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Internal structure of ultralow-velocity zones consistent with origin from a basal magma ocean

Nature Geoscience volume 15, pages 79–84 (2022)Cite this article

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Abstract

Seismological observations reveal patches of low-velocity anomalies at the core–mantle boundary known as ultralow-velocity zones. Despite recent advances, their origin and dynamic link to the lowermost mantle remain unclear. Here we employ seismic data analysis and high-resolution geodynamic modelling to study the origin of ultralow-velocity zones beneath the Coral Sea between Australia and New Zealand. The analysis of core-reflected waveforms with rigorous estimation of Bayesian uncertainties shows strong evidence of stratified density increases (~30%) and shear-wave velocity decreases (~50%) within the ultralow-velocity zones. These zones thin on two sides and occur at the edge of the Pacific large low-shear-velocity province. Geodynamic modelling demonstrates that these features are consistent with the presence of compositional heterogeneities within the ultralow-velocity zones that may be caused by the remnants of Earth’s early differentiation. We conclude that small-scale structures that are compositionally distinct from their surroundings reside at the bottom of the mantle without full homogenization, throughout Earth’s history.

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Fig. 1: Illustration of data type and study region.
Fig. 2: Inversion results in terms of S-wave velocity and density.
Fig. 3: High-resolution geodynamic modelling results.
Fig. 4: Schematic representation of the ULVZ, its evolution and the dynamic processes in the mantle.

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Data availability

All the data used in this study are freely available in the GitHub repository: https://github.com/pachhaisurya200/Pachhai_etal_2021_Data. Source data are provided with this paper.

Code availability

Bayesian inversion code is available from S.P. 2D forward computation code can be obtained from M.S.T. The 2D geodynamic modelling code CITCOM is publicly available49,50.

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Acknowledgements

We would like to acknowledge members of the Seismology and Mathematical Group of the Research School of Earth Sciences (RSES), The Australian National University (ANU), for maintaining the seismic networks considered in this study. We would also like to acknowledge the University of Utah Center for High Performance Computing, the Argo cluster at ICTP, and ANU Terrawulf cluster at RSES for computing resources. S.P. would like to acknowledge partial support from an ANU PhD scholarship and NSF grant no. EAR-1723081. M.S.T. acknowledges partial support from NSF grant no. EAR-1723081. M.L. was supported by NSF grants nos. EAR-1849949 and EAR-1855624. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Research School of Earth Sciences, The Australian National University, Canberra, Australian Capital Territory, Australia

    Surya Pachhai & Hrvoje Tkalčić

  2. Department of Geology and Geophysics, University of Utah, Salt Lake City, UT, USA

    Surya Pachhai & Michael S. Thorne

  3. School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA

    Mingming Li

  4. Department of Geoscience, University of Calgary, Calgary, Alberta, Canada

    Jan Dettmer

Authors
  1. Surya Pachhai
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Contributions

S.P. conceived this study during his PhD under the supervision of H.T. and J.D., performed all the data processing, analysis, waveform inversions and figures preparation, and wrote the first version of the manuscript. M.L. performed the geodynamical computation, prepared Fig. 3, Extended Data Fig. 8 and Supplementary Videos 1–3, and wrote the geodynamical method part. All authors discussed the results and shaped the manuscript to its final stage. M.S.T. and S.P. designed the 2D models. M.S.T. implemented the finite-difference PSVaxi code to compute the synthetic ScP waveforms for various 2D geometries.

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Correspondence to Surya Pachhai.

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The authors declare no competing interests.

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Nature Geoscience thanks the anonymous reviewers for their contribution to the peer review of this work.Primary Handling editors: Simon Harold, Stefan Lachowycz

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Extended data

Extended Data Fig. 1 Observed ScP waveforms.

Individual ScP waveforms, after removing the source and path effects, sorted as a function of distance from the center of their respective geographical bins. Cross-correlation coefficients between stacked waveform (cyan color on the bottom panels) and individual waveforms are also shown.

Source data

Extended Data Fig. 2 Stacked ScP-waveforms.

(a) Stack of deconvolved ScP-waveforms (black lines) with stacked P-waveforms (blue line) for different geographical bins analyzed in this study. (b) Example waveforms resulting from the effect of earthquake source complexity.

Source data

Extended Data Fig. 3 Inversion results for bins 1 and 2.

Posterior probability density of interfaces, and P-wave velocity, S-wave velocity and density as a function of depth, and observed and ensemble model predictions in the case of bin (top) 1 and (bottom) 2. The color represents the probability density with each depth interval normalized to unit probability. Dark color represents high probability density with the scale clipped at unity.

Source data

Extended Data Fig. 4 Inversion results for bins 5 and 7.

Same as Extended Data Fig. 3 but in the case of bins 5 (top) and 7 (bottom).

Source data

Extended Data Fig. 5 Inversion results for bins 8 and 10.

Same as Extended Data Fig. 3 but in the case of bins 8 (top) and 10 (bottom).

Source data

Extended Data Fig. 6 Inversion results for bins 3 and 4.

Same as Extended Data Fig. 3 but in the case of bin 3 (top) and 4 (bottom).

Source data

Extended Data Fig. 7 Inversion results for bins 6 and 9.

Same as Extended Data Fig. 3 but in the case of bin 6 (top) and 9 (bottom).

Source data

Extended Data Fig. 8 High-resolution geodynamic modeling results.

Snapshots of the temperature field (left column) and the residual buoyancy field (right column) for the case 1 (A, B), case 2 (C, D) and case 3 (E, F) after 1.4 Gyr. Case 1 is the reference case in which the initial density anomaly of the ULVZ layer ranges from 1.5 to 15%. Case 2 is the same as Case 1, expect that the initial density anomaly of the ULVZ layer ranges from 1.5 to 10%. Case 3 is the same as case 1 except that the ULVZ materials are intrinsically 100 times less viscous than the surrounding mantle. In the residual buoyancy panels, the inserted figures are zoomed in from the locations marked by the black box near the bottom of the model domain. Note that the density anomalies (represented by the residual buoyancy) with the ULVZ patches (zoomed in by the rectangle boxes) are more homogenized in case 2 (D) and case 3 (F) than in case 1 (B), whereas the large-scale structure are more similar in all cases.

Source data

Supplementary information

Supplementary Information

Validity of 1D forward modelling, Supplementary Figs. 1–17 and Tables 1 and 2.

Supplementary Video 1

Movie for case 1: time evolution of (bottom) temperature field, (middle) chemical field and (top) thermochemical field in the case of density increases from 1.5% (top of ULVZ) to 15% (at the CMB).

Supplementary Video 2

Movie for case 2: time evolution of (bottom) temperature field, (middle) chemical field and (top) thermochemical field in the case of density increases from 1.5% (top of ULVZ) to 10% (at the CMB).

Supplementary Video 3

Movie for case 3: time evolution of (bottom) temperature field, (middle) chemical field and (top) thermochemical field in terms of residual buoyancy in the case of density increases from 1.5% (top of ULVZ) to 5% (at the CMB).

Source data

Source Data Fig. 1

Earthquake source parameters including focal mechanisms and S-velocity model for the lowermost mantle (Ritsema et al. 2011).

Source Data Fig. 2

Output of the Bayesian inversions for the data from bins #1 to 10.

Source Data Fig. 3

Geodynamic modelling results for three different cases.

Source Data Extended Data Fig. 1

Individual waveforms for all the events considered in this study.

Source Data Extended Data Fig. 2

Stacked ScP and P wavelets for all the events that are considered in the inversion.

Source Data Extended Data Fig. 3

Bayesian inversion output for bins #1 and 2.

Source Data Extended Data Fig. 4

Bayesian inversion output for bins #5 and 7.

Source Data Extended Data Fig. 5

Bayesian inversion output for bins #8 and 10.

Source Data Extended Data Fig. 6

Bayesian inversion output for bins #3 and 4.

Source Data Extended Data Fig. 7

Bayesian inversion output for bins #6 and 9.

Source Data Extended Data Fig. 8

Geodynamic modelling results for three different cases.

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Pachhai, S., Li, M., Thorne, M.S. et al. Internal structure of ultralow-velocity zones consistent with origin from a basal magma ocean. Nat. Geosci. 15, 79–84 (2022). https://doi.org/10.1038/s41561-021-00871-5

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