Characterization of mechanical discontinuities based on data-driven classification of compressional-wave travel times. (July 2021)

• Record Type: Journal Article
• Authors: Li, Hao; Misra, Siddharth; Liu, Rui
• Is Part Of: International journal of rock mechanics and mining sciences. Volume 143(2021)
• Publication Date: 2021-07
• Subjects: Machine learning -- Fast marching method (FMM) -- Discrete fracture network (DFN) -- Wave propagation -- Mechanical discontinuities -- Crack; Rock mechanics -- Periodicals; Soil mechanics -- Periodicals; Mining engineering -- Periodicals; Roches, Mécanique des -- Périodiques; Sols, Mécanique des -- Périodiques; Technique minière -- Périodiques; 624.151305
• DOI: 10.1016/j.ijrmms.2021.104793
• Languages: English
• ISSNs: 1365-1609

Abstract

Abstract: 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.