The phenomenon of flow-channeling, or the existence of preferential pathways for flow in fracture networks, is well known. Identification of the channels (“backbone”) allows for system reduction and computational efficiency in simulation of flow and transport through fracture networks. However, the purpose of machine learning techniques for backbone identification in fractured media is two-pronged system reduction for computational efficiency in simulation of flow and transport as well as physical insight into the phenomenon of flow channeling. The most critical aspect of this problem is the need to have a truly “physics-informed” technique that respects the constraint of connectivity. We present a method that views a network as a union of connected paths with each path comprising a sequence of fractures. Thus, the fundamental unit of selection becomes a sequence of fractures, classified based on attributes that characterize the sequence. In summary, this method represents a parametrization of the sample space that ensures every selected sample sub-network (which is the union of all selected sequences of fractures) always respects the constraint of connectivity, demonstrating that it is a truly physics-informed method. The algorithm has a user-defined parameter which allows control of the backbone size when using the random forest or logistic regression classifier. Even when the backbones are less than 30% in size (leading to computational savings), the backbones still capture the behavior of the breakthrough curve of the full network. Moreover, there is no need to check for path connectedness in the backbones unlike previous methods since the backbones are guaranteed to be connected.

中文翻译：

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物理信息机器学习，用于离散裂缝网络中的骨干识别

流动通道现象或裂缝网络中流动优先通道的存在是众所周知的。通道（“骨干”）的识别可在模拟裂缝网络中的流动和输送过程中减少系统并提高计算效率。但是，机器学习技术用于裂缝介质中骨干识别的目的是为了简化两管齐下的系统，以简化流动和运输模拟以及对流动通道现象的物理洞察的计算效率。这个问题最关键的方面是需要一种真正的“物理通知”技术，该技术要考虑到连通性的约束。我们提出了一种将网络视为连接路径的并集，而每个路径都包含一系列断裂的方法。从而，选择的基本单位将成为裂缝序列，并根据表征该序列的属性进行分类。总而言之，该方法代表了样品空间的参数化，可确保每个选定的样品子网络（所有选定的断裂序列的并集）始终遵守连通性的约束条件，从而证明这是一种真正的物理方法。该算法具有用户定义的参数，当使用随机森林或逻辑回归分类器时，该参数允许控制主干大小。即使骨干网的大小小于30％（导致节省计算空间），骨干网仍然可以捕获整个网络的突破曲线的行为。此外，