AUTHOR=Barnoud Anne , Cayol Valérie , Lelièvre Peter G. , Portal Angélie , Labazuy Philippe , Boivin Pierre , Gailler Lydie TITLE=Robust Bayesian Joint Inversion of Gravimetric and Muographic Data for the Density Imaging of the Puy de Dôme Volcano (France) JOURNAL=Frontiers in Earth Science VOLUME=8 YEAR=2021 URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2020.575842 DOI=10.3389/feart.2020.575842 ISSN=2296-6463 ABSTRACT=
Imaging the internal structure of volcanoes helps highlighting magma pathways and monitoring potential structural weaknesses. We jointly invert gravimetric and muographic data to determine the most precise image of the 3D density structure of the Puy de Dôme volcano (Chaîne des Puys, France) ever obtained. With rock thickness of up to 1,600 m along the muon lines of sight, it is, to our knowledge, the largest volcano ever imaged by combining muography and gravimetry. The inversion of gravimetric data is an ill-posed problem with a non-unique solution and a sensitivity rapidly decreasing with depth. Muography has the potential to constrain the absolute density of the studied structures but the use of the method is limited by the possible number of acquisition view points, by the long acquisition duration and by the noise contained in the data. To take advantage of both types of data in a joint inversion scheme, we develop a robust method adapted to the specificities of both the gravimetric and muographic data. Our method is based on a Bayesian formalism. It includes a smoothing relying on two regularization parameters (an