Probing the viscosity of Venus mantle from dynamic topography at Baltis Vallis

The Baltis Vallis channel on Venus preserves a record of long-wavelength deformation generated by a convecting mantle, providing a unique window into the planet's geodynamics. Notably, the observed topography along the channel is not downhill, suggesting complex interactions between surface processes and mantle dynamics. We statistically compare the observed dynamic topography of Baltis Vallis with dynamic topographies generated by a suite of stagnant-lid mantle convection models to constrain Venus' interior dynamics. Baltis Vallis's relatively young age (likely less than 250 Myr) and low root-mean-square relief of 217 m indicate vigorous convection in Venus's mantle, with a Rayleigh number greater than 4x108, implying a mantle viscosity 1-2 orders of magnitude lower than Earth's. This difference may result from either a water-rich, less-degassed interior or a higher-temperature mantle beneath an insulating lid. Additionally, our simulations suggest that melt advection may dominate heat transport on Venus, potentially leading to non-linear temperature profiles in the crust. Upcoming missions such as VERITAS and EnVision will deliver higher-resolution gravity and topographic data, providing further constraints on Venus's present-day internal dynamics and the origin of Baltis Vallis.

Methods
Our mantle dynamics simulations use the StagYY code in a 3D spherical shell, using the yin-yang grid scheme (Tackley, 2008). StagYY uses a finite-volume, primitive variable discretization, solves the velocity-pressure equation using a multigrid solver, advects the temperature field using the MPDATA advection technique (Smolarkiewicz, 1984) and uses tracer particles to track composition and melt (Tackley & King, 2003). We use the Generic Mapping Tools (GMT) 6 software package (Wessel et al., 2019) to generate a topographic map from the modeled topography, project the topography onto the BV profile, and extract the model topography for further analysis. The data is then analyzed using Python scripts. We compare the simulated topographies of model BV profiles to the observed topography of BV using two metrics. The first metric is the root-mean-square (RMS) height (Shepard et al., 2001). The second metric is the decorrelation time, inspired by the observation of BV’s present-day uphill flow and the inference that the present-day topography must be uncorrelated with the original topography since BV formed flowing downhill. We assume a cutoff value of zero based on correlations between synthetic profiles randomly generated from a power-law distribution function and BV’s topography. The BV profile along with all model profiles are detrended prior to calculating their decorrelation times and RMS heights. See our paper (McGregor et al., 2024) for further details about the StagYY code (Tackley, 2008) as well as our models and statistical analysis.