Abstract
The core mantle boundary (CMB) separates Earth’s liquid iron outer core from the solid but slowly convecting mantle. The detailed structure and dynamics of the mantle within ~300 km of this interface remain enigmatic: it is a complex region, which exhibits thermal, compositional and phase-related heterogeneity, isolated pockets of partial melt and strong variations in seismic velocity and anisotropy. Nonetheless, characterising the structure of this region is crucial to a better understanding of the mantle’s thermo-chemical evolution and the nature of core-mantle interactions. In this study, we examine the heterogeneity spectrum from a recent P-wave tomographic model, which is based upon trans-dimensional and hierarchical Bayesian imaging. Our tomographic technique avoids explicit model parameterization, smoothing and damping. Spectral analyses reveal a multi-scale wavelength content and a power of heterogeneity that is three times larger than previous estimates. Inter alia, the resulting heterogeneity spectrum gives a more complete picture of the lowermost mantle and provides a bridge between the long-wavelength features obtained in global S-wave models and the short-scale dimensions of seismic scatterers. The evidence that we present for strong, multi-scale lowermost mantle heterogeneity has important implications for the nature of lower mantle dynamics and prescribes complex boundary conditions for Earth’s geodynamo.
Highlights
The core mantle boundary (CMB) separates Earth’s liquid iron outer core from the solid but slowly convecting mantle
The evidence that we present for strong, multi-scale lowermost mantle heterogeneity has important implications for the nature of lower mantle dynamics and prescribes complex boundary conditions for Earth’s geodynamo
In our previous study[14], we presented a P-wave tomography model of the lowermost mantle (LMM) and the corresponding uncertainty based on a hierarchical trans-dimensional Bayesian method
Summary
This study uses a recently developed trans-dimensional and hierarchical Bayesian imaging technique described in depth by Young et al.[14]. The key advantages of the trans-dimensional hierarchical approach are that the number and distribution of the model parameters are implicitly controlled by the data and that the standard deviation of the data noise (assumed to have a Gaussian distribution) is treated as an unknown in the inversion. In the areas of poor or non-existent sampling, the partition modelling will result in a slower convergence and less stable velocity values and Voronoi cell shapes, which will lead to high uncertainty (standard deviation). The recovery of a LMM map of P-wave velocity heterogeneity requires over 20,000 CPU hours This is five orders of magnitude greater than the time taken by a traditional, non-linear approach (e.g.11), the improved quality of the results and provision of uncertainty estimates fully justify the additional cost. The immense computational cost of such ensemble inference approaches currently prohibits the inversion for whole-mantle structure
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