crust, the AK135 Green's function differs with respect to PREM‐iso at small distances up to 100 km from the point load, which is in agreement with the conclusions of Wang et al []. The same behavior is observed for PREM‐HB because the crust model of Holder and Bott [] has lower seismic velocities than PREM‐iso.…”

Section: Predicted Otl Displacement Using Elastic Earth Modelsmentioning

confidence: 99%

Ocean tide loading displacements in western Europe: 2. GPS‐observed anelastic dispersion in the asthenosphere

et al. 2015

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GPS‐observed vertical ocean tide loading displacements show in Cornwall, southwest England, and in Brittany, northwest France, discrepancies of 2–3 mm with predicted values based on the isotropic Preliminary Reference Earth Model for the main tidal harmonic M2, yet in central Europe the agreement is better than 0.5 mm. By comparison of ocean tide models and validation with tide gauge observations, we demonstrate that the uncertainties in the former are too small to cause this disagreement. Furthermore, we find that different local models of the crust and different global elastic reference models derived from seismological observations can only reduce the observed discrepancies to 1–2 mm, which still exceeds the GPS observational uncertainty of 0.2–0.4 mm. It is customary to use the elastic properties of the Earth as given by seismic models. Previously, there has been insufficient evidence to determine how to modify these properties during the transformation from seismic to tidal frequencies to account for possible anelastic dispersion in the asthenosphere, and so this effect has been ignored. If we include this effect, then our discrepancies reduce further to 0.2–0.4 mm. This value is of the same order as the sum of the remaining errors due to uncertainties in the ocean tide models and in the GPS observations themselves. This research provides evidence in western Europe of a reduction of around 8–10% of the seismic shear modulus in the asthenosphere at tidal frequencies. In addition, we find that the asthenosphere absorption band frequencies can be represented by a constant quality factor Q.

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Section: Predicted Otl Displacement Using Elastic Earth Modelsmentioning

confidence: 99%

Ocean tide loading displacements in western Europe: 2. GPS‐observed anelastic dispersion in the asthenosphere

et al. 2015

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“…We use the variable transformation method by Wang et al (2012) to compute the two load Love numbers (h n , k n ) for the PREM and MQ-PREM Earth models (Fig. 2).…”

Section: Earth Models and Load Love Numbersmentioning

confidence: 99%

Effects of the Tibetan Plateau Crustal Structure on the Inversion of Water Trend Rates Using Simulated GRACE/GPS Data

Wang¹,

Xiang²,

Wu³

et al. 2013

Terr. Atmos. Ocean. Sci.

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The crustal structure under the Tibetan Plateau is quite different from that given by the commonly used Preliminary Reference Earth Model (PREM, Dziewonski and Anderson 1981). We investigate the effects of such differences on inversion results of water trend rates in the area using simulated GRACE (Gravity Recovery and Climate Experiment) and GPS data. When using simulated GRACE data for the inversion, the effects of crustal differences are negligible, confirming the validity of using PREM for the inversion of water trend rates from current and even follow-on GRACE data. However, when using simulated GPS data, effects of crustal differences are very prominent suggesting that an Earth model with a realistic crustal structure instead of PREM should be used for this case.

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“…The elastic loading Green's functions for models (1) and (2) are provided in Farrell [] and Jentzsch [], respectively; the others are provided in Wang et al . []. Together with the PREM continental‐crust model that we have adopted as a baseline, we analyze the ensemble of these seven sets of modeled vertical displacements due to the same SMB loading.…”

Section: Discussionmentioning

confidence: 99%

“…Among all the Earth models considered, the PREM oceanic‐crust model gives the largest differences from the baseline model with an annual amplitude that is 0.4 mm (10%) smaller. These similarities are because (1) the loading Green's functions of these spherical Earth models differ mostly in the near field (shorter than 100 km from the loads) [ Wang et al ., ], and (2) as will be shown later in this section and Figures and , a significant portion of the SMB loading fields is located in the far field and contributes to the loading displacements at the annual period. We note that the analysis described above does not quantify the effects of lateral variations of crustal structure on the modeled loading displacements.…”

Section: Discussionmentioning

confidence: 99%

Annual variations in GPS‐measured vertical displacements near Upernavik Isstrøm (Greenland) and contributions from surface mass loading

et al. 2017

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In response to present‐day ice mass loss on and near the Greenland Ice Sheet, steady crustal uplifts have been observed from the network of Global Positioning System (GPS) stations mounted on bedrock. In addition to the secular uplift trends, the GPS time series also show prominent annual variability. Here we examine the annual changes of the vertical displacements measured at two GPS stations (SRMP and UPVK) near Upernavik Isstrøm in western Greenland. We model elastic loading displacements due to various surface mass loading including three nonice components: atmospheric pressure, ocean bottom pressure, continental water storage, and one ice component, i.e., surface mass balance (SMB). We find that the contribution from atmospheric pressure changes can explain 46% and 78% of the annual amplitude observed in the GPS verticals at SRMP and UPVK, respectively. We also show that removing the predicted loading displacements due to SMB adversely increases the annual variance of the GPS residuals. However, using the GPS data alone, we cannot identify the exact cause(s) of this discrepancy because the annual loading displacements are sensitive to the SMB changes from over 85% of the ice sheet area. Alternatively, by differencing vertical displacements between the two stations, we find a good agreement between the modeled differential SMB loading displacements and the GPS residuals after removing nonice components. Our study highlights the necessity of correcting for nonice loading contributions in the GPS measurements of crustal deformation to infer ice mass changes in Greenland at annual periods.

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Load Love numbers and Green's functions for elastic Earth models PREM, iasp91, ak135, and modified models with refined crustal structure from Crust 2.0

Cited by 157 publications

References 17 publications

Ocean tide loading displacements in western Europe: 2. GPS‐observed anelastic dispersion in the asthenosphere

Ocean tide loading displacements in western Europe: 2. GPS‐observed anelastic dispersion in the asthenosphere

Effects of the Tibetan Plateau Crustal Structure on the Inversion of Water Trend Rates Using Simulated GRACE/GPS Data

Annual variations in GPS‐measured vertical displacements near Upernavik Isstrøm (Greenland) and contributions from surface mass loading

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