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# Instability of the African large low-shear-wave-velocity province due to its low intrinsic density

## Abstract

Seismic observations have revealed two seismic anomalies in the lowermost mantle, one beneath Africa and the other beneath the Pacific Ocean, named large low-shear-wave-velocity provinces. These structures are generally considered to be intrinsically dense thermochemical piles that influence mantle and core processes. However, the controls on their morphology, including their relative height difference and their stability, remain unclear. Here we analyse published global shear-wave tomography models, which show that the African anomaly is about 1,000 km greater in height than the Pacific anomaly. With our numerical simulations, we find that the maximum height a thermochemical pile can reach is more controlled by its density and the surrounding mantle viscosity, and less so by its own viscosity and volume. Comparing these findings suggests that the African anomaly has a relatively lower density and thus may be less stable than the Pacific anomaly, implying the two anomalies have different compositions, dynamics and evolution histories.

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## Data availability

All seismic tomography models are downloaded from the SubMachine59 website. The seismic data and all other source data about geodynamic modelling results presented in this study are available at https://figshare.com/projects/Yuan_Li_2022_NG/129185. The seismic data include 17 global shear-wave models, including TX20112, GyPSuM-S15, SAW642ANb16, SEMUCB-WM117, SEMum18, SGLOBE-rani19, TX201520, SEISGLOB121, SEISGLOB222, HMSL-S0623, PRI-S0524, SP12RTS-S25, SPani-S26, S20RTS27, S362ANI+M28, S40RTS29 and SAVANI30.

## Code availability

The author’s modified 2D Citcom code used in this study is available from https://figshare.com/projects/Yuan_Li_2022_NG/129185. The CitcomCU code is available at https://geodynamics.org/cig/software/citcomcu/.

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## Acknowledgements

We are grateful to the extensive discussions and comments on the manuscript by E. Garnero. We also thank K. Hosseini for SubMachine datasets and Y. Wang and S. Yu for valuable comments. The numerical models were performed on the Agave cluster at Arizona State University. Both Q.Y. and M.L. are supported by National Science Foundation grant numbers EAR-1849949 and EAR-1855624.

## Author information

Authors

### Contributions

M.L. conceived the project and Q.Y. performed all experiments. Both authors analysed the data and wrote the manuscript.

### Corresponding author

Correspondence to Qian Yuan.

## Ethics declarations

### Competing interests

The authors declare no competing interests.

## Peer review

### Peer review information

Nature Geoscience thanks Wei Leng, Neala Creasy and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Louise Hawkins and Stefan Lachowycz, in collaboration with the Nature Geoscience team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Extended data

### Extended Data Fig. 1 Cross-section locations through two LLSVPs used for depth profiles of the averaged Vs anomaly.

A1-A10 and B1-B5 are for African LLSVP while C1-C5 and D1-D13 performed for Pacific LLSVP. A4, B4 and C2, D6 are the four cross-sections that were found bear the maximum height for African and Pacific LLSVP, respectively. They are respectively named as AA´, BB´ and CC´, DD´ in the main text. All section data are downloaded from the SubMachine website59. The 17 global S-wave models are TX20112, GyPSuM-S15, SAW642ANb16, SEMUCB-WM117, SEMum18, SGLOBE-rani19, TX201520, SEISGLOB121, SEISGLOB222, HMSL-S0623, PRI-S0524, SP12RTS-S25, SPani-S26, S20RTS27, S362ANI + M28, S40RTS29, SAVANI30. This figure was generated using GMT software version 6.0.0 (https://www.generic-mapping-tools.org/).

### Extended Data Fig. 2 Depth profiles of the $$\overline{\mathrm{dV}}_{\rm{s}}$$ (gray dashed line) and their gradient (blue lines) at 2 selected vertical cross-section locations through the African LLSVP.

a, c, the gradient of the $$\overline{\mathrm{dV}}_{\rm{s}}$$ as a function of depth for African A4 and B4 vertical cross-sections as shown in Extended Data Fig. 1. b, d, similar to panel a and c, but only the negative values of dVs are used when calculating the $$\overline {\mathrm{dV}}_{\rm{s}}$$. Below the turning point (yellow filled circle) the gradient is mostly positive (shown by the vertical red dotted lines), while above which the gradient is fluctuating around 0 (shown by the vertical black dotted lines). The gradient is defined by the change of $$\overline{\mathrm{dV}}_{\rm{s}}$$ over the change of radius.

### Extended Data Fig. 3 Depth profiles of the $$\overline{\mathrm{dV}}_{\rm{s}}$$ (gray dashed line) and its gradient (blue line) at 2 selected vertical cross-section locations through the Pacific LLSVP.

a, c, the gradient of the $$\overline{\mathrm{dV}}_{\rm{s}}$$ as a function of depth for Pacific C2 and D6 vertical cross-sections as shown in Extended Data Fig. 1. b, d, similar to panel a and c, but only the negative values of dVs are used when calculating the $$\overline{\mathrm{dV}}_{\rm{s}}$$. Below the turning point (yellow filled circle) the gradients are mostly positive (shown by the vertical red dotted lines), while above which the gradient is fluctuating around 0 (shown by the vertical black dotted lines). The gradient is defined by the change of $$\overline{\mathrm{dV}}_{\rm{s}}$$ over the change of radius.

### Extended Data Fig. 4 Depth profiles of the $$\overline{\mathrm{dV}}_{\rm{s}}$$ at 15 vertical cross-section locations through the African LLSVP.

a, the horizontally averaged $$\overline{\mathrm{dV}}_{\rm{s}}$$ as a function of depth for the 10 vertical cross-sections as shown in Extended Data Fig. 1. b, similar to panel a, but only the negative values of dVs are used when calculating the $$\overline{\mathrm{dV}}_{\rm{s}}$$. c, the horizontally averaged $$\overline{\mathrm{dV}}_{\rm{s}}$$ as a function of depth for the 5 vertical cross-sections as shown in Extended Data Fig. 1. d, similar to panel c, but only the negative values of dVs are used when calculating the $$\overline{\mathrm{dV}}_{\rm{s}}$$. The yellow filled circle marks the turning point we defined as the maximum height in main text.

### Extended Data Fig. 5 Depth profiles of the $$\overline{\mathrm{dV}}_{\rm{s}}$$ at 18 vertical cross-section locations through the Pacific LLSVP.

a, the horizontally averaged $$\overline{\mathrm{dV}}_{\rm{s}}$$ as a function of depth for the 5 vertical cross-sections as shown in Extended Data Fig. 1. b, similar to panel a, but only the negative values of dVs are used when calculating the $$\overline{\mathrm{dV}}_{\rm{s}}$$. c, the horizontally averaged $$\overline{\mathrm{dV}}_{\rm{s}}$$ as a function of depth for the 13 vertical cross-sections as shown in Extended Data Fig. 1. d, similar to panel c, but only the negative values of dVs are used when calculating the $$\overline{\mathrm{dV}}_{\rm{s}}$$. The yellow filled circle marks the turning point we defined as the maximum height in main text.

### Extended Data Fig. 6 The effects of model parameters on the height of piles revealed by the field of residual buoyancy (with the horizontal averaged removed).

a, the reference case. From b to f, only one parameter is modified from the reference case, they are respectively the initial volume of pile materials (11%) (b), pile viscosity (30 times higher than the reference run) (c), 25 times higher background mantle viscosity (d), a larger buoyancy number of 1.2 (e), and smaller buoyancy number of 0.6 (f). Green curves show pile edges.

### Extended Data Fig. 7 The effects of model parameters on the height of piles revealed by the deviatoric stress.

All cases here from a-f are the same corresponding to that in Extended Data Fig. 6. The green curves show the pile edges.

### Extended Data Fig. 8 Pile height as a function of pile buoyancy number and background mantle viscosity for 450 individual models.

Gray crosses indicate the cases that the thermochemical piles are not stable.

### Extended Data Fig. 9 The effect of different geometry on pile height.

a and b show the composition and temperature fields respectively for a model of same parameters with the reference case but with aspect ratio of 6. c, Blue solid circles show the height of pile from models with aspect ratio of 1, and orange solid circles show the height of pile from models with aspect ratio of 6. The error bar of each calculation refers to one standard deviation of the maximum heights from different timesteps when the model reached steady state. The composition field in panel a shows pure background mantle materials (Beff = 0), pure pile materials (Beff = 0.8), or a mixture between them (intermediate values).

### Extended Data Fig. 10 The setup and height of piles in 3D models.

(a), snapshot of the compositional field in the 3D reference case whose parameters are the same as the 2D reference case. Only the lowermost 1,000 km of the model domain is shown. (b-f), the laterally averaged composition as a function of depth (represented by the height above the CMB) and time for the 3D reference model (b), and other 3D models with 4% more initial pile volume (c), 100 times higher pile viscosity (d), 20 times higher background mantle viscosity (e), and a smaller buoyancy number of 0.5 (f). The black curves show the contours at average composition of 0.05, which are defined as the top of the thermochemical piles. The yellow curve in (b) are the same contour of a higher resolution case, whose parameters are the same to the 3D reference model.

## Supplementary information

### Supplementary Information

Supplementary Figs. 1–4 and Table 1.

### Supplementary Data 1

Physical parameters and results for all cases in this study.

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Yuan, Q., Li, M. Instability of the African large low-shear-wave-velocity province due to its low intrinsic density. Nat. Geosci. 15, 334–339 (2022). https://doi.org/10.1038/s41561-022-00908-3

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• DOI: https://doi.org/10.1038/s41561-022-00908-3