Archean tectonics was capable of producing virtually indestructible cratonic mantle lithosphere, but the dominant mechanism of this process remains a topic of considerable discussion. Recent geophysical and petrological studies have refuelled the debate by suggesting that thickening and associated vertical movement of the cratonic mantle lithosphere after its formation are essential ingredients of the cratonization process. Here we present a geodynamical study that focuses on how the thick stable cratonic lithospheric roots can be made in a thermally evolving mantle. Our numerical experiments explore the viability of a cratonization process in which depleted mantle lithosphere grows via lateral compression into a > 200-km thick, stable cratonic root and on what timescales this may happen. Successful scenarios for craton formation, within the bounds of our models, are found to be composed of two stages: an initial phase of tectonic shortening and a later phase of gravitational self-thickening. The initial tectonic shortening of previously depleted mantle material is essential to initiate the cratonization process, while the subsequent gravitational self-thickening contributes to a second thickening phase that is comparable in magnitude to the initial tectonic phase. Our results show that a combination of intrinsic compositional buoyancy of the cratonic root, rapid cooling of the root after shortening, and the long-term secular cooling of the mantle prevents a Rayleigh-Taylor type collapse, and will stabilize the thick cratonic root for future preservation. This two-stage thickening model provides a geodynamically viable cratonization scenario that is consistent with petrological and geophysical constraints.
Abstract The particle‐in‐cell method is generally considered a flexible and robust method to model the geodynamic problems with chemical heterogeneity. However, velocity interpolation from grid points to particle locations is often performed without considering the divergence of the velocity field, which can lead to significant particle dispersion or clustering if those particles move through regions of strong velocity gradients. This may ultimately result in cells void of particles, which, if left untreated, may, in turn, lead to numerical inaccuracies. Here we apply a two‐dimensional conservative velocity interpolation (CVI) scheme to steady state and time‐dependent flow fields with strong velocity gradients (e.g., due to large local viscosity variation) and derive and apply the three‐dimensional equivalent. We show that the introduction of CVI significantly reduces the dispersion and clustering of particles in both steady state and time‐dependent flow problems and maintains a locally steady number of particles, without the need for ad hoc remedies such as very high initial particle densities or reseeding during the calculation. We illustrate that this method provides a significant improvement to particle distributions in common geodynamic modeling problems such as subduction zones or lithosphere‐asthenosphere boundary dynamics.
Through geodynamical modelling, two hypotheses about the craton stability and evolution were
revisited and an important process of cratonization is investigated. Unlike most previous, related
numerical studies, non-Newtonian rheology with composition dependence was used in these
studies, and the rheological parameters are thus directly comparable with laboratory experiment
of mantle. The first hypothesis, that the cratonic lithosphere is “isopycnic”, is found to be not
strictly necessary for craton stability and longevity. The high viscosity of the cratonic litho-
sphere due to compositional effects on the mantle rheology is found to be essential to maintain a
thickness difference between cratonic and non-cratonic lithosphere for over billions of years and
it allows a modest negative buoyancy of the cratonic root, depending on the strengthening factor
due to the compositional effects. The second hypothesis to be tested is that mantle plume im-
pingements cause rapid, significant removal of subcontinental lithosphere. The results presented
in this thesis show that the erosion caused by a plume impact on a continent that is strong
enough to have survived billions of years of Earth’s history is rather limited. A special weaken-
ing mechanism of such highly viscous and buoyant roots is required to reactivate this cratonic
lithosphere and thus cause significant thinning within 10s of Myrs. The fluid/melt-rock interac-
tion during mantle metasomatism is probably the most likely mechanism to modify and weaken
depleted cratonic lithosphere. Therefore, metasomatic weakening is essential for the significant
thinning of subcontinental lithosphere observed, e.g.at North China Craton and Namibia, south-
ern African, no matter whether caused by a plume impact or another tectonic event.
Using the reasonable compositional effects on the buoyancy and rheology of mantle rocks
from the above studies, numerical experiments are performed to study the formation of thick
cratonic lithosphere from a layered, depleted mantle material. In this scenario, substantial tec-
tonic shortening and thickening of previously depleted material seems to be an essential ingre-
dient to initiate the cratonization process. Afterwards, gravitational self-thickening will cause
further thickening. Compositional buoyancy resists Rayleigh-Taylor instability collapse and
stabilizes the thick cratonic root, while the secular cooling also has a stabilizing effect on the
cratonic root by reducing the thermal buoyancy contrast between lithosphere and asthenosphere
and increasing mantle viscosity. The presented numerical results are consistent with the vertical
movement of cratonic peridotite as suggested on petrological grounds.
Pumping tests are very important means for investigating aquifer properties; however, interpreting the data using common analytical solutions become invalid in complex aquifer systems. The paper aims to explore the potential of machine learning methods in retrieving the pumping tests information in a field site in the Democratic Republic of Congo. A newly planned mining site with a pumping test of three pumping wells and 28 observation wells over one month was chosen to analyze the significance of machine learning methods in the pumping test analysis. Widely used machine learning methods, including correlation, cluster, time-series analysis, artificial neural network (ANN), support vector machine (SVR), random forest (RF) method, and linear regression, are all used in this study. Correlation and cluster analyses among wells provide visual pictures of possible hydraulic connections. The pathway with the best permeability ranges from the depth of 250 m to 350 m. Time-series analysis perfectly captured changes of drawdowns within the three pumping wells. The RF method is found to have the higher accuracy and the lower sensitivity to model parameters than ANN and SVR methods. The coupling of the linear regressive model and analytical solutions is applied to estimate hydraulic conductivities. The results found that ML methods can significantly and effectively improve our understanding of pumping tests by revealing inherent information hidden in those tests.
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