Abstract Image-based modelling of porous media to study the transport and reaction processes has become an essential tool. The availability of increasingly large image datasets at high resolution creates a need to develop algorithms that can process massive size images at a low computational cost. This study presents an efficient workflow to extract pore networks form large size porous domains using a watershed segmentation with geometrical domain decomposition. The method subdivides a porous image into smaller overlapping subdomains and performs a watershed segmentation on each subdomains in parallel or serial modes of operation to save CPU time or memory RAM, respectively. The computational performance of the algorithm was analyzed on a large size image and found to consume 50 percent less memory and upto 7 times less CPU time than the standard watershed implementation. Pore networks of four massive digital rock images were extracted and the the effective permeability predicted by the networks agreed well with previously investigated values illustrating the accuracy of the method. An additional application of this methods, taking advantage of the reduced computational cost, is the upgrading of low-resolution image. It was found that that increasing the resolution of a coarse image leads to more accurate predictions by helping the watershed segmenation prouduce a more faithful pore network model. The developed algorithm is implemented in Python, and included in the open source project PoreSpy. It uses highly optimized and efficient modules such as Dask and Numba to obtain the maximum performance. The domain decomposition approach used here will also lend itself well to processing on distributed memory clusters, enabling the processing of even larger porous domains.

中文翻译：

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通过几何域分解从大量断层图像中有效提取孔隙网络

摘要 基于图像的多孔介质建模已成为研究输运和反应过程的重要工具。越来越大的高分辨率图像数据集的可用性需要开发能够以低计算成本处理大量图像的算法。本研究提出了一种有效的工作流程，使用带几何域分解的分水岭分割从大尺寸多孔域中提取孔隙网络。该方法将多孔图像细分为较小的重叠子域，并以并行或串行操作模式对每个子域执行分水岭分割，以分别节省 CPU 时间或内存 RAM。在大尺寸图像上分析了该算法的计算性能，发现与标准分水岭实现相比，它消耗的内存减少了 50%，CPU 时间减少了多达 7 倍。提取了四张大型数字岩石图像的孔隙网络，网络预测的有效渗透率与先前研究的值非常吻合，说明了该方法的准确性。这种方法的另一个应用，利用降低的计算成本，是低分辨率图像的升级。研究发现，通过帮助分水岭分割产生更可靠的孔隙网络模型，提高粗略图像的分辨率会导致更准确的预测。开发的算法是用 Python 实现的，并包含在开源项目 PoreSpy 中。它使用了高度优化和高效的模块，例如 Dask 和 Numba，以获得最大的性能。这里使用的域分解方法也很适合在分布式内存集群上进行处理，从而能够处理更大的多孔域。