During rock failure and instability, cracks usually appear as microcracks in local areas and then expand into significant macroscopic cracks. In this study, the whole process of rock deformation and instability under uniaxial loading is investigated with standard rock specimens, and acoustic emission (AE) and digital image correlation (DIC) technology are introduced to explore the process of rock failure and instability. AE technology is used to identify the location of crack propagation caused by microcracks and large cracks, and DIC is used to measure the crack propagation at different locations. Results show that the evolution of accumulated energy is closely related to the change in stress. When the specimen approaches failure, a “y” shaped localization zone is formed, and the evolution path is consistent with the through-through path of the crack, which better reflects the propagation law of the crack in the rock. The spatial distribution of the AE location event and energy density is consistent with the evolution path of the localization zone. The deformation value of the deformation field is closely related to the initiation and evolution of the deformation localization zone. On the basis of density-based spatial clustering of applications with a noise-clustering algorithm, AE positioning events are further processed and projected into the digital image of the deformation field, and the results of clustering projection are in good agreement with the deformation localization zone. Results show that AE and DIC coupling localization techniques can effectively identify the fracture process zone and fracture mechanism of rock, providing a new technical means for further studying the mechanical properties of rock materials.
Monitoring and providing warnings for coal mine rockburst disasters is a worldwide problem. Several rockburst accidents have occurred in a 1301 belt transport chute near a 1300 fully mechanized caving mine face. To address this issue, an empirical study of the occurrence mechanism of rockbursts in the adjacent area of the fully mechanized top-coal caving face was carried out. This paper mainly addresses the following issues: (1) based on microseismic monitoring technology, the distribution characteristics of the host-rock-supported pressure of the 1300 working face were measured, and the evolution and distribution of the deep-well caving working face host-rock-supported pressure were analyzed. It is revealed that the occurrence mechanism of rockburst in the adjacent area is actually caused by the evolution and superposition of the lateral abutment pressure of the 1300 stope, and the stress of the original rock along the 1301 belt transport down chute; (2) a theoretical calculation model of dynamic and static abutment pressure in longwall stope is built, and an example is tested. The results show that the peak position of lateral abutment pressure of the coal body outside the 1300 goaf is around 63 m, and the peak value of abutment pressure is around 47 MPa; (3) coal body stress monitoring, bolt dynamometer detection, and other means are compared and analyzed. At the same time, with the help of CT geophysical prospecting and drilling cutting measurements, it is concluded that the 1301 belt transport down chute is in the bearing pressure influence zone (superimposed zone), which further verifies the validity of microseismic analysis results and the accuracy of the above theoretical model. Based on this, the early warning system and prevention measures for rockburst based on microseismic monitoring are proposed. The engineering practice shows that the dynamic and static bearing pressure distribution and evolution law of the working face can be dynamically obtained by using microseismic technology, which provides a basis for the accurate prediction and treatment of rockbursts.
The purpose of denoising microseismic mine signals (MMS) is to extract relevant signals from background interference, enabling their utilization in wave classification, identification, time analysis, location calculations, and detailed mining feature analysis, among other applications. To enhance the signal-to-noise ratio (SNR) of single-channel MMS, a frequency-domain denoising method based on the Fourier transform, inverse transform, and singular value decomposition was proposed, along with its processing workflow. The establishment of key parameters, such as time delay, τ, reconstruction order, k, Hankel matrix length, n, and dimension, m, were introduced. The reconstruction order for SVD was determined by introducing the energy difference spectrum, E, and the denoised two-dimensional microseismic time series was obtained based on the SVD recovery principle. Through the analysis and processing of three types of typical microseismic waveforms in mining (blast, rock burst, and background noise) and with the evaluation of four indicators, SNR, ESN, RMSE, and STI, the results show that the SNR is improved by more than 10 dB after FSVD processing, indicating a strong noise suppression capability. This method is of significant importance for the rapid analysis and processing of microseismic signals in mining, as well as subsequently and accurately picking the initial arrival times and the exploration and analysis of microseismic signal characteristics in mines.
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