CHEM 1227

kig [at] umd [dot] edu

Laurent Montési

I am interested in constraining the interior structure of the terrestrial planets and the Moon. I currently use Lunar gravity data as input in a novel global inversion algorithm to obtain the range of large-scale density anomalies that might exist in its deep interior and subsurface. My research relies on Bayesian statistics and Monte Carlo methods to obtain a representative sampling of density models that fit the data. Cluster analysis and measures of significance allow me to further simplify the view of the Lunar interior that these models provide and say how much we can trust it.

Izquierdo, K., Lekic, V., Montesi, L. (2020) A Bayesian Approach to Infer Interior Mass Anomalies from the Gravity Data of Celestial Bodies. Geophysical Journal International. 220 1687-1699. DOI: 10.1093/gji/ggz544