Wave propagation and diffusive transport phenomena are influenced by the mechanical discontinuities in material. This study shows that certain bulk properties of the network of low-velocity mechanical discontinuities (e.g. air-filled cracks) in a material can be characterized by processing compressional-wave travel times using traditional data-driven classification techniques. To that end, we perform three tasks in chronological order: (1) use the discrete fracture network (DFN) method to create two-dimensional (2D) numerical models of crack-bearing material embedded with various types of low-velocity mechanical discontinuities, (2) use the fast marching method (FMM) to simulate the propagation of the wave/diffusion front from a single source through the 2D crack-bearing material to multiple receivers placed on the boundary of the material, and (3) train 9 data-driven classifiers to characterize the crack-bearing materials (i.e. bulk properties of the network of mechanical discontinuities in the crack-bearing material) by learning from the simulations of travel times detected by multiple receivers placed around the crack-bearing material. The classifiers identified the orientation, spatial distribution, and dispersion of the low-velocity mechanical discontinuities. Voting classifier performs the best among the 9 classifiers. For the characterization of bulk dispersion and distribution of discontinuities, the sensors located on the adjacent boundaries are more important; whereas for the characterization of bulk orientation of discontinuities, the sensors located on the opposite side are more important.

波的传播和扩散传输现象受机械不连续性的影响在材料上。这项研究表明，材料中的低速机械不连续性网络（例如充气裂缝）的某些整体性质可以通过使用传统的数据驱动分类技术处理压缩波传播时间来表征。为此，我们按时间顺序执行了三个任务：（1）使用离散断裂网络（DFN）方法创建嵌有各种低速机械不连续性的含裂纹材料的二维（2D）数值模型， （2）使用快速行进方法（FMM）模拟波/扩散锋从单一源通过2D含裂纹材料到放置在材料边界上的多个接收器的传播，以及（3）训练9个数据驱动的分类器来表征含裂纹的材料（即 通过从模拟模拟中学习到的传播时间，该模拟是通过在承载裂纹的材料周围放置的多个接收器检测到的传播时间来实现的。分类器确定了低速机械不连续性的方向，空间分布和分散性。投票分类器在9个分类器中表现最佳。为了表征体积分散和不连续性分布，位于相邻边界的传感器更为重要。而对于表征不连续体的整体取向，位于相对侧的传感器更为重要。