Microseismic signals induced by mining activities often have low signal-to-noise ratios, and traditional picking methods are easily affected by noise, making accurate identification of P-wave arrivals difficult. To address this problem, this study proposes an adaptive denoising algorithm based on wavelet-threshold-enhanced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and develops an automatic P-wave arrival picking method incorporating fractal box dimension features, along with a corresponding accuracy evaluation framework. The raw microseismic signals are decomposed using the improved CEEMDAN method, with high-frequency intrinsic mode functions (IMFs) processed by wavelet-threshold denoising and low- and mid-frequency IMFs retained for reconstruction, effectively suppressing background noise and enhancing signal clarity. Fractal box dimension is applied to characterize waveform complexity over short and long-time windows, and by introducing fractal derivatives and short-long window differences, abrupt changes in local-to-global complexity at P-wave arrivals are revealed. Energy mutation features are extracted using the short-term/long-term average (STA/LTA) energy ratio, and noise segments are standardized via Z-score processing. A multi-feature weighted fusion scoring function is constructed to achieve robust identification of P-wave arrivals. Evaluation metrics, including picking error, mean absolute error, and success rate, are used to comprehensively assess the method’s performance in terms of temporal deviation, statistical consistency, and robustness. Case studies using microseismic data from a mining site show that the proposed method can accurately identify P-wave arrivals under different signal-to-noise conditions, with automatic picking results highly consistent with manual labels, mean errors within the sampling interval (2–4 ms), and a picking success rate exceeding 95%. The method provides a reliable tool for seismic source localization and dynamic hazard prediction in mining microseismic monitoring.

The stability of the surrounding rock is the key problem regarding the normal use of coal mine roadways, and the floor heave of roadways is one of the key factors that can restrict high-yield and high-efficiency mining. Based on the 1305 auxiliary transportation roadway geological conditions in the Dananhu No. 1 Coal Mine, Xinjiang, the mechanism of roadway floor heave was studied by field geological investigation, theoretical analysis, and numerical simulation. We think that the surrounding rock of the roadway presents asymmetrical shrinkage under the original support condition, and it is the extrusion flow type floor heave. The bottom without support and influence of mining are the important causes of floor heave. Therefore, the optimal support scheme is proposed and verified. The results show that the maximum damage depth of the roadway floor is 3.2 m, and the damage depth of the floor of roadway ribs is 3.05 m. The floor heave was decreased from 735 mm to 268 mm, and the force of the rib bolts was reduced from 309 kN to 90 kN after using the optimization supporting scheme. This scheme effectively alleviated the “squeeze” effect of the two ribs on the soft rock floor, and the surrounding rock system achieves long-term stability after optimized support. This provides scientific guidance for field safe mining.

Water-conducting fractured zone is the direct inducement of water inrush, water losing, and environmental deterioration in coal mines. How to predict the height of water-conducting fractured zone economically and accurately has always been the research difficulty of water-preserved mining. The paper selects the Meihuajing coal mine in Ningdong mining area as the engineering background. Firstly, transform the distribution law of the water-conducting fractured zone into a deterioration mechanism of coal-rock strength under the action of water-rock. Through laboratory tests, the water-rock coupling degradation law of rock mass under uniaxial action is revealed, and an intelligent statistical model of damage rate response under different water content is proposed. Secondly, based on the cross-scale elastic modulus calibration principle and the rate response intelligent statistical model proposed above, the borehole elastic modulus instrument is used to quantitatively characterize the strength characteristics of elastic modulus rate response law and field lithological parameters. Finally, based on the 18 samples of the water-conducting fractured zone, a height prediction model of a water-conducting fractured zone based on the measured value of elastic modulus is proposed by using the method of PSO-SVR. Taking $R2$ and RMSE as evaluation indexes, the error comparison between PSO-SVR and the empirical formula is carried out. Research indicates that, compared with the empirical formula, $R2$ of the PSO-SVR model increased by 18.3% and RMSE decreased by 92.7%. The predicted value of the PSO-SVR is consistent with the measured value, which significantly improves the prediction accuracy of the height of the water-conducting fractured zone. It provides a theoretical basis and technical support for the coordinated development of safe and efficient development of coal and ecological protection in Ningdong mining area.