Uniaxial cyclic loading-unloading compression experiments with schemes of six loading rates and six unloading rates were carried out on the combined testing platform. Impact of loading and unloading rates on rock AE characteristics was revealed. Results show that increasing loading and unloading rates resulted in decreasing total AE rings in the entire rock deformation and failure process. Increasing loading rate decreased the AE rings at loading stages, and increasing unloading rate decreased the AE rings at unloading stages in the same cycle. Total AE counts had a negative linear relationship to the loading and unloading rates. The loading stage was the active period of the AE phenomena, and impacts of the loading rate on the AE characteristics were more apparent. Especially when the loading rate was greater than 2.0 kN/s, brittle failure of rock specimens became noticeable. After the cyclic load reached the uniaxial compressive strength of 1/3∼1/2 times, the rock Felicity effect became more obvious. With the increase of the loading rate, the Felicity ratio decreased in the elastic stage and increased a little in the plastic stage, whereas with the increase of the unloading rate, the Felicity ratio decreased gradually in the elastic stage and remained almost the same in the plastic stage.

To reduce noise components from original microseismic waves, a comprehensive fine signal processing approach using the integrated decomposition analysis of the wave duration, frequency spectrum, and wavelet coefficient domain was developed and implemented. Distribution regularities of the wave component and redundant noise on the frequency spectrum and the wavelet coefficient domain were first expounded. The frequency threshold and wavelet coefficient threshold were determined for the identification and extraction of the effective wave component. The frequency components between the reconstructed microseismic wave and the original measuring signal were compared. The noise elimination effect via the scale-changed domain decomposition was evaluated. Interaction between the frequency threshold and the wavelet coefficient threshold in the time domain was discussed. The findings reveal that tri-domain decomposition analysis achieves the precise identification and extraction of the effective microseismic wave component and improves the reliability of waves by eliminating the redundant noise. The frequency threshold and the wavelet coefficient threshold on a specific time window are two critical parameters that determine the degree of precision for the identification of the extracted wave component. This research involves development of the proposed integrated domain decomposition method and provides a diverse view on the fine processing of the microseismic signal.

Based on the energy attenuation characteristics of residual wave in deep rock, a method was developed to determine the microseismic focus energy. Differential energy loss in infinitesimal spreading distance is logically deduced, upon which energy attenuation equation was established. With a logarithmic transformation, a linear relation of the residual seismic energy with distance is formulated. Its intercept was used to determine the microseismic focus energy. The result is compared with that determined by the energy density method. The reliability of the determined focus energy and the impact of the built-in velocity threshold on the residual wave energy computation are discussed. Meanwhile, the energy absorption coefficient used for representing the absorption characteristics of the rock medium in the mining region under study is also clarified. Key findings show that the microseismic focus energy confirmed by the residual wave attenuation is reliable. The result’s accuracy is quite high, especially for the events in deep rock with great homogeneity. The developed focus energy computation method is closely dependent on the integrity of waveform, accuracy of repositioning, and reliability of effective components extraction. The new method has been shown to be effective and practical.