  We propose a new methodology to perform mineralogic inversion from wellbore
logs based on a Bayesian linear regression model. Our method essentially relies
on three steps. The first step makes use of Approximate Bayesian Computation
(ABC) and selects from the Bayesian generator a set of candidates-volumes
corresponding closely to the wellbore data responses. The second step gathers
these candidates through a density-based clustering algorithm. A mineral
scenario is assigned to each cluster through direct mineralogical inversion,
and we provide a confidence estimate for each lithological hypothesis. The
advantage of this approach is to explore all possible mineralogy hypotheses
that match the wellbore data. This pipeline is tested on both synthetic and
real datasets.
