Constraining Venus' Interior Structure Using Geophysical Data from PVO and Magellan: Implications for EnVision

Existing geodetic constraints from Pioneer Venus Orbiter (PVO) and Magellan provide the foundation for investigating Venus' interior (Konopliv & Yoder 1996). Estimates of the planet's mass, moment of inertia (MoI), and tidal Love number (k₂ = 0.295 ± 0.033) enable the development of probabilistic interior models despite limited direct seismic or magnetic measurements. In this study, we analyze these geophysical observations that characterize the internal mass distribution and tidal response of Venus to invert its internal structure by using a Bayesian approach based on the Markov Chain Monte Carlo (MCMC) method. The analysis incorporates current uncertainties and highlights the potential of k₂ as a more sensitive constraint than MoI in distinguishing internal configurations. The interior is modeled as a spherically symmetric, layered structure composed of crust, upper and lower mantle, and an iron-rich core. The mantle and core compositions are derived from thermodynamic constraints assuming single-stage core-mantle differentiation. Mantle mineralogy follows the MgO-FeO-SiO₂ system, while the core includes Fe, FeSi, FeS, and FeO end-members. Equations of state account for pressure- and temperature-dependent density variations. The thermal structure is defined by a conductive crust, an adiabatic mantle, and a temperature drop across the core-mantle boundary. The planet's tidal response is modeled with the PyALMA framework using Andrade rheology, assuming constant viscosities and depth-dependent shear modulus. Model convergence is evaluated with the Gelman-Rubin criterion, and the resulting posterior distributions constrain plausible internal structures consistent with current data. Looking ahead, the EnVision mission will provide significantly improved measurements of k₂ and, potentially, inform on the tidal dissipation. These enhanced constraints will allow the retrieval of higher-resolution information on Venus' interior, including the detection of partial melt in the core and mantle viscosity gradients. The methodology developed here is designed to assimilate future data, offering a flexible approach to track how increasingly precise geophysical measurements refine our understanding of planetary interiors.