This study presents a new computational approach for simulating the microbial decomposition of organic matter, from 3D micro-computed tomography (micro-CT) images of soil. The method employs a valuated graph of connected voxels to simulate transformation and diffusion processes involved in microbial decomposition within the complex soil matrix. The resulting model can be adapted to simulate any diffusion-transformation processes in porous media. We implemented parallelization strategies and explored different numerical methods, including implicit, explicit, synchronous, and asynchronous schemes. To validate our method, we compared simulation outputs with those provided by LBioS and by Mosaic models. LBioS uses a lattice-Boltzmann method for diffusion and Mosaic takes benefit of Pore Network Geometrical Modelling (PNGM) by means of geometrical primitives such as spheres and ellipsoids. This approach achieved comparable results to traditional LBM-based simulations, but required only one-fourth of the computing time. Compared to Mosaic simulation, the proposed method is slower but more accurate and does not require any calibration. Furthermore, we present a theoretical framework and an application example to enhance PNGM-based simulations. This is accomplished by approximating the diffusional conductance coefficients using stochastic gradient descent and data generated by the current approach.

翻译：本研究提出了一种新的计算方法，用于从土壤三维微计算机断层扫描（micro-CT）图像模拟有机物的微生物分解过程。该方法采用连接体素的赋值图来模拟复杂土壤基质中微生物分解涉及的转化与扩散过程。所得模型可适用于模拟多孔介质中的任意扩散-转化过程。我们实现了并行化策略，并探索了包括隐式、显式、同步和异步方案在内的多种数值方法。为验证本方法，我们将模拟输出与LBioS及Mosaic模型的结果进行了比较。LBioS采用格子玻尔兹曼方法处理扩散过程，而Mosaic则利用球体和椭球体等几何基元，通过孔隙网络几何建模（PNGM）获得优势。本方法取得了与传统基于LBM的模拟相当的结果，但仅需其四分之一的计算时间。与Mosaic模拟相比，所提方法速度较慢但精度更高，且无需任何参数校准。此外，我们提出了一个理论框架和应用实例来增强基于PNGM的模拟。这是通过使用随机梯度下降和本方法生成的数据来近似扩散传导系数实现的。