Understanding the causes of intraplate earthquakes is challenging, as it requires extending plate tectonic theory to the dynamics of continental deformation. Seismicity in the western United States away from the plate boundary is clustered along a meandering, north–south trending ‘intermountain’ belt1. This zone coincides with a transition from thin, actively deforming to thicker, less tectonically active crust and lithosphere. Although such structural gradients have been invoked to explain seismicity localization2,3, the underlying cause of seismicity remains unclear. Here we show results from improved mantle flow models that reveal a relationship between seismicity and the rate change of ‘dynamic topography’ (that is, vertical normal stress from mantle flow). The associated predictive skill is greater than that of any of the other forcings we examined. We suggest that active mantle flow is a major contributor to seismogenic intraplate deformation, while gravitational potential energy variations have a minor role. Seismicity localization should occur where convective changes in vertical normal stress are modulated by lithospheric strength heterogeneities. Our results on deformation processes appear consistent with findings from other mobile belts4, and imply that mantle flow plays a significant and quantifiable part in shaping topography, tectonics, and seismic hazard within intraplate settings.
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We thank C. Kreemer for publishing his geodetic strain-rate models in electronic form, and C. Conrad for comments. All figures were created with Generic Mapping Tools28. Some analysis was based on data services provided by the Plate Boundary Observatory operated by UNAVCO for EarthScope and supported by the National Science Foundation (NSF; EAR-0350028 and EAR-0732947). T.W.B. was partially supported by NSF/US Geological Survey Southern California Earthquake Center, as well as EAR-1215720 and EAR-1215757. A.R.L. was supported by EAR-0955909 and EAR-1358622.
The authors declare no competing financial interests.
Extended data figures and tables
Extended Data Figure 1 Maps of selected, additional scalar fields considered for the Molchan analysis in Fig. 2 and Extended Data Fig. 2.
a–c, Moho depth estimates from Levander and Miller3 (LM12S), Lowry and Pérez-Gussinyé11 (LP), and Shen et al.47 (CUB). d–f, ‘Lithosphere asthenosphere boundary’ (LAB) depth inferred from receiver functions by Levander and Miller3, shear wave version (d), horizontal normal strainrates based on GPS geodesy from Kreemer et al.29 (extension positive) (e), and vertical GPS rates, smoothed from the 2014 UNAVCO/PBO velocity release (pbo.final_frame.vel at ftp://data-out.unavco.org/pub/products/velocity/) with presumed outliers removed (f).
We show results for three Moho models: CUB47, LM12S3 (S wave version) and LP11 (as used in the main text, compare Extended Data Fig. 1a–c), estimates of LAB thickness from receiver functions (Extended Data Fig. 1d) and elastic thickness, and two kinds of GPE models, as in the main text, gradients thereof, geodynamic model predictions, and kinematic models for the western United States (Fig. 1). Dashed lines indicate the regions outside of which results are 95% significant. This more comprehensive analysis confirms the conclusions about relative model performance.
Extended Data Figure 4 Correlation, r, between seismicity density (ϕ, Fig. 1b) and different models.
Obtained by even-area sampling of regions outside the plate boundary zone in Fig. 1, compare with Extended Data Fig. 2. Filled and open symbols are for linear (Pearson), r, and Spearman rank, rS, correlation, respectively. Dashed lines indicate the regions outside of which results are 95% significant, grey shading denotes approximate random density function distribution. Relative performance of Pearson correlation is consistent with results for skill (Extended Data Fig. 2), and Spearman rank shows some deviations, indicating lack of simple, linear relationships between ϕ and scalar fields.
Models considered: shear strain rates from geodesy (Fig. 1c); rate change of dynamic topography (Fig. 1f); gradients of Moho depth11; and GPE (Fig. 1d). Circles on right show the total correlation as in Extended Data Fig. 4. Solid, heavy lines and filled symbols are for linear (Pearson) correlation, and thin, dashed lines and open symbols are for Spearman rank correlation, respectively. Rate change of dynamic topography shows positive correlation with seismicity across most wavelengths. Shorter wavelengths, where the response of a rheologically complex lithosphere will be most relevant, emphasize the differences between r and rS seen in Extended Data Fig. 4.
Extended Data Figure 6 Illustrative, synthetic mantle flow models exploring dynamic topography and rate change thereof for simple, ad hoc temperature anomalies.
a–h, Bottom subplots show temperature anomalies and mantle flow velocities, top subplots show dynamic topography on the surface (left) and temporal change thereof (right) for four mantle density cases along profiles. a, b, Shallow, hot anomaly; c, d, shallow, cold anomaly; e, f, deep, hot anomaly; g, h, deep, cold anomaly. Note how rate change of dynamic topography is positive for both cold, sinking and hot, rising anomalies because of reduction of negative and increase of positive dynamic topography, respectively23,24.
Extended Data Figure 7 Multivariable regression analysis to best fit the seismicity density, evaluating a subset of the models of Extended Data Fig. 4.
The normalized contribution of each model vector, the importance wi , is indicated by filled and open symbols from a non-negative or regular least squares solution of equation (1), respectively. Results indicate that rate change of dynamic topography is the strongest driver of seismicity, with contributions from GPE, and modulated by Moho depth gradients and elastic thickness variations (compare with Extended Data Fig. 4).
Extended Data Figure 8 Wavelength-dependent correlation between vertical GPS rates (Extended Data Fig. 1f) and selected scalar fields.
For models and legend see Extended Data Fig. 5. Rate of topography change and shear strain rates show positive correlation with geodetically imaged uplift at wavelengths of ∼500…900 km, but rate change of dynamic topography is negatively correlated at longer wavelengths. Results indicate that rate change of dynamic topography may need to be understood more fully in context of a rheologically realistic model of lithospheric deformation, whereas geodetic uplift rates may be strongly affected by other signals, for example, climatic and hydrological.
Extended Data Figure 9 Molchan curves for mantle flow predictions for a range of tomographic models.
Flow modelling uses our reference model12 (compare with Fig. 2a and Extended Data Fig. 2), and older tomography38: SH11 (ref. 39) is a precursor of ref. 12; DNA10 a joint surface and body wave inversion by Obrebski et al.48; and NWUS-S an earlier, USArray-based shear wave tomography model by James et al.49. The best GPE model and smoothed seismicity are shown for reference. Older tomographic models substantiate our conclusions based on ref. 12; in particular SH11, which was found to bestpredict delay times40, has near-identical skill.
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Becker, T., Lowry, A., Faccenna, C. et al. Western US intermountain seismicity caused by changes in upper mantle flow. Nature 524, 458–461 (2015). https://doi.org/10.1038/nature14867
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