PETROLOGIC AND THERMAL STRUCTURE OF THE UPPER MANTLE BENEATH SOUTH AFRICA IN THE CRETACEOUS

1975 ◽  
pp. 455-466 ◽  
Author(s):  
IAN D. MACGREGOR
Keyword(s):  
South Africa ◽  
Upper Mantle ◽  
Thermal Structure

scholarly journals Effects of upper mantle heterogeneities on lithospheric stress field and dynamic topography

2017 ◽  
Author(s):  
Anthony Osei Tutu ◽  
Bernhard Steinberger ◽  
Stephan V. Sobolev ◽  
Irina Rogozhina ◽  
Anton A. Popov
Keyword(s):  
Heat Flow ◽  
Stress Field ◽  
Upper Mantle ◽  
Western Europe ◽  
Thermal Structure ◽  
Mantle Flow ◽  
Density Structure ◽  
Dynamic Topography ◽  
S Wave ◽  
Lithospheric Stress

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.

Heat flow in the Bushveld Complex, South Africa: implications for upper mantle structure

2017 ◽  
Vol 120 (3) ◽  
pp. 351-370 ◽  
Author(s):  
M.Q.W. Jones
Keyword(s):  
South Africa ◽  
Thermal Conductivity ◽  
Heat Flux ◽  
Heat Flow ◽  
Seismic Tomography ◽  
Upper Mantle ◽  
Bushveld Complex ◽  
Kaapvaal Craton ◽  
Flow Measurements ◽  
Witwatersrand Basin

Abstract Geothermal measurements in South Africa since 1939 have resulted in a good coverage of heat flow observations. The Archaean Kaapvaal Craton, in the central part of South Africa, is the best-studied tectonic domain, with nearly 150 heat flow measurements. The greatest density of heat flow sites is in the Witwatersrand Basin goldfields, where geothermal data are essential for determining refrigeration requirements of deep (up to 4 km) gold mines; the average heat flow is 51 ± 6mWm-2. The Bushveld Complex north of the Witwatersrand Basin is an extensive 2.06 Ga ultramafic-felsic intrusive complex that hosts the world’s largest reserves of platinum. The deepest platinum mines reach ~2 km and the need for thermal information for mine refrigeration engineering has led to the generation of a substantial geothermal database. Nearly 1000 thermal conductivity measurements have been made on rocks constituting the Bushveld Complex, and borehole temperature measurements have been made throughout the Complex. The temperature at maximum rock-breaking depth (~2.5 km) is 70°C, approximately 30°C higher than the temperature at equivalent depth in the Witwatersrand Basin; the thermal gradient in the Bushveld Complex is approximately double that in the Witwatersrand Basin. The main reason for this is the low thermal conductivity of rocks overlying platinum mines. The Bushveld data also resulted in 31 new estimates for the heat flux through the Earth’s crust. The overall average value for the Bushveld, 47 ± 7 mW m-2, is the same, to within statistical error, as the Witwatersrand Basin average. The heat flow for platinum mining areas (45 mW m-2) and the heat flux into the floor of the Witwatersrand Basin (43 mW m-2) are typical of Archaean cratons world-wide. The temperature structure of the Kaapvaal lithosphere calculated from the Witwatersrand geothermal data is essentially the same as that derived from thermobarometric studies of Cretaceous kimberlite xenoliths. Both lines of evidence lead to an estimated heat flux of ~17 mW m-2 for the mantle below the Kaapvaal Craton. The estimated thermal thickness of the Kaapvaal lithosphere (235 km) is similar to that defined on the basis of seismic tomography and magnetotelluric studies. The lithosphere below the Bushveld Complex is not significantly hotter than that below the Witwatersrand Basin. This favours a chemical origin rather than a thermal origin for the upper mantle anomaly below the Bushveld Complex that has been identified by seismic tomography studies and magnetotelluric soundings.

Advances on multiobservable thermochemical tomography for the physical state of the upper mantle

2020 ◽  
Author(s):  
Ilya Fomin ◽  
Juan Afonso
Keyword(s):  
Upper Mantle ◽  
High Probability ◽  
Large Scale ◽  
Physical State ◽  
Thermal Structure ◽  
Rock Properties ◽  
Geophysical Research ◽  
Reduced Order ◽  
Mcmc Algorithms ◽  
Forward Problems

<p>Multiobservable thermochemical tomography (MTT) is a recent computational approach to obtain estimates of the physical state (e.g. temperature distribution, compositional structure and rock properties) for the upper mantle [1]. It allows to jointly invert multiple independent datasets (e.g. gravity, seismic, magnetotelluric) within a thermodynamically-constrained and fully probabilistic framework. Evaluation of the plausibility of different physical states of the mantle with Markov Chain Monte Carlo (MCMC) simulations requires the solution of complex forward problems (e.g. Stokes flow, Maxwell’s equations, etc.) millions of times, making MTT computationally demanding for large-scale inverse problems. Furthermore, the number of parameters in a global study can easily reach several millions, making it increasingly difficult to 1) locate the regions of high probability and 2) sample these regions appropriately.</p><p>In order to overcome these limitations, we have combined and implemented a number of techniques, such as reduced-order modelling and efficient parallelization of both the forward problems and the MCMC algorithms, which dramatically accelerate the solution of the forward problems. Our software, LitMod1D_4INV and LitMod3D_4INV, allow to compute a proposal in less than 1 second, even when solving multiple complex forward problems together. We develop a multi-level parallel MPI driver for a collection of advanced MCMC sampling strategies to locate and sample high-probability regions efficiently. The massive amounts of data generated by large-scale MTT inversions need to be managed efficiently. We output results to open-source freeware formats, such as HDF5, TileDB, designed for big data problems. We emphasize that our methods and approaches are not only useful for MTT, but for any demanding inverse problem.</p><p>In this contribution, we will present applications of our software to complex, large-scale MTT problems and discuss its benefits, limitations and future improvements.</p><p>[1] J.C. Afonso, N. Rawlinson, Y. Yang, D. L. Schutt, A. G. Jones, J. Fullea, W. L. Griffin, 3‐D multiobservable probabilistic inversion for the compositional and thermal structure of the lithosphere and upper mantle: III. Thermochemical tomography in the Western‐Central U.S., Journal of Geophysical Research, 121, doi:10.1002/2016JB013049, 2016</p>

Sign in / Sign up

Export Citation Format

Share Document