Computational microbiology of soil organic matter mineralization: Use of the concept of curve skeleton to partition the 3D pore space in computed tomography images

Recent advances in 3D X-ray Computed Tomography (CT) sensors have stimulated research efforts to unveil the extremely complex micro-scale processes that control the activity of soil microorganisms. Classical methods for the numerical simulation of biological dynamics using meshes of voxels, such as the Lattice Boltzmann Method (LBM), tend to require long computation times. The use of more compact geometrical representations of the pore space can drastically decrease the computational cost of simulations. Recent research has introduced basic analytic volume primitives to define piece-wise approximations of the pore space to simulate drainage, diffusion, and microbial mineralization of organic matter in soils. Such approaches work well but a drawback is that they give rise to non-negligible approximation errors. In the present article, another alternative is proposed, where pore space is described by means of geometrically relevant connected subsets of voxels (regions) regrouped on the basis of the curve linear skeleton (3D medial axis). This curve skeleton has been adopted to characterize 3D shapes in various fields (e.g., medical imaging, material sciences, etc.) but the few publications that have used it in the context of soils, have dealt exclusively with the determination of pore throats. This technique is used mostly to describe shape and not to partition it into connected subsets. Here, the pore space is partitioned by using the branches of the curve skeleton, then an attributed relational graph is created in order to simulate numerically the microbial mineralization of organic matter, including the diffusion of by-products. This new representation can be used for graph-based simulations, which are different from voxel-based simulations.