Generic tool for numerical simulation of transformation-diffusion processes in complex volume geometric shapes: application to microbial decomposition of organic matter

This paper presents a generic framework for the numerical simulation of transformation-diffusion processes in complex volume geometric shapes. This work follows a previous one devoted to the simulation of microbial degradation of organic matter in porous system at microscopic scale. We generalized and improved the MOSAIC method significantly and thus yielding a much more generic and efficient numerical simulation scheme. In particular, regarding the simulation of diffusion processes from the graph, in this study we proposed a completely explicit and semi-implicit numerical scheme that can significantly reduce the computational complexity. We validated our method by comparing the results to the one provided by classical Lattice Boltzmann Method (LBM) within the context of microbial decomposition simulation. For the same datasets, we obtained similar results in a significantly shorter computing time (i.e., 10-15 minutes) than the prior work (several hours). Besides the classical LBM method takes around 3 weeks computing time.

3D Quantum Cuts for Automatic Segmentation of Porous Media in Tomography Images

Binary segmentation of volumetric images of porous media is a crucial step towards gaining a deeper understanding of the factors governing biogeochemical processes at minute scales. Contemporary work primarily revolves around primitive techniques based on global or local adaptive thresholding that have known common drawbacks in image segmentation. Moreover, absence of a unified benchmark prohibits quantitative evaluation, which further clouds the impact of existing methodologies. In this study, we tackle the issue on both fronts. Firstly, by drawing parallels with natural image segmentation, we propose a novel, and automatic segmentation technique, 3D Quantum Cuts (QCuts-3D) grounded on a state-of-the-art spectral clustering technique. Secondly, we curate and present a publicly available dataset of 68 multiphase volumetric images of porous media with diverse solid geometries, along with voxel-wise ground truth annotations for each constituting phase. We provide comparative evaluations between QCuts-3D and the current state-of-the-art over this dataset across a variety of evaluation metrics. The proposed systematic approach achieves a 26% increase in AUROC while achieving a substantial reduction of the computational complexity of the state-of-the-art competitors. Moreover, statistical analysis reveals that the proposed method exhibits significant robustness against the compositional variations of porous media.