Simulation-Based Inference of Martian Lithospheric Structure Using Normalising Flows

Due to the limited availability of seismic data from Mars, the current best source of information about the internal structure of the planet is from the measurements of the gravitational field. In this work, the lateral density variations in the lithosphere are studied using a simulation-based approach linking the planetary model and measurements of gravity. A two-layer planetary model is implemented, where we study lateral density distributions in the crust and the mantle. However, gravity data is known for its insensitivity to depth-related information. Therefore, we parameterize the two layered model using a Matérn covariance function to simulate realistic distributions and reduce the number of freedoms. The question we aim to answer is; what can we learn about the Matérn parameters governing these distributions?

We simulate various synthetic 2-layer crust-mantle models from a multivariate normal distribution. The synthetic models are inputted in a Bayesian inference and probabilistic modelling is performed using a Normalising Flow neural network techniques. From the inference results, we can learn about the scale and structure of density variations in the lithosphere, as well as the relationships between the parameters governing these distributions in the two layers, and the sensitivity of available gravity data to this information. We have then used the available gravity data of the Martian gravity field to estimate the Matérn covariance parameters for a two layered density model. These parameters give insight the the subsurface density distribution in the form of variance of crustal and mantle anomalies, their spatial correlation, and any smoothing effects. The results of this study can inform future gravity inversion efforts and provide a stepping stone to the development of a global density map of the lithosphere of Mars.