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 phaserelated 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 thermochemical evolution and the nature of coremantle interactions. In this study, we examine the heterogeneity spectrum from a recent Pwave tomographic model, which is based upon transdimensional and hierarchical Bayesian imaging. Our tomographic technique avoids explicit model parameterization, smoothing and damping. Spectral analyses reveal a multiscale 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 longwavelength features obtained in global Swave models and the shortscale dimensions of seismic scatterers. The evidence that we present for strong, multiscale lowermost mantle heterogeneity has important implications for the nature of lower mantle dynamics and prescribes complex boundary conditions for Earth’s geodynamo.
Introduction
The lowermost mantle (LMM) is the only region of Earth’s interior that compares to the lithosphere in terms of the strength of seismic heterogeneity. It exhibits thermal, compositional and phaserelated heterogeneity (e.g.^{1,2}), isolated pockets of partial melt (e.g.^{3}) and strong variations in seismic velocity and anisotropy (e.g.^{4}). Determining the spatial distribution of this heterogeneity and its thermochemical nature, is crucial to an improved understanding of key geodynamical processes, including global mantle convection and the associated variations in heat transfer across the CMB (e.g.^{2,5}), the behaviour of the magnetic field (e.g.^{6,7}) and the growth of Earth’s inner core (IC) (e.g.^{8,9}). Although there is increasing evidence to support a heterogeneous LMM in terms of temperature, chemistry, phase and the resulting seismic velocity structure, the debate into the strength and lengthscale of these seismic velocity variations is ongoing. This lack of concordance, in part, arises from the parameterisation and regularisation procedures of typical seismological imaging inversion techniques (e.g.^{10,11,12,13}). Arbitrary model parameterisation, such as blocks or spherical harmonics, prevent the full utilisation of the information content of the data by rigidly enforcing artificial discontinuities, oversmoothing, or over interpreting. An additional issue is the improper handling of data noise. Ad hoc methods of estimating error leave models prone to oversimplification, or worse, overcomplexity. Here, we overcome these limitations by inverting for data noise and model parameters simultaneously and accompany our images of the LMM with maps of the associated standard deviation.
In our previous study^{14}, we presented a Pwave tomography model of the LMM and the corresponding uncertainty based on a hierarchical transdimensional Bayesian method. We build on that here, by examining the model’s heterogeneity spectrum. To address some of the remaining questions regarding LMM heterogeneity, we produce highresolution images of the LMM compressional wave velocity structure using the handpicked data set of PKPabPKIKP, PKPbcPKIKP and PcPP differential traveltimes of Young et al.^{14}. By using waveform crosscorrelation and differential travel times, the biases associated with event mislocation and lateral heterogeneity in the lithosphere are minimised. The inclusion of PcPP data helps to isolate IC structure from structure in the LMM. We use a probabilistic, Bayesian inversion scheme (e.g.^{15}) to invert for LMM structure and obtain a robust representation of the complexity and amplitude of the Pwave velocity heterogeneity, alongside uncertainty estimates (see methods for further details).
To attest to the impressive capabilities of the transdimensional and hierarchical Bayesian inversion scheme, we first design and examine a comprehensive, allembracing synthetic resolution test to demonstrate the recovery of velocity discontinuities, smooth velocity transitions, structures of varying scales and strengths (see Fig. 1A for the synthetic input model). Unlike the resolution test presented in Young et al.^{14}, this new test, although retaining the configuration of heterogeneity, now also includes velocity gradients that are comparable to those expected in the LMM. The test goes far beyond the conventional checkerboard tests that are commonly utilized, as it checks not only the ability of the inversion method to recover the coexistence of various heterogeneity scales in the lowermost mantle, but also the gradients in seismic velocity field that might be present at the base of the mantle at the transition from one type of structure to another. The Bayesian method successfully recovers relative perturbation amplitudes and discontinuities where sufficient ray path coverage exists (Fig. 1B,C). To further illustrate the ability of our tomography scheme to recover structural complexities in the LMM, we perform a test with a hemispherical structure on which we superimpose smaller semicircular features in wellsampled equatorial regions and larger, horizontallypositioned elliptical features at modestlysampled, moderate latitudes (Fig. 2). Although the PKPabPKIKP differential travel time dataset successfully recovers most features, aside from within regions where there is no sampling (elevated standard deviation), it is clear that the addition of PcPP data helps resolve sourcereceiver ambiguity. The recovery of both long and shortscale features is impressive, with the standard deviation map fully in concordance with raypath coverage (Fig. 1C). This test illustrates that image smearing and distortion are minimal and mostly constrained to the Pacific and Antarctica. Despite the fact that Voronoi cells, which underlie the algorithm, have straight edges, curved lines can be fully retrieved after averaging many differentlyorientated straightsided polygons (see methods for further details).
Our results using the observed data (Fig. 3A) demonstrate that the power of heterogeneity in the LMM has a rootmeansquare (RMS) Pwave velocity variation of 0.87%^{14}, which is 2–3 times larger than previous globalscale estimates (e.g.^{10,11,13,16}). The firstorder heterogeneity distribution is generally consistent with other Pwave tomographic models of the lowermost mantle (e.g.^{11,13,16,17}), including an agreement on the presence of fast velocities beneath Central America and East Asia and slow velocities beneath south Africa and the southwest Pacific. However, there are also clear discrepancies between our images and those of other studies. Apart from differences in velocity structure in North America, Australia and Europe, we do not observe a strong spherical harmonic degree 2 component. Such discrepancies could be a result of differing datasets, poorer sampling, the inversion method, and/or data noise. The map of standard deviation (Fig. 3B), unsurprisingly, reveals that the greatest certainty is achieved in areas of best raypath coverage (Fig. 1C). Posterior probability plots of the data noise and the number of cells (Fig. 3C) reveal the estimated noise in each dataset and the number of Voronoi cells to which the inversion converges.
From visual inspection, the lengthscale of heterogeneity ranges from ~600–2400 km (measured at the CMB radius, where 1 angular degree is ~60.7 km). For a more rigorous estimate of the lateral extent of velocity anomalies, we perform a 2D Fast Fourier Transform (FFT) of the velocity pattern and estimate the power spectrum (Fig. 4A,B). The wavelength is given in degrees and is accompanied by the corresponding length scale (in kilometres at the CMB) of heterogeneity. Note that we also provide the results of our synthetic test featured in Fig. 1A,B, which is designed to attest to the ability of the 2D FFT method to retrieve wavelength content (Fig. 4C). We find that the power of heterogeneity in the model steadily increases from a lengthscale of ~350 km and peaks at ~1450 km. However, there is substantial and comparable multiscale heterogeneity, at length scales of ~500–6000 km, which provides a link between the very shortscale features whose presence has been established through scattering experiments (e.g.^{18}) and the long wavelength maps resulting from more traditional tomographic approaches with predetermined parameterizations (e.g.^{16}). The power spectra of LMM Pwave velocity models from Della Mora et al.^{17} and Soldati et al.^{13} (Fig. 4A) are shifted toward longer wavelengths and do not exhibit the significant short wavelength (<10 degree) structural content evident in our transdimensional hierarchical model. This confirms our visual inspection. Our resolution tests give us confidence this conclusion is not hampered by the contribution from the areas with a higher standard deviation.
Soldati et al.^{13} link the CMB topography with geodynamics to predict seismic travel times. To investigate the effect of CMB topography on our results, we compare tomographic models derived from: 1) PKPabPKIKP and 2) PcPP datasets. The two independent datasets result in similar models of harmonic degrees 1 and 2 using the Bayesian hierarchical method of Muir and Tkalčić^{19}, illustrating that large scale CMB topography does not have a dominant impact on travel times of these two datasets (Figure S1). In the case that CMB topography had a strong degree 1 or degree 2 signal dominating the velocity heterogeneity, it would generate PKPabPKIKP and PcPP differential travel times of opposite sign, resulting in negatively correlated velocity anomalies between the two models, which we do not see. We therefore heuristically argue that CMB topography has only a minor influence on our results. Short scale topography on the other hand could still affect the travel time residuals, but a more complete and high quality coverage of the LMM is needed to reach more definitive conclusions.
By allowing the data content to drive the parameterisation and noise assessment and by forgoing smoothing and damping regularisation, we can be confident that the features displayed in our final model are required by the data and are not an artefact of arbitrary modelling and inversion schemes. The absence of shorter wavelength features in previous global models could either be simply a consequence of the truncation of spherical harmonic expansions (e.g.^{20}) or due to the strong smoothing and damping employed by tomographic methods with constant velocity block parameterisation (e.g.^{10,11}).
The longwavelength component of LMM heterogeneity has been confirmed in a number of studies over recent decades (e.g.^{16,21,22}). Whilst localized scattering studies (e.g.^{18,23,24}) indicate the presence of shortscale structure, the vast majority of globalscale models remain confined to the >1000 km realm. Mapping smallscale structures globally is key to understanding the role of the LMM in global geodynamics. In particular, the multiscale heterogeneity wavelength spectrum imaged herein helps to discriminate between two possible endmember scenarios for the nature of LMM dynamics: (i) thermochemical ‘piles’, where recent subduction history focuses chemically dense material into two distinct, discontinuous structures at the mantle’s base (the large low shear wave velocity provinces – LLSVPs  imaged beneath Africa and the Pacific^{1}), which are stabilised through excess chemical density; and (ii) principally thermal heterogeneity, where LLSVPs beneath Africa and the Pacific are attributed to clusters of plumes, which are connected by long linear ridges (e.g.^{2,25}). The dominant degree two structure of previous tomography models is regularly cited as firm evidence in support of the former (e.g.^{26}), implying a key role for chemical heterogeneity in dictating the form of lowermost mantle dynamics. However, the multiscale nature of lowermost mantle heterogeneity imaged herein, which lacks the dominant degree two distributions of these previous studies and, significantly, contains fast seismic velocity anomalies inside LLSVP regions, is more consistent with the latter (see^{27} for a detailed review). It is important to emphasize that a thermally dominated lower mantle has implications for the longterm stability of LLSVPs, their potential to act as longterm geochemical reservoirs, the source characteristics of deepmantle plumes and the origin and morphology of ultralowvelocityzones (ULVZs) (e.g.^{28,29,30}).
In addition to thermal and/or chemical heterogeneity, a third electrostructural mechanism can provide an explanation for possible smallscale seismic wave heterogeneities in the LMM. As iron is the most abundant transitionmetal element in the mantle^{31}, ironbearing minerals should play a role in the geochemistry and geodynamics of the LMM. Therefore, irondoping in LMM materials opens up the possibility of pressureinduced electronic spin transitions in magnesiowüstite, ferromagnesian silicate perovskite and postperovskite phases^{32}, which could induce smallscale heterogeneities such as those imaged herein. Although, the highspin to lowspin transition in the Fe 3d states considerably affect the (Mg_{0.83},Fe_{0.17})O sound velocity by ~15%^{32}, it is reasonable to believe that, at LMM conditions, only a subtle temperaturemodulated velocity jump can be found due to low magnesiowüstite content (20%) and hightemperature conditions. Thus, the aforementioned electrostructural mechanism can possibly explain why the origin of smallscale heterogeneities remains debated and unaddressed in earlier seismic studies.
Strong smallscale structure in the LMM also has implications for Earth’s core. It could explain large differential travel time residuals of PKP wave travel times, possibly offering an alternative to strong IC anisotropy as an explanation (e.g.^{33}). Furthermore, at ~2200 km below the CMB, the liquid outer core (OC) slowly crystalizes into the solid IC at the inner core boundary (ICB). There is increasing evidence from studies of seismic attenuation and velocity that the uppermost inner core (UIC) has more than degree 1 complexity^{34,35}, which could be explained by geodynamical models where spatially variable heat fluxes across the CMB are mapped onto the UIC through a process of thermochemical convection in the OC (e.g.^{8,9}; see^{36} for a detailed review). Multiscale heterogeneity in the LMM would likely create more complex thermochemical convection in the OC, which, in turn, would drive inhomogeneous solidification of the IC, a process that would result in the observed lateral variations in the UIC. Future increases in computational power will enable the application of transdimensional, hierarchical Bayesian tomography to the whole mantle, thereby shedding more light on this intriguing problem.
Methods
This study uses a recently developed transdimensional and hierarchical Bayesian imaging technique described in depth by Young et al.^{14}. The LMM (we tested 250, 300 and 350 km thick layer and found that a 300 km thick layer results in the most robust models and hyperparameters and the fastest convergence rate) is partitioned into a mosaic of Voronoi polygons (all points within a Voronoi polygon are closer to the nucleus of that polygon than to any other nucleus) whose size, location, velocity and number vary throughout the inversion according to the information content of the data (partition modelling). The data used are old and newly collected travel time data sensitive to the LMM and core and collected by waveform crosscorrelation. We avoid using data from South Sandwich Island events, however, as they may be influenced by IC structure or strong mantle heterogeneity related to slabs or fragments^{37}. Upper mantle structure is accounted for by correcting travel times using the mantle model of Della Mora et al.^{17}. The forward method involves 1D ray tracing. For each sourcereceiver path, approximately the same number of equidistant points and their locations along each path sampling the lowermost 300 km of the mantle is determined. The ray path geometry is fixed (it does not change with each new realization of Voronoi cells).
The key advantages of the transdimensional 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 nonexistent 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). Our Bayesian method provides the statistical robustness of Garcia et al.^{38} and the multiscale resolution capabilities of Simons et al.^{12} in addition to introducing a novel approach to handling data noise and model parameterisation. As a result, the required complexity of the solution is inferred from the data itself (e.g.^{39}), rather than from ad hoc uncertainty estimation techniques and arbitrary parameterisations so often utilised by more traditional, linearised inversion techniques. The recovery of a LMM map of Pwave velocity heterogeneity requires over 20,000 CPU hours. Although this is five orders of magnitude greater than the time taken by a traditional, nonlinear approach (e.g.^{11}), the improved quality of the results and provision of uncertainty estimates fully justify the additional cost. Nonetheless, the immense computational cost of such ensemble inference approaches currently prohibits the inversion for wholemantle structure.
Additional Information
How to cite this article: Tkalčić, H. et al. Strong, MultiScale Heterogeneity in Earth,s Lowermost Mantle. Sci. Rep. 5, 18416; doi: 10.1038/srep18416 (2015).
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Acknowledgements
Calculations were performed on the Terrawulf cluster, a computational facility supported through the AuScope Australian Geophysical Observing System (AGOS). AuScope is funded under the National Collaborative Research Infrastructure Strategy (NCRIS) and the Education investment Fund (EIF3) both Australian Commonwealth Government Programs. D. R. D. is funded by an ARC Future Fellowship (FT140101262). M. M. acknowledges financial support by the Spanish Ministry of Economy and Competitiveness (CGL 201341860P) and by the BBVA Foundation (PR14 CMA10). We are thankful to J. Byrne for his assistance with Terrawulf cluster and M. Sambridge and T. Bodin for useful discussions regarding technical aspects of the Bayesian inversion. All figures were made by The Generic Mapping Tools^{40}.
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All five authors significantly contributed to the results presented in this article. H.T. guided the interpretation and graphical representation of the results. M.Y. and H.T. contributed the travel time measurements and the data calculations, tomographic inversions of synthetic and real data and wrote the article. J.M. developed the spherical harmonic analysis code and helped with interpretation and editing. D.R.D. and M.M. helped with interpretation and editing.
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Tkalčić, H., Young, M., Muir, J. et al. Strong, MultiScale Heterogeneity in Earth’s Lowermost Mantle. Sci Rep 5, 18416 (2015). https://doi.org/10.1038/srep18416
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