Mantle viscosity plays a key role in the Earth’s internal dynamics and thermal history. Geophysical inferences of the viscosity structure, however, have shown large variability depending on the types of observables used or the assumptions imposed1,2,3. Here, we study the mantle viscosity structure by using the postseismic deformation following a deep (approximately 560 km) earthquake located near the bottom of the upper mantle. We apply independent component analysis4 to geodetic time series to successfully detect and extract the postseismic deformation induced by the moment magnitude 8.2, 2018 Fiji earthquake. To search for the viscosity structure that can explain the detected signal, we perform forward viscoelastic relaxation modelling5,6 with a range of viscosity structures. We find that our observation requires a relatively thin (approximately 100 km), low-viscosity (1017 to 1018 Pa s) layer at the bottom of the mantle transition zone. Such a weak zone could explain the slab flattening7 and orphaning8 observed in numerous subduction zones, which are otherwise challenging to explain in the whole mantle convection regime. The low-viscosity layer may result from superplasticity9 induced by the postspinel transition, weak CaSiO3 perovskite10, high water content11 or dehydration melting12.
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GNSS data were obtained from the Nevada Geodetic Lab (geodesy.unr.edu). We also provide the detected postseismic signal, for example, displacements and time evolution, as source data for Fig. 1. Source data are provided with this paper.
The forward modelling code RELAX is available at https://geodynamics.org/cig/software/relax/. The code for the ICA has been made available33 on the Zenodo repository, https://doi.org/10.5281/zenodo.4322548.
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We thank M. Gurnis, H. Kanamori and R. Bürgmann for useful discussions. This work was partially support by the National Science Foundation grant NSF EAR 2142152.
The authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 Observed (black arrows, large circles) and predicted (green arrows, small circles) coseismic deformation.
Same as Fig. 4a, except coseismic displacements are plotted. Error ellipses (grey circles; 95% confidence intervals) are obtained from trajectory modelling.
Extended Data Fig. 2 Predicted postseismic deformation for weak MTZ BH with two different Maxwell times.
a, The same as the right panel of Fig. 2f. b, The same as a, but with weak MTZ BH (Maxwell time of 1.25 years).
Extended Data Fig. 3 Postseismic deformation predicted by different models (MTZ BH with 0.63-year Maxwell time and MTZ B65 with 0.05-year Maxwell time) compared against the data.
a–b, The same as the Fig. 4a,b but with increased viscosity MTZ BH of 0.63-year Maxwell time. c–d, The same as a–b, except with reduced viscosity MTZ B65 of 0.05-year Maxwell time.
Extended Data Fig. 4 Postseismic deformation predicted by our preferred model in presence of a weak asthenosphere.
a–b, The same as the Fig. 4a,b but with a weak asthenosphere of 0.63-year Maxwell time. c–d, The same as a–b, but with a weak asthenosphere of 2.5-year Maxwell time. In each case, the displacements are close to the linear summation of the displacements resulting from modeling the weak asthenosphere and the weak base of MTZ separately. Horizontal displacements are in similar directions, and add constructively, resulting in 10 to 20 % larger displacements at the sites close to the earthquake. On the other hand, due to the uplift in the west of the epicentre and west of the trench produced by the weak asthenosphere, opposite from those produced by weak base of MTZ (Fig. 2), the vertical displacements tend to add destructively in stations in Fiji and Tonga islands (such as LAUT and TONG). This decreases (worsens) the overall fit to the observation by about 14 and 6 %. These results suggest that, in order to explain the observed subsidence around the epicenter and the trench in presence of weak asthenosphere, the weak zone at the base of MTZ is even more strongly required.
Extended Data Fig. 5 Raw and pre-processed GNSS data of station LAUT.
a–c, Raw GNSS data of east, north, and vertical components with uncertainties (blue error bars; one standard deviation). Magenta and green dashed lines denote the timings of steps associated with instrument issues and coseismic displacements, respectively. Solid orange line represents the best-fitting trajectory model. d–f, The same as a–c, except for the timeseries are pre-processed, i.e., linear trend and offsets derived from the trajectory model removed from raw data (a–c).
Extended Data Fig. 6 Independent components other than the postseismic component, and their spatial distributions.
a, The time evolution (top) and the spatial distribution of the most significant seasonal deformation component, shown in the same manner as in Fig. 1, where error ellipses (one standard deviation) are estimated in the same way as those of postseismic displacements. b–d, The same as a, but for the 2nd, 3rd, and 4th most significant seasonal components, respectively.
Extended Data Fig. 7 Postseismic deformation at each pre-processed GNSS timeseries.
a, Pre-processed east (top), north (middle), and up (bottom) components of the GNSS data of station LAUT (the same as Extended Data Fig. 5d–f) are plotted in black, while the contribution from the postseismic deformation in each timeseries is plotted in red. Error bars are for one standard deviation. The time of the earthquake is shown with a solid green line. Note that the scale of up component is larger than those of horizontal components. b–d, Same as a, but for stations NIUM, SAMO, and TONG, respectively. Stations SAMO and TONG have gaps in data close to the end of the analyzed time period.
Extended Data Fig. 8 Ratio of the shear modulus (μ) and viscosity (η) to the reference values (μref, ηref) as a function of depth.
The ratios (solid blue line) with respect to the reference (solid orange line) are calculated based on the Preliminary Reference Earth Model34.
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Park, S., Avouac, JP., Zhan, Z. et al. Weak upper-mantle base revealed by postseismic deformation of a deep earthquake. Nature 615, 455–460 (2023). https://doi.org/10.1038/s41586-022-05689-8
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