Coal and rock dynamic disasters have been the main concern in underground engineering because these seriously threaten the safety of miners and industrial production. Aiming to improve the EMR and AE monitoring technology, the refined nonlinear characteristics of EMR and AE during coal splitting failure are studied using Hilbert-H and multifractal theory, and valuable information pertaining to coal fracture law contained in EMR and AE waveform was revealed. The results show that the EMR and AE of coal splitting failure are related to the process of coal crack propagation. They possess the same initiation time and frequency band, however, the signal duration of EMR is comparatively longer than AE, and the main frequency of AE is higher than EMR. The EMR of coal splitting failure has the same excitation source as AE; nonetheless, the excited forms display different behavior. In terms of signal duration, the distribution of EMR signal is relatively uniform, the proportion of large-signal is less, the amount of information is more than that of AE, and the multifractal characteristics are more complicated. During the coal splitting failure, AE is mainly generated in the process of wall vibration caused by crack propagation, while the generation of EMR includes piezoelectric effect, charge separation, free charge vibration, charge neutralization and other processes, making EMR more complicated than AE and has a relatively low frequency. The research provides an effective method for studying nonlinear refinement characteristics of coal EMR and AE, and can provide an important basis for the study of the mechanism of EMR generation.

The formation of coalesced fractures critically alters the mechanical properties of the surrounding virgin material, significantly changing the stress distribution and deformation behavior of the rock mass. However, understanding the generation mechanism and accurate prediction of rock fracture growth remain challenging in many engineering projects. Despite, wide range of conventional approaches including field investigations, laboratory-scale tests, and numerical modeling, the complex geological conditions hinder their accurate determination. This study introduces a new robust and cost-effective holistic geophysical approach to determine fractures propagation and predict failure in coal-rock masses at laboratory scale applicable across scales. The proposed approach combines rock mechanics and Acoustic Emission (AE) testing systems to make useful correlation between AE source parameters and deformation kinetics. This correlation analyzes the spatiotemporal distribution of AE events to elucidate the evolution of fracture patterns in coal-rock specimens from a complex mining project. Results showed dense and complex fracturing networks within coal specimens due to higher density, compaction, and mechanical strength compared to rock samples. This is indicated by peak acoustic events at 80%–100% load versus minimal events at 0–15% load. Simulated fracture patterns closely matched observed acoustic events, identifying key lineaments (macro-cracks) representing the transition from microcracks to macro-fractures. The convergence of these lineaments indicated intensely deformed zones prone to failure, consistent with previous field investigations. Acoustic parameters describing critical damage revealed an inverse relationship between stress and AE event magnitude. At roughly 70% σ _max, a dramatic fall is seen in acoustic parameters indicated the shift from small-scale to large-scale microfractures, ultimately leading to catastrophic failure of the samples. Furthermore, Single Link Cluster (SLC) analysis demonstrated strong correlation among AE events, spatial correlation length (ξ) and information entropy (H). Both increased significantly at the onset of loading and fluctuated in proximity to ultimate failure. Using the micro-crack density criterion and 3D-crack growth theory, changes in above parameters verified the cracks transformation process. These findings showed that, the proposed approach compared with the conventional approaches, can improve disaster control and management plans, predict critical failures, and save lives in global mining projects when applied to field-scale studies.