Anelasticity and thermal structure of the oceanic upper mantle: Temperature calibration with heat flow data

1989 ◽  
Vol 94 (B5) ◽  
pp. 5705 ◽  
Author(s):  
Hiroki Sato ◽  
I. Selwyn Sacks
2019 ◽  
Vol 219 (3) ◽  
pp. 1648-1659 ◽  
Author(s):  
B Mather ◽  
L Moresi ◽  
P Rayner

SUMMARY The variation of temperature in the crust is difficult to quantify due to the sparsity of surface heat flow observations and lack of measurements on the thermal properties of rocks at depth. We examine the degree to which the thermal structure of the crust can be constrained from the Curie depth and surface heat flow data in Southeastern Australia. We cast the inverse problem of heat conduction within a Bayesian framework and derive its adjoint so that we can efficiently find the optimal model that best reproduces the data and prior information on the thermal properties of the crust. Efficiency gains obtained from the adjoint method facilitate a detailed exploration of thermal structure in SE Australia, where we predict high temperatures within Precambrian rocks of 650 °C due to relatively high rates of heat production (0.9–1.4 μW m−3). In contrast, temperatures within dominantly Phanerozoic crust reach only 520 °C at the Moho due to the low rates of heat production in Cambrian mafic volcanics. A combination of the Curie depth and heat flow data is required to constrain the uncertainty of lower crustal temperatures to ±73 °C. We also show that parts of the crust are unconstrained if either data set is omitted from the inversion.


2017 ◽  
Author(s):  
Anthony Osei Tutu ◽  
Bernhard Steinberger ◽  
Stephan V. Sobolev ◽  
Irina Rogozhina ◽  
Anton A. Popov

Abstract. The orientation and tectonic regime of the observed crustal/lithospheric stress field contribute to our knowledge of different deformation processes occurring within the Earth's crust and lithosphere. In this study, we analyze the influence of the thermal and density structure of the upper mantle on the lithospheric stress field and topography. We use a 3D lithosphere-asthenosphere numerical model with power-law rheology, coupled to a spectral mantle flow code at 300 km depth. Our results are validated against the World Stress Map 2016 and the observation-based residual topography. We derive the upper mantle thermal structure from either a heat flow model combined with a sea floor age model (TM1) or a global S-wave velocity model (TM2). We show that lateral density heterogeneities in the upper 300 km have a limited influence on the modeled horizontal stress field as opposed to the resulting dynamic topography that appears more sensitive to such heterogeneities. There is hardly any difference between the stress orientation patterns predicted with and without consideration of the heterogeneities in the upper mantle density structure across North America, Australia, and North Africa. In contrast, we find that the dynamic topography is to a greater extent controlled by the upper mantle density structure. After correction for the chemical depletion of continents, the TM2 model leads to a much better fit with the observed residual topography giving a correlation of 0.51 in continents, but this correction leads to no significant improvement in the resulting lithosphere stresses. In continental regions with abundant heat flow data such as, for instant, Western Europe, TM1 results in relatively a small angular misfits of 18.30° between the modeled and observation-based stress field compared 19.90° resulting from modeled lithosphere stress with s-wave based model TM2.


2020 ◽  
Author(s):  
Sheona Masterton ◽  
Samuel Cheyney ◽  
Chris Green ◽  
Peter Webb

<p>Temperature and heat flow are key parameters for understanding the potential for source rock maturation in sedimentary basins. Knowledge of the thermal structure of the lithosphere in both a regional and local context can provide important constraints for modelling basin evolution through time.</p><p>In recent years, global coverage of heat flow data constraints have enhanced scientific understanding of the thermal state of the lithosphere. However, sample bias and variability in sampling methods continues to be a major obstacle to heat flow-derived isotherm prediction, particularly in frontier areas where data are often sparse or poorly constrained. Consideration and integration of alternative approaches to predict temperature at depth may allow interpolation of surface heat flow in such data poor areas.   </p><p>We have attempted to integrate three independent approaches to modelling temperature with depth. The first approach is based on heat flow observations, in which a 1D steady-state model of the lithosphere is constructed from quality-assessed surface heat flow data, crustal thickness estimates and associated lithospheric thermal properties. The second approach is based on terrestrial (airborne, ground and shipborne) magnetic data, in which the maximum depth of magnetisation within the lithosphere is estimated using a de-fractal method and used as a proxy for Curie temperature depth. The third approach is based on satellite magnetic data and estimates the thickness of the magnetic layer within the lithosphere based on the varying amplitudes of satellite magnetic data, accounting for global variations in crustal magnetisation. Curie temperature depth results from each of these approaches have been integrated into a single global grid, then used to calculate temperature-depth variations through the crust.</p><p>We have evaluated our isotherm predictions by comparing them with temperature-depth control points and undertook qualitative and quantitative analyses of discrepancies that exist between different modelling approaches; this has provided insights into the origin of such discrepancies that can be integrated into our models to generate a better controlled global temperature-depth result.  </p><p>We present details of our methodology and the results of our integrated studies. We demonstrate areas where the independent results are in good agreement, providing vital information for high-level basin screening. We also highlight areas of disagreement and suggest possible causes for these discrepancies and potential resolutions.</p>


2014 ◽  
Vol 35 (4) ◽  
pp. 345-359 ◽  
Author(s):  
V. I. Starostenko ◽  
M. N. Dolmaz ◽  
R. I. Kutas ◽  
O. M. Rusakov ◽  
E. Oksum ◽  
...  

2000 ◽  
Vol 181 (3) ◽  
pp. 395-407 ◽  
Author(s):  
Axel H.E Röhm ◽  
Roel Snieder ◽  
Saskia Goes ◽  
Jeannot Trampert

Solid Earth ◽  
2018 ◽  
Vol 9 (3) ◽  
pp. 649-668 ◽  
Author(s):  
Anthony Osei Tutu ◽  
Bernhard Steinberger ◽  
Stephan V. Sobolev ◽  
Irina Rogozhina ◽  
Anton A. Popov

Abstract. The orientation and tectonic regime of the observed crustal/lithospheric stress field contribute to our knowledge of different deformation processes occurring within the Earth's crust and lithosphere. In this study, we analyze the influence of the thermal and density structure of the upper mantle on the lithospheric stress field and topography. We use a 3-D lithosphere–asthenosphere numerical model with power-law rheology, coupled to a spectral mantle flow code at 300 km depth. Our results are validated against the World Stress Map 2016 (WSM2016) and the observation-based residual topography. We derive the upper mantle thermal structure from either a heat flow model combined with a seafloor age model (TM1) or a global S-wave velocity model (TM2). We show that lateral density heterogeneities in the upper 300 km have a limited influence on the modeled horizontal stress field as opposed to the resulting dynamic topography that appears more sensitive to such heterogeneities. The modeled stress field directions, using only the mantle heterogeneities below 300 km, are not perturbed much when the effects of lithosphere and crust above 300 km are added. In contrast, modeled stress magnitudes and dynamic topography are to a greater extent controlled by the upper mantle density structure. After correction for the chemical depletion of continents, the TM2 model leads to a much better fit with the observed residual topography giving a good correlation of 0.51 in continents, but this correction leads to no significant improvement of the fit between the WSM2016 and the resulting lithosphere stresses. In continental regions with abundant heat flow data, TM1 results in relatively small angular misfits. For example, in western Europe the misfit between the modeled and observation-based stress is 18.3°. Our findings emphasize that the relative contributions coming from shallow and deep mantle dynamic forces are quite different for the lithospheric stress field and dynamic topography.


2020 ◽  
Author(s):  
Zdenek Martinec ◽  
Javier Fullea ◽  
Jakub Velimsky

<p>Conventional methods of seismic tomography, surface topography and gravity data analysis constrain distributions of seismic velocity and density at depth, all depending on temperature and composition of the rocks within the Earth. WINTERC-grav, a new global thermochemical model of the lithosphere-upper mantle constrained by state-of-the-art global waveform tomography, satellite gravity (geoid and gravity anomalies and gradiometric measurements from ESA's GOCE mission), surface elevation and heat flow data has been recently released. WINTERC-grav is based upon an integrated geophysical-petrological approach where all relevant rock physical properties modelled (seismic velocities and density) are computed within a thermodynamically self-consistent framework allowing for a direct parameterization of the temperature and composition variables. In this study, we derive a new three dimensional distribution of the electrical conductivity in the Earth's upper mantle combining WINTERC-grav's thermal and compositional fields along with laboratory experiments constraining the conductivity of mantle minerals and melt. We test the derived conductivity model over oceans by simulating a tidally induced magnetic field. Here, we concentrate on the simulation of M2 tidal magnetic field induced by the ocean M2 tidal flow that is modelled by two different assimilative barotropic models, TPXO8-atlas (Egbert and Erofeeva, 2002) and DEBOT (Ein\v spigel and Martinec, 2017). We compare our synthetic results with the M2 tidal magnetic field estimated from 5 years of Swarm satellite observations and CHAMP satellite data by the comprehensive inversion of Sabaka et al. (2018).</p>


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