Identification of Rock Crack Damage Regions using Machine Learning-data

Dentification of macrocracks and their evolution under external loads is pivotal in studying rock failure. The formation of macrocracks can be attributed to the progression of microcracks. One significant challenge in studying microcracks is their location within the rock, rendering direct observation infeasible. This paper presents a methodology to characterize hypothetical damage regions formed by microcracks using Acoustic Emission (AE) data. Initially, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm was employed to filter out less significant AE events, thereby pinpointing critical clusters of microcracks. The spatial distribution of microcracks was then modeled using a Gaussian Mixture Model (GMM), with the Expectation-Maximization (EM) algorithm utilized to compute the GMM components. Confidence regions of the Gaussian distributions were calculated to delineate the damage regions. The results demonstrated that this approach effectively identifies damage regions associated with macrocracks. The generated damage regions accurately indicate the location and orientation of macrocracks. This investigation enhances the understanding of the distribution of microcracks and their relation to macrocracks. Moreover, this approach can be considered an automated method for quantitatively assessing cracks.