During mining activities, the deformation and damage of coal rock materials might result in coal rock dynamic disasters, such as rock burst. It leads to serious casualties and property losses. Generally, the occurrence of dynamic failure of coal and rock are caused by shear failure of coal seam. Geophysics signals are generated and related to damage evolution in this loading process. In this paper, sandstone samples were subjected to shear failure laboratory experiments, and the electric-magnetic-acoustic signal regularity was measured and analyzed comparatively. The results indicated magnetic signals were more correlated with stress and acoustic emission (AE) signals, while the amplitude of electric signal fluctuation was larger when main failure occurred. With the increase of sample size and shear strength, the strength of electric-magnetic-acoustic signals increased. The correlation coefficients between the magnetic signal and stress as well as AE energy were superior to those of electric signals. The coupling model between AE and electric signals was established, which shows good statistical correlation. This study lays the foundations for further interpreting the generation mechanism of the electric signal. It provides a new method to indicate the damage evolution of coal rock materials.

Implementing precise and advanced early warning systems for rock bursts is a crucial approach to maintaining safety during coal mining operations. At present, FEMR data play a key role in monitoring and providing early warnings for rock bursts. Nevertheless, conventional early warning systems are associated with certain limitations, such as a short early warning time and low accuracy of early warning. To enhance the timeliness of early warnings and bolster the safety of coal mines, a novel early warning model has been developed. In this paper, we present a framework for predicting the FEMR signal in deep future and recognizing the rock burst precursor. The framework involves two models, a guided diffusion model with a transformer for FEMR signal super prediction and an auxiliary model for recognizing the rock burst precursor. The framework was applied to the Buertai database, which was recognized as having a rock burst risk. The results demonstrate that the framework can predict 360 h (15 days) of FEMR signal using only 12 h of known signal. If the duration of known data is compressed by adjusting the CWT window length, it becomes possible to predict data over longer future time spans. Additionally, it achieved a maximum recognition accuracy of 98.07%, which realizes the super prediction of rock burst disaster. These characteristics make our framework an attractive approach for rock burst predicting and early warning.