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Reorientation and faulting of Pluto due to volatile loading within Sputnik Planitia

Abstract

Pluto is an astoundingly diverse, geologically dynamic world. The dominant feature is Sputnik Planitia—a tear-drop-shaped topographic depression approximately 1,000 kilometres in diameter possibly representing an ancient impact basin1,2. The interior of Sputnik Planitia is characterized by a smooth, craterless plain three to four kilometres beneath the surrounding rugged uplands, and represents the surface of a massive unit of actively convecting volatile ices (N2, CH4 and CO) several kilometres thick1,2,3,4,5. This large feature is very near the Pluto–Charon tidal axis. Here we report that the location of Sputnik Planitia is the natural consequence of the sequestration of volatile ices within the basin and the resulting reorientation (true polar wander) of Pluto. Loading of volatile ices within a basin the size of Sputnik Planitia can substantially alter Pluto’s inertia tensor, resulting in a reorientation of the dwarf planet of around 60 degrees with respect to the rotational and tidal axes. The combination of this reorientation, loading and global expansion due to the freezing of a possible subsurface ocean generates stresses within the planet’s lithosphere, resulting in a global network of extensional faults that closely replicate the observed fault networks on Pluto. Sputnik Planitia probably formed northwest of its present location, and was loaded with volatiles over million-year timescales as a result of volatile transport cycles on Pluto6,7. Pluto’s past, present and future orientation is controlled by feedbacks between volatile sublimation and condensation, changing insolation conditions and Pluto’s interior structure.

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Figure 1: Geometry of Sputnik Planitia in the Pluto–Charon system.
Figure 2: True polar wander solutions for Sputnik Planitia and the resulting tectonic patterns.
Figure 3: Insolation patterns and volatile-driven TPW of Pluto.

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Acknowledgements

We thank the New Horizons science team for their many years of work that resulted in the successful flyby of Pluto, and for promptly releasing the public data and published work1,2,3,4,13,16 that enabled this research. We note that all names of features on Pluto are informal. J.T.K. acknowledges support from the University of Arizona Theoretical Astrophysics Program and NASA Solar System Workings.

Author information

Authors and Affiliations

Authors

Contributions

J.T.K. identified the link between Sputnik Planitia and the tidal axis, performed true polar wander analyses, mapping and analysis of tectonic features, proposed connection between polar wander and secular volatile transport, was the primary author of the text and created all figures. I.M. performed analyses of Pluto’s shape and gravity field and performed tectonics calculations. S.K. provided Love numbers for Pluto. J.K.S. provided estimates of volatile deposition timescales for Sputnik Planitia and provided input on volatile transport.

Corresponding author

Correspondence to James T. Keane.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Mass anomalies of impact basins on other planetary bodies.

a, An oblique cross-section of a typical lunar mascon basin, Freundlich–Sharonov, highlighting the different components that contribute to a basin’s overall mass anomaly. b, A table of large basins on other planetary bodies (in order of the basin’s diameter relative to the host planet) for which we have adequate gravity measurements to determine their respective mass anomalies. c, The mass anomaly of each basin. The mass anomalies for lunar impact basins have been comprehensively characterized in ref. 12. Mass anomalies for impact basins on Mars and Mercury are calculated in the same way, using available gravity data for these two bodies. Uncertainties have not been quantified for impact basins on Mars and Mercury.

Extended Data Figure 2 Mascon components and their mass anomalies.

af, Mass anomaly (Q′) of an individual component of a basin (for example, volatile ice load or topographic depression) as a function of that component’s thickness and density. White dashed regions denote plausible areas of parameter space (densities of ices on Pluto are from ref. 7; basin depths and ice thicknesses are from refs 1, 2, 4, 5; ejecta blanket thicknesses are estimated by redistributing the mass excavated from the basin into an annulus outside the basin, between 1 and 2 crater radii). The mass anomaly of the impact basin can be constructed by linearly summing these components.

Extended Data Figure 3 Simple models for Sputnik Planitia.

ad, Q′ as a function of volatile thickness (left column) for each simple model of Sputnik Planitia (right column). Different colours and lines denote different model results assuming different densities of model components (volatile ice density, crust density, mantle density, and ejecta density, when appropriate). Nominal outputs from each model are shown in Fig. 2b. See Methods for discussion of this figure. a, Volatiles loading on the surface of a planet, which is equivalent to volatiles loading a basin with no intrinsic mass anomaly. b, Volatiles filling an initially uncompensated basin, with a fixed depth of 3 km from the surface of the planet to the top of the volatiles. c, Volatiles filling an initially uncompensated impact basin surrounded by an ejecta blanket extending from 1 to 2 crater radii containing the total mass excavated from within the basin. The height from the top of the rim to the top of the volatiles is fixed to 3 km. d, Volatiles filling a basin that is initially compensated from isostatic uplift of the presumed ocean at depth (see ref. 16 for a more thorough investigation of this hypothesis). In all plots, it is assumed that volatiles are partially supported by Pluto’s elastic lithosphere (Methods).

Extended Data Figure 4 The initial orientation of Pluto and the range of possible mass anomalies.

a–c, Orthographic spherical projections of Pluto for example initial orientations from the perspective of an inertial viewer, fixed with respect to the tidal/rotational axes. Base map: NASA/Johns Hopkins University Applied Physics Laboratory/Southwest Research Institute. The dashed white line indicates the strike of the ‘washboard terrain’ (Methods). d, Contours enclosing the possible initial locations of Sputnik Planitia as a function Q′. This is the same as Fig. 2a, but in an equirectangular map projection. e, Histogram of the Q′ values of allowable reorientation scenarios as shown in d (and Fig. 2a). The vast majority of solutions are positive mass anomaly solutions.

Extended Data Figure 5 Tectonic models.

aq, Tectonic patterns depend on the geometry of proposed reorientation (af), the interior structure (ik), and the size and location of the perturbing load (lq). Yet, despite all of the possible dependencies, it is striking that for the allowed reorientation scenarios, the predicted tectonic patterns show little variance. Most predict quasi-radial faults proximal to Sputnik Planitia (due to loading, g), transitioning to quasi-azimuthal faults distal to Sputnik Planitia (due to TPW stresses, h). Faults are coloured by azimuth, as in Fig. 2g–j. Black lines show mapped faults, as in Fig. 2g, h. rt, Tectonics patterns from TPW, loading and global expansion provide a far better match to the observed fault distribution than do de-spinning, orbit migration or global expansion.

Extended Data Figure 6 Tectonics due to the progressive loading of Sputnik Planitia.

ah, The predicted tectonic pattern on Pluto as a function of the amount of ice within Sputnik Planitia, as in Fig. 3d–i: f denotes the fraction of the total global reservoir of ice within Sputnik Planitia (where the reservoir is equivalent to a 200-m-thick global layer); f = 9% corresponds to 500 m, f = 12% corresponds to 800 m (as in Fig. 3f), f = 21% corresponds to 1,400 m (as in Fig. 3g) and f = 52% corresponds to 3,500 m. The left column shows a view above the faults west of Sputnik Planitia, while the right column shows a view above the faults east of Sputnik Planitia. Grid lines and coloured vectors denote the instantaneous principal axis reference frame. As Sputnik Planitia is loaded with volatiles, Pluto reorients. This changes the location of these features with respect to the principal axis reference frame (resulting in the reorientation of the latitude and longitude grid in each frame). This also changes the stresses as a function of loading within Sputnik Planitia, resulting in time-evolution of the tectonic patterns. The transition from quasi-radial (loading-dominated) to quasi-azimuthal (TPW-dominated) faults increases in distance from Sputnik Planitia as a function of the loading within Sputnik Planitia. This change in stress pattern may be reflected in future study of the cross-cutting relationships of Pluto’s faults. These images are snapshots from Supplementary Videos 1,2,3, which show additional time-steps.

Extended Data Figure 7 Probability of randomly being near a principal axis.

The probability of a randomly located feature being located near to any given principal axis (or set of axes). N is the number of axes considered; for example, the blue line indicates the chance probability of being near any single principal axis, whereas the yellow line indicates the chance probability of being near any of the principal axes. There is a 9% probability of Sputnik Planitia being this close to the tidal axis of Pluto.

Extended Data Figure 8 Pluto’s wobbles.

a, b, The location of Pluto’s minimum (a) and maximum (b) principal axes of inertia with respect to their present locations as a function of changes in ice thickness in Sputnik Planitia. Around 1 m of volatile ice can be transported seasonally across the entire surface of Pluto7, although it is unclear how volatiles migrate into and out of Sputnik Planitia. c, The change in rotational kinetic energy resulting from the transport of volatile ice into and out of Sputnik Planitia. df, The spherical harmonic degree-2 gravity coefficients associated with Sputnik Planitia and the remnant figure for each of the possible reorientation scenarios shown in Fig. 2a. Measurements of degree-2 gravity (or equivalently the moments of inertia) of Pluto will constrain possible reorientation scenarios.

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Supplementary information

Reorientation and faulting of Pluto due to the progressive loading of Sputnik Planum

Orthographic spherical projection of Pluto as it reorients in response to the transfer of volatiles from two polar caps into Sputnik Planum (as in Fig. 3d-i), as viewed from reference frame fixed to Pluto’s instantaneous principal axes of inertia. Color corresponds to ice thickness in one of the three possible reservoirs: the polar caps, Sputnik Planum, or the equatorial band (which is always ice-free in this simple model). The total volatile reservoir is equivalent to a global layer 200 meters thick. Observed tectonic patterns are in white, and predicted tectonic patterns at every time-step are shown colored by their azimuth (as in Fig. 2). (AVI 4420 kb)

Reorientation and faulting of Pluto due to the progressive loading of Sputnik Planum—Western Faults

Orthographic spherical projection of Pluto as it reorients in response to the transfer of volatiles from two polar caps into Sputnik Planum (as in Fig. 3d-i), as viewed from reference frame fixed above the faults west of Sputnik Planum. All symbols and colors are the same as in Supplementary Video 1. Snapshots of this video are shown in Extended Data Figure 8. (AVI 7669 kb)

Reorientation and faulting of Pluto due to the progressive loading of Sputnik Planum—Eastern Faults

Orthographic spherical projection of Pluto as it reorients in response to the transfer of volatiles from two polar caps into Sputnik Planum (as in Fig. 3d-i), as viewed from reference frame fixed above the faults east of Sputnik Planum. All symbols and colors are the same as in Supplementary Video 1. Snapshots of this video are shown in Extended Data Figure 8. (AVI 6698 kb)

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Keane, J., Matsuyama, I., Kamata, S. et al. Reorientation and faulting of Pluto due to volatile loading within Sputnik Planitia. Nature 540, 90–93 (2016). https://doi.org/10.1038/nature20120

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