The time–frequency characteristics of electromagnetic radiation (EMR) waveform induced by rock fracture are very important to the monitoring and early–warning using the EMR method for the mine rockburst. The empirical wavelet transform (EWT), as a waveform time–frequency analysis method, has the advantages of a clear theoretical basis, convenient calculation, and no modal aliasing. To apply EWT to the field of EMR time–frequency analysis, the operation of Fourier axis segmentation of EWT is improved. In detail, the adaptive selection method for a window width of closing operation and the adaptive determination method of segment number of Fourier axis are proposed for EWT. The Fourier axis obtained by short–time Fourier transform (STFT) is used in the EWT process, rather than that obtained by discrete Fourier transform (DFT), taking a better Fourier axis segmentation effect. The improved EWT together with Hilbert transform (HT) applied to the time–frequency analysis for the EMR waveform of rock fracture, and the time–frequency spectrum obtained by EWT–HT can well describe the time–frequency evolution characteristics. Compared with STFT and Hilbert–Huang transform (HHT), EWT–HT has significant advantages in time–frequency resolution and overcoming modal aliasing, providing a powerful tool for time–frequency analysis for the EMR waveform induced by rock fracture.

Establishing the relationship between the deformation of coal samples and acoustic emission response is the basis for the deformation prediction of coal samples. Using a combination of laboratory tests and theoretical analysis, acoustic emission tests of the uniaxial loading process were conducted on coal samples in the study area and the test results were analyzed, focusing on the rule of variation of acoustic emission counts with loading time. Based on the analysis of stress, strain, time, and acoustic-emission parameters variation, the relationship between the deformation of coal samples and acoustic emission response was established and analyzed. The analysis results show that during the loading process, the acoustic emission counts show the characteristics of stage changes, which can be divided into three stages: the initial stage with sporadic acoustic emission events, the middle stage with a stable increase of acoustic emission events, and the final stage with the rapid increase of acoustic emission events. This stage division has good consistency with the deformation stages of coal samples. Moreover, the acoustic emission counts have obvious and easily identifiable characteristics of changes in the deformation process of coal samples. The acoustic emission count can be used as a sensitive indicator in this study area to predict the deformation of coal samples. It provides a reference for the application of acoustic-emission prediction technology in this study area, which is important to improve the accuracy of geohazard prediction.