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Random neighborhood graphs as models of fracture networks on rocks: Structural and dynamical analysis

机译:随机邻域图作为岩石上骨折网络的模型:结构和动态分析

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摘要

We propose a new model to account for the main structural characteristics of rock fracture networks (RFNs). The model is based on a generalization of the random neighborhood graphs to consider fractures embedded into rectangular spaces. We study a series of 29 real-world RFNs and find the best fit with the random rectangular neighborhood graphs (RRNGs) proposed here. We show that this model captures most of the structural characteristics of the RFNs and allows a distinction between small and more spherical rocks and large and more elongated ones. We use a diffusion equation on the graphs in order to model diffusive processes taking place through the channels of the RFNs. We find a small set of structural parameters that highly correlates with the average diffusion time in the RFNs. We found analytically some bounds for the diameter and the algebraic connectivity of these graphs that allow to bound the diffusion time in these networks. We also show that the RRNGs can be used as a suitable model to replace the RFNs in the study of diffusion-like processes. Indeed, the diffusion time in RFNs can be predicted by using structural and dynamical parameters of the RRNGs. Finally, we also explore some potential extensions of our model to include variable fracture apertures, the possibility of long-range hops of the diffusive particles as a way to account for heterogeneities in the medium and possible superdiffusive processes, and the extension of the model to 3-dimensional space. (C) 2017 Elsevier Inc. All rights reserved.
机译:我们提出了一种新模型,以考虑岩石骨折网络(RFN)的主要结构特征。该模型基于随机邻域图的概括,以考虑嵌入矩形空间的裂缝。我们研究了一系列29个现实世界RFN,并找到了在此提出的随机矩形邻域图(RRNG)的最佳拟合。我们表明,该模型捕获了RFN的大部分结构特征,并且允许小于较小的球形岩石和大而细长的岩石之间的区别。我们在图上使用扩散方程,以便通过RFN的频道模拟漫射过程。我们发现一小一组结构参数,与RFNS中的平均扩散时间高度相关。我们发现分析了这些图形的直径的一些界限,这些图形可以在这些网络中结合扩散时间。我们还表明RRNG可以用作合适的模型来取代rfns在研究中的扩散过程中的研究中。实际上,可以通过使用RRNGS的结构和动态参数来预测RFN中的扩散时间。最后,我们还探讨了我们模型的一些潜在扩展,包括可变骨折孔径,扩散粒子的远程跳跃的可能性作为介质中的异质性和可能的​​超级流程的异质性,以及模型的延伸三维空间。 (c)2017年Elsevier Inc.保留所有权利。

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