  Recent advances in 3D X-ray Computed Tomographic (CT) sensors have stimulated
research efforts to unveil the extremely complex micro-scale processes that
control the activity of soil microorganisms. Voxel-based description (up to
hundreds millions voxels) of the pore space can be extracted, from grey level
3D CT scanner images, by means of simple image processing tools. Classical
methods for numerical simulation of biological dynamics using mesh of voxels,
such as Lattice Boltzmann Model (LBM), are too much time consuming. Thus, the
use of more compact and reliable geometrical representations of pore space can
drastically decrease the computational cost of the simulations. Several recent
works propose basic analytic volume primitives (e.g. spheres, generalized
cylinders, ellipsoids) to define a piece-wise approximation of pore space for
numerical simulation of draining, diffusion and microbial decomposition. Such
approaches work well but the drawback is that it generates approximation
errors. In the present work, we study another alternative where pore space is
described by means of geometrically relevant connected subsets of voxels
(regions) computed from the curvilinear skeleton. Indeed, many works use the
curvilinear skeleton (3D medial axis) for analyzing and partitioning 3D shapes
within various domains (medicine, material sciences, petroleum engineering,
etc.) but only a few ones in soil sciences. Within the context of soil
sciences, most studies dealing with 3D medial axis focus on the determination
of pore throats. Here, we segment pore space using curvilinear skeleton in
order to achieve numerical simulation of microbial decomposition (including
diffusion processes). We validate simulation outputs by comparison with other
methods using different pore space geometrical representations (balls, voxels).
