This thesis presents a methodology to characterize the interior structure of Ganymede, Jupiter’s largest moon, through a joint Bayesian inversion framework that integrates gravity, magnetic induction, tidal, and libration observations. The motivation arises from the growing scientific interest in icy moons, which not only provide insights into Solar System evolution but also represent promising candidates for habitability due to their subsurface oceans. Ganymede stands out as the largest moon in the Solar System and the only one known to possess its own intrinsic magnetic field. It is also believed to host a subsurface ocean beneath its icy crust, making it an interesting target for scientific exploration. However, much of its internal structure remains uncertain. The upcoming ESA’s Juice mission will carry out detailed observations of the moon through a series of flybys followed by an extended orbital tour, delivering high-precision data that will help constrain Ganymede's structure.
This work addresses the challenge of constraining Ganymede's interior structure using multiple datasets - gravity, magnetic induction, tidal, and libration observations - each sensitive to different interior parameters and affected by parameter degeneracies. We first perform a global sensitivity analysis to identify how each observable relates to specific interior properties. Our model assumes a five-layer spherical structure comprising a metallic core, silicate mantle, high-pressure ice, liquid salty ocean, and outer ice shell. The sensitivity analysis shows that magnetic induction is most sensitive to ocean thickness and composition, tidal displacement to ice shell thickness and rigidity, and libration amplitude to shell rigidity. Degeneracies also emerge, such as between shell thickness, ocean density, and shear modulus, highlighting the need for a joint inversion approach.
The Bayesian inversion is carried out progressively. Starting with Ganymede’s moment of inertia, we constrain the core and mantle but leave the hydrosphere largely unconstrained. Including magnetic induction data substantially improves the estimation of ice shell and ocean thicknesses, while the real part of the tidal Love number k₂ refines constraints on the densities of the ice shell and ocean and enables inference of ocean composition. These observations also provide information on the rigidity of high-pressure ice and the compressibility of interior layers. Finally, the combination of moment of inertia, magnetic induction amplitude, and both the real and imaginary parts of k₂ offers constraints on the viscosities of the ice layers, related to the tidal dissipation within Ganymede.
Beyond advancing our understanding of Ganymede, this study demonstrates the effectiveness of joint Bayesian inference for the characterization of planetary interior.This framework contributes to the scientific preparation for the ESA's Juice mission, identifies the most critical measurements to break degeneracies in interior model parameters, and can be extended to other icy moons, such as Europa and Enceladus, where spacecraft data will provide similar observational constraints.