In the central part of Fennoscandia, the crust is currently rising, because of the delayed response of the viscous mantle to melting of the Late Pleistocene ice sheet. This process, called Glacial Isostatic Adjustment (GIA), causes a negative anomaly in the present-day static gravity field as isostatic equilibrium has not been reached yet. Several studies have tried to use this anomaly as a constraint on models of GIA, but the uncertainty in crustal and upper mantle structures has not been fully taken into account. Therefore, our aim is to revisit this using improved crustal models and compensation techniques. We find that in contrast with other studies, the effect of crustal anomalies on the gravity field cannot be effectively removed, because of uncertainties in the crustal and upper mantle density models. Our second aim is to estimate the effects on geophysical models, which assume isostatic equilibrium, after correcting the observed gravity field with numerical models for GIA. We show that correcting for GIA in geophysical modelling can give changes of several kilometer in the thickness of structural layers of modeled lithosphere, which is a small but significant correction. Correcting the gravity field for GIA prior to assuming isostatic equilibrium and inferring density anomalies might be relevant in other areas with ongoing postglacial rebound such as North America and the polar regions.
This article reviews a spectral forward gravity field modelling method that was initially designed for topographic/isostatic mass reduction of gravity data. The method transforms 3D spherical density models into gravitational potential fields using a spherical harmonic representation. The binomial series approximation in the approach, which is crucial for its computational efficiency, is examined and an error analysis is performed. It is shown that, this method cannot be used for density layers in crustal and upper mantle regions, because it results in large errors in the modelled potential field. Here, a correction is proposed to mitigate this erroneous behaviour. The improved method is benchmarked with a tesseroid gravity field modelling method and is shown to be accurate within ±4 mGal for a layer representing the Moho density interface, which is below other errors in gravity field studies. After the proposed adjustment the method can be used for the global gravity modelling of the complete Earth's density structure.
The Barents Sea is subject to ongoing postglacial uplift since the melting of the Weichselian ice sheet that covered it. The regional ice sheet thickness history is not well known because there is only data at the periphery due to the locations of Franz Joseph Land, Svalbard, and Novaya Zemlya surrounding this paleo ice sheet. We show that the linear trend in the gravity rate derived from a decade of observations from the Gravity Recovery and Climate Experiment (GRACE) satellite mission can constrain the volume of the ice sheet after correcting for current ice melt, hydrology, and far‐field gravitational effects. Regional ice‐loading models based on new geologically inferred ice margin chronologies show a significantly better fit to the GRACE data than that of ICE‐5G. The regional ice models contain less ice in the Barents Sea than present in ICE‐5G (5–6.3 m equivalent sea level versus 8.5 m), which increases the ongoing difficulty in closing the global sea level budget at the Last Glacial Maximum.
S U M M A R YLithospheric density structure can be constructed from seismic tomography, gravity modelling, or using both data sets. The different approaches have their own uncertainties and limitations. This study aims to characterize and quantify some of the uncertainties in gravity modelling of lithosphere densities. To evaluate the gravity modelling we compare gravity-based and seismic velocity-based approaches to estimating lithosphere densities. In this study, we use a crustal model together with lithospheric isostasy and gravity field observations to estimate lithosphere densities. To quantify the effect of uncertainty in the crustal model, three models are implemented in this study: CRUST1.0, EuCrust-07 and a high-resolution P-wave velocity model of the British Isles and surrounding areas. Different P-wave velocity-to-density conversions are used to study the uncertainty in these conversion methods. The crustal density models are forward modelled into gravity field quantities using a method that is able to produce spherical harmonic coefficients. Deep mantle signal is assumed to be removed by removing spherical harmonic coefficients of degree 0-10 in the observed gravity field. The uncertainty in the resulting lithosphere densities due to the different crustal models is ±110 kg m −3 , which is the largest uncertainty in gravity modelling. Other sources of uncertainty, such as the V P to density conversion (±10 kg m −3 ), long-wavelength truncation (±5 kg m −3 ), choice of reference model (<±20 kg m −3 ) and Lithosphere Asthenosphere Boundary uncertainty (±30 kg m −3 ), proved to be of lesser importance. The resulting lithosphere density solutions are compared to density models based on a shear wave velocity model. The comparison shows that the gravity-based models have an increased lateral resolution compared to the tomographic solutions. However, the density anomalies of the gravity-based models are three times higher. This is mainly due to the high resolution in the gravity field. To account for this, the gravity-based density models are filtered with a spatial Gaussian filter with 200 km half-width, which results in similar density estimates (±35 kg m −3 ) with the tomographic approach. Lastly, the gravity-based density is used to estimate laterally varying conversion factors, which correlate with major tectonic regions. The independent gravity-based solutions could help in identifying different compositional domains in the lithosphere, when compared to the tomographic solutions.
SUMMARY Current seismic tomography models show a complex environment underneath the crust, corroborated by high-precision satellite gravity observations. Both data sets are used to independently explore the density structure of the upper mantle. However, combining these two data sets proves to be challenging. The gravity-data has an inherent insensitivity in the radial direction and seismic tomography has a heterogeneous data acquisition, resulting in smoothed tomography models with de-correlation between different models for the mid-to-small wavelength features. Therefore, this study aims to assess and quantify the effect of regularization on a seismic tomography model by exploiting the high lateral sensitivity of gravity data. Seismic tomography models, SL2013sv, SAVANI, SMEAN2 and S40RTS are compared to a gravity-based density model of the upper mantle. In order to obtain similar density solutions compared to the seismic-derived models, the gravity-based model needs to be smoothed with a Gaussian filter. Different smoothening characteristics are observed for the variety of seismic tomography models, relating to the regularization approach in the inversions. Various S40RTS models with similar seismic data but different regularization settings show that the smoothening effect is stronger with increasing regularization. The type of regularization has a dominant effect on the final tomography solution. To reduce the effect of regularization on the tomography models, an enhancement procedure is proposed. This enhancement should be performed within the spectral domain of the actual resolution of the seismic tomography model. The enhanced seismic tomography models show improved spatial correlation with each other and with the gravity-based model. The variation of the density anomalies have similar peak-to-peak magnitudes and clear correlation to geological structures. The resolvement of the spectral misalignment between tomographic models and gravity-based solutions is the first step in the improvement of multidata inversion studies of the upper mantle and benefit from the advantages in both data sets.
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