Comparing gravity-based to seismic-derived lithosphere densities: a case study of the British Isles and surrounding areas

Abstract

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.

Integrating Gravity and Surface Elevation With Magnetic Data: Mapping the Curie Temperature Beneath the British Isles and Surrounding Areas

“…The general pattern is dominated by dense material beneath S-E Great Britain and the British Atlantic margin, and a low-density anomaly in Scotland, Northern Ireland and the N-W offshore domain. This feature is also present in some of the models in the gravity-tomography study of Root et al (2017). The lithospheric structure in Ireland in our M1_s model follows a pattern similar to that in Fullea et al (2014), with a moderate thinning from S to N. Elastic thickness maps (derived from thermal and seismic data at European scale) reflecting the strength of the lithosphere are rather diverse in the British Isles (e.g., Cloetingh et al, 2005;Tesauro et al, 2009a,b).…”

“…These authors modeled a moderate lithospheric thickening in the western Irish margin aligned with the ISZ. More recently Root et al (2017) have studied the mantle density structure of the British Isles and surrounding areas combining gravity data and seismic tomography models (e.g., Schaeffer and Lebedev, 2013) utilizing different crustal models: CRUST1.0 (Laske et al, 2013), EuCRUST-07 (Tesauro et al, 2008) and Kelly et al (2007).…”

“…One of the main limitations of gravity data inversion/forward modeling is non-uniqueness. One of the strategies to alleviate such a problem is to simultaneously model various gravity data (e.g., gravity and geoid anomalies) and/or other geophysical datasets with complementary sensitivity (e.g., elevation and heat flow data; Zeyen and Fernàndez, 1994;Torne et al, 2000;Fullea et al, 2006Fullea et al, , 2007, or gravity and seismic tomography data (e.g., Root et al, 2017). A more complex way is to jointly model or to invert gravity and other data sets within an integrated geophysicalpetrological framework where the parameter space is redefined in terms of temperature and composition of rocks (e.g., Khan et al, 2007;Afonso et al, 2008;Fullea et al, 2009).…”

Practical Tips for 3D Regional Gravity Inversion

“…Consequently, this means that the gravitational effect of the mantle density distribution fromẑ − V to z Max can be predicted with an accuracy (1σ) better than 1.5 mGal (for the current scenario). Since Root et al [29] showed that in general, available tomographic models are filtered with a 200 km half width Gaussian filter, we evaluate the effect of the presence of high frequencies in the lithosphere by computing the gravitational signal due to random density anomalies with correlation length in the x, y directions of 50 km, an amplitude of 100 kg m −3 and a fixed thickness of 5000 m. It turns out that this perturbation to the initial model produces effects of the order of few tenths of a milligal which are therefore negligible.…”

Calibration of a multi-anvil high-pressure apparatus to simulate planetary interior conditions

“…[9,10]). Interior models for other planets and moons in the solar system are constrained by remote sensing and geodesic data obtained by space missions (e.g., [11][12][13][14]). …”