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Post-subduction tectonics induced by extension from a lithospheric drip

Nature Geoscience volume 16, pages 646–652 (2023)Cite this article

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Abstract

Subduction termination leads to complex tectonic and geological activity, with the observational record often including clear evidence for exhumation, anomalous magmatism and topographic subsidence, followed by rapid uplift. However, the mechanism(s) driving these responses remain enigmatic and cannot be reconciled with our current understanding of post-subduction tectonics. A prime example of recent subduction termination can be found in northern Borneo (Malaysia), where subduction ceased in the late Miocene (at ~9 Ma). Here we use recently acquired passive seismic data to image, at unprecedented resolution (~35 km), a sub-vertical lithospheric drip, inferred to have developed as a Rayleigh–Taylor gravitational instability from the root of a volcanic arc. We use thermo-mechanical simulations to reconcile these images with time-dependent dynamical processes within the crust and underlying mantle following subduction termination. Our model predictions illustrate how substantial extension from a lithospheric drip can thin the crust in an adjacent orogenic belt, facilitating lower-crustal melting and possible exhumation of sub-continental material, as is observed. These discoveries provide evidence for extension-driven melting of the lower crust, exhumation, core-complex formation and orogeny that also may occur in other areas of recent subduction termination.

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Fig. 1: Geological map of Sabah with seismic stations of nBOSS and MetMalaysia.
Fig. 2: Crustal thickness, lithospheric thickness, anisotropy and P-wave tomographic model.
Fig. 3: Temporal snapshots illustrating the evolution of a lithospheric drip from our simulation.
Fig. 4: Stretching in the Crocker Range and upward deflection of isotherms.
Fig. 5: Viscosity, vertical velocity and adaptive mesh.

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Data availability

Part of the nBOSS dataset is accessible through the IRIS Data Management (https://www.fdsn.org/networks/detail/YC_2018/). Data from the remaining nBOSS stations will be available from February 2024. Data from the Malaysian national seismic network (https://www.fdsn.org/networks/detail/MY/) are restricted but may be obtained by contacting the Malaysian Meteorological Department.

Code availability

The Fluidity computational modelling framework, including source code and documentation, is available from https://fluidityproject.github.io/; the latest release, which was used for the simulations presented herein, has been archived at Zenodo: https://doi.org/10.5281/zenodo.5221157. The source code and manual for FMTOMO are available via http://iearth.edu.au/codes/FMTOMO/.

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Acknowledgements

S.P. acknowledges support from the Natural Environmental Research Council (NERC) Grant NE/R013500/1 and from the European Union’s Horizon 2020 Research and Innovation Program under Marie Skłodowska-Curie Grant Agreement 790203. We thank the TanDEM-X Science Communication Team (German Aerospace Center (DLR) e.V.) for providing TanDEM topographic data. We thank the NERC Geophysical Equipment Facility for loan 1038. Numerical simulations were undertaken on the NCI National Facility in Canberra, Australia, which is supported by the Australian Commonwealth Government. A.G. was funded by an Independent Research Fellowship from the Royal Astronomical Society.

Author information

Authors and Affiliations

  1. Department of Earth Sciences–Bullard Labs, University of Cambridge, Cambridge, UK

    S. Pilia, C. A. Bacon, T. Greenfield & N. Rawlinson

  2. Department of Earth and Environmental Sciences, University of Milan-Bicocca, Milan, Italy

    S. Pilia

  3. College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    S. Pilia

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

    D. R. Davies & S. Ghelichkhan

  5. SE Asia Research Group, Department of Earth Sciences, Royal Holloway University of London, Surrey, UK

    R. Hall

  6. School of Geosciences, University of Aberdeen, Aberdeen, UK

    A. Gilligan & D. G. Cornwell

  7. Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia

    F. Tongkul

  8. Department of Earth Science and Engineering, Imperial College London, London, UK

    S. C. Kramer

  9. Earth and Planets Laboratory, Carnegie Institution for Science, Washington DC, USA

    C. R. Wilson

  10. Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX, USA

    L. Colli

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Contributions

S.P. conceived this study, created the P-wave tomographic model, integrated all observational constraints and made all figures. S.P. performed the numerical simulation with guidance from D.R.D., building on developments by D.R.D., S.C.K. and C.R.W. S.P., D.R.D. and R.H. made the main interpretation. C.A.B. measured azimuthal anisotropy. A.G. created the crustal-thickness map. T.G. created the lithospheric thickness map. S.G. converted the P–T model to seismic velocity. S.P. and L.C. discussed the uplift effects of the drip. S.P., C.A.B., A.G., T.G., F.T., D.G.C. and N.R. participated in the seismic field acquisition. N.R., S.P. and A.G. acquired funding for this study. N.R. and F.T. conceived the nBOSS experiment. S.P. wrote the paper following discussion with, and contributions from, all authors.

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Correspondence to S. Pilia.

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Extended data

Extended Data Fig. 1 nBOSS and MetMalaysia seismic networks.

The nBOSS seismic experiment includes 46 temporary seismic stations (yellow squares), augmented by 28 permanent stations of the seismic monitoring network operated by the Meteorological Department of Malaysia (MetMalaysia, pink squares). Sensors of the nBOSS network deployed in the field are 3-component Güralp CMG-3ESPCDs (18 in total) and 6TDs (28 in total). MetMalaysia data are detected using Streckeisen STS-2 sensors. When the two seismic networks are jointly used (bottom histograms), they make a dense seismic network with an average station separation of 32 km, capable of resolving relatively fine structure below Sabah (for comparison, the USArray has an average station separation of ~70 km). In this study we use all data available from the nBOSS network, which operated between March 2018 and January 2020. The same time window has been used for data generated by MetMalaysia. MK and CR denote the location of Mt Kinabalu and Crocker Range, respectively. Histograms in the lower panels illustrate the station separation of the nBOSS network with and without inclusion of MetMalaysia stations.

Extended Data Fig. 2 P-wave tomographic model.

Horizontal slices taken from the final P-wave tomographic model every 50 km in depth. Black dots at depths of 150 and 200 km show the location of the seismic sensors. Histogram shows the distribution of relative arrival-time residuals for the initial (grey) and solution (aqua green) model.

Extended Data Fig. 3 Resolution tests.

Resolution test based on synthetic structures involving a vertical (profile A to B) and tilted (profile C to D) high-velocity anomaly. Gray dashed line indicates the location of the input synthetic structure. High and low velocity heterogeneities outside the recovered target structures are largely a function of the random noise that is added to the data.

Extended Data Fig. 4 Resolution tests.

Resolution test based on synthetic structures involving three spikes. A, B and C show the location of the spikes in horizontal view (top panels) and vertical view (bottom panels). High and low velocity heterogeneities outside the recovered target structures are largely a function of the random noise that is added to the data.

Extended Data Fig. 5 2-D numerical model setup and initial conditions.

At the top boundary, x-velocities of 4 cm/yr are prescribed to mimic regional plate motion. The y-component is left free and is integrated in time to track free-surface elevation. The computational domain is adjusted continuously to match this elevation through a mesh movement procedure. A zero-slip boundary condition is imposed at the bottom of the model, with open, hydrostatic sides (inflow on left, outflow on right). Temperature boundary conditions are set to 273 K at the surface and 1573 K at the base. The inflow boundary (LHS) is prescribed at a temperature that matches its initial condition, with the outflow boundary left free. Our model includes 4 different materials (labeled in orange): upper-crust, lower-crust, a higher density seed and underlying mantle.

Extended Data Fig. 6 Effects of lower crustal strength, plate motion and buoyancy.

We have undertaken a series of simplified tests to isolate the key factors controlling the dynamics of the model presented in the main paper. For these tests, extension is analyzed via the change in spacing between Lagrangian particles (particle plot), with predictions compared to Fig. 4 of the main manuscript (dashed gray line). We analyze sets of particles located in the lower crust (colored diamonds), with colors from each panel consistent with those shown in the particle plot. Case T1_strong includes a flat crust at 35 km depth with plate motion turned off. We include two sets of equidistant particles to the left and right of the drip. For case T1_strong, the particle plot illustrates that increased lower crustal strength is sufficient to prevent lower-crustal extension in response to motion of the drip – the spacing between particles does not increase substantially as the simulation evolves. This demonstrates that a weak lower crust is a fundamental requirement for drip-induced extension. Case T2_Asym includes thickened crust to the left (L) and right (R) of the drip, with the strength of the lower crust doubled on the right, through an increase in the yield stress (at x ≥ 100 km). The particle plot indicates that, for this case, lower crustal extension is substantially enhanced to the left of the drip relative to the right, demonstrating that regional differences in lower crustal strength (facilitated by crustal-thickness variations to the west of the drip in our main model) play a major role in localizing extension. This is confirmed by Case T2_Sym, in which the two areas of thick crust have consistent strength, comparable to our main model, and extension is symmetric. In Case T3_PM, where plate motion is included through a kinematic surface boundary condition, the observed tilt of the drip is reproduced, with lower crustal extension only slightly modified. Finally, to confirm that the observed extension (and changes in topography) is primarily driven by the drip as opposed to positive buoyancy associated with the region of thicker crust, in Case T4_Buoy we remove the drip and calculate both lower crustal extension and the vertical movement of a particle located in the lower crust (V is vertical; H is horizontal). Predictions from this case demonstrate that only a small fraction of crustal uplift and lower-crustal extension is driven by the positive buoyancy of the thickened crust. Furthermore, the time-dependent extension predicted by all other tests where the drip is present (that is, initial rapid stretching followed by a lower rate of extension) is not predicted in Case T4_Buoy, with extension increasing linearly with time. Taken together, these results strongly support a direct link between the Semporna drip, the evolution of regional topography, and lower-crustal extension beneath the Crocker Range.

Extended Data Fig. 7 Snapshots of viscosity, the second invariant of strain-rate and the dominant deformation mechanism.

Time in Myr from the start of the simulation. Black lines delineate the interface between different materials (upper crust, lower crust, dense seed and upper mantle). Note how the area of thick crust exhibits high strain-rates, evident at t = 0.1 Myr, which localizes yielding and deformation. As the simulation evolves and the drip descends into the underlying mantle, this region remains weak, which facilitates lower-crustal extension. In the underlying mantle, material deforms principally through diffusion creep, aside from regions very close to the drip, where higher strain-rates promote dislocation creep.

Extended Data Fig. 8 Temperature field and conversion to dVp.

Distribution of Temperature at simulation times of (a) 2.0 and (b) 10.0 Myr, respectively. White dashed vertical line in a) shows the location where the 1-D temperature profile plotted in c) was taken. 1573 K isotherm is highlighted at 10 Myr; c) illustrates a 1-D temperature profile extracted from panel a) at model distance 816 km (Crocker Range), compared against a water-saturated solidus curve applicable to lower crustal material30 – the region of melting is highlighted in light pink; d) shows the converted model from P-T to seismic velocity structure as perturbations away from the model’s average. The black dashed line highlights the location of the drip as inferred from the tomographic model shown in Fig. 2d. In panels a), b) and c), black lines delineate the interface between different materials (upper crust, lower crust, dense seed and upper mantle).

Extended Data Fig. 9 Melt fraction associated with lithospheric drip.

Predicted melt fractions, based upon the peridotite melting parameterization of Katz et al. 2003, at a simulation time of 10.0 Myr. Peridotite melting is strongly affected by water content - we have assumed 1000 ppm of water in our analysis, which is plausible in a region recently affected by subduction60. Dark green lines delineate isotherms in K. The drip is outlined by the black contour.

Extended Data Table 1 Physical parameters used in simulation
Full size table

Supplementary information

Supplementary Information

Supplementary Figs. 1–5.

Supplementary Video 1

Density and mantle flow model evolution. Upper panel shows the density field as the model evolves through 10 Myr of the simulation. The density seed is characterized by relatively higher density, illustrated in red. Bottom panel shows the mantle flow (black arrows) and vertical velocities induced by sinking of the gravitational instability through the mantle.

Supplementary Video 2

Viscosity and mesh model evolution. Upper panel shows the viscosity field as the model evolves through 10 Myr of the simulation (red is high viscosity while blue is low). Black lines are temperature contours in Kelvin at 273, 500, 750, 1,000, 1,250 and 1,573, respectively. Bottom panel shows the underlying anisotropic dynamically adaptive computational mesh.

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Pilia, S., Davies, D.R., Hall, R. et al. Post-subduction tectonics induced by extension from a lithospheric drip. Nat. Geosci. 16, 646–652 (2023). https://doi.org/10.1038/s41561-023-01201-7

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