Flexure of the Lithosphere Beneath the North Polar Cap of Mars: Implications for Ice Composition and Heat Flow

Abstract

The geodynamical response of the lithosphere under stresses imposed by the geologically young north polar cap is one of the few clues we have to constrain both the polar cap composition and the present‐day thermal state of Mars. Here we combine radar data with a flexural loading model to self‐consistently estimate the density ( ρ) and real dielectric constant ( ε′) of the polar cap, and the elastic thickness of the lithosphere underneath ( Te). Our results show that ρ ranges from 920 to 1,520 kg m −3, ε′ is 2.75 (+0.40, −0.35), and Te is between 330 and 450 km. We determine a polar cap volume that is up to 30% larger than current estimates that all neglect lithospheric flexure. Our inferred compositions suggest that, for dust content larger than about 6 vol%, 10 vol% CO 2 ice is mixed within the polar deposits, which has important implications for the climate evolution of Mars.

Characteristics of the Basal Interface of the Martian South Polar Layered Deposits

“…We find no evidence for significant “hidden” CO 2 deposits elsewhere in the SPLD (e.g., postulated in the NPLD in Broquet et al., 2020, and “reflection‐free zones” in Phillips et al., 2011), although the sensitivity to such deposits in our method would make their detection difficult. Our assumption of a water ice wave speed results in a plausible geometry of the basal interface both regionally and locally.…”

Thickness and structure of the martian crust from InSight seismic data

“…First, the gravitational attraction of the surface was computed using finite-amplitude techniques (85). Second, the gravitational attraction of the low-density polar caps was accounted for using densities of 1250 and 1300 kg m -3 for the north and south polar caps, respectively, along with the polar cap thickness model of Broquet et al (86). Third, the gravitational attraction of hydrostatic relief in the mantle and core beneath the lithosphere was computed using the method described in Wieczorek et al (25).…”

Toward Constraining Mars' Thermal Evolution Using Machine Learning

“…On the other hand, several observables useful to constrain the thermal history of Mars are localized in space and time. Particularly relevant are (i) the thickness of the elastic lithosphere associated with the loading of surface features (e.g., Broquet et al., 2020; McGovern et al., 2002; Phillips et al., 2008); (ii) the surface heat flux, which can be inferred from the latter (unfortunately, the efforts of the HP3 experiment to obtain a measurement of the heat flux at the landing site of the NASA mission InSight (Spohn et al., 2018) have not been successful); (iii) the local thickness of the crust, which can be obtained indirectly from gravity and topography data (e.g., Goossens et al., 2017; Wieczorek & Zuber, 2004), but which could be seismically detected in the future also by the InSight mission (Banerdt et al., 2020), possibly along with additional seismic discontinuities bearing information on the interior temperature; (iv) indications of past volcanic activity at specific locations (e.g., Hauber et al., 2011; Werner, 2009). In order to invert these local observations, the MDNs would need to be trained on 3D data generated with simulations that are well representative of the Martian interior.…”