Dijun Rao

Microseismic P- and S-phase segmentation is an influential step that limits the accuracy of event location, parameter inversion, and mechanism analysis. Therefore, an improved Unet named PSSegNet is proposed to intelligently segment the P- and S-phases. The designed masks are used as the outputs of PSSegNet, which is used to obtain the time–frequency features of the P- and S-phases. As a result, the MSE (mean square error) between the predicted mask and the actual labeled mask is concentrated below 2.5, and the AE (accumulated error) of the reconstructed P/S-phase based on the predicted mask is concentrated below 1.0 × 10⁻³. Arrival picking results show that the overall error of the entire test set is less than 50 ms and most of the errors are less than 20 ms. Data with SNR (signal to noise ratio) < 2, 2 ≤ SNR < 3, PSR (P-phase to S-phase ratio) < 1, or 1 ≤ PSR < 2 in the dataset were selected for arrival picking and their errors were counted. The statistical results show that PSSegNet is robust at low SNR and PSR. The P- and S-phase segmentation based on PSSegNet has excellent potential for use in various applications and can effectively reduce the difficulty of obtaining the P/S-phase arrivals.

To reduce the workload and misjudgment of manually discriminating microseismic events and blasts in mines, an artificial intelligence model called PSO-ELM, based on the extreme learning machine (ELM) optimized by the particle swarm optimization (PSO) algorithm, was applied in this study. Firstly, based on the difference between microseismic events and mine blasts and previous research results, 22 seismic parameters were selected as the discrimination feature parameters and their correlation was analyzed. Secondly, 1600 events were randomly selected from the database of the microseismic monitoring system in Fankou Lead-Zinc Mine to form a sample dataset. Then, the optimal discrimination model was established by investigating the model parameters. Finally, the performance of the model was tested using the sample dataset, and it was compared with the performance of the original ELM model and other commonly used intelligent discrimination models. The results indicate that the discrimination performance of PSO-ELM is the best. The values of the six evaluation indicators are close to the optimal value, which shows that PSO-ELM has great potential for discriminating microseismic events and blasts. The research results obtained can provide a new method for discriminating microseismic events and blasts, and it is of great significance to ensure the safe and smooth operation of mines.

A 3D numerical model was presented to investigate the blast-induced damage characteristics of highly stressed rock mass. The RHT (Riedel, Hiermaier, and Thoma) model in LS-DYNA was used to simulate the blast-induced damage and its parameters were calibrated by a physical model test. Based on the calibrated numerical model, the influences of confining pressure and free surface span on the blast-induced damage characteristics were investigated. The results show that under uniaxial loading, the crater volume increases with confining pressure increases. The uniaxial static load can change the optimal burden and the critical embedding depth of charge. In stressed rock, the variation law of the crater shape affected by radial tensile fractures is opposite to that affected by reflected tensile fractures. Under the biaxial static load, the crater volume of the borehole placed on the side of the max static load is greater than the other side. The explosion crater can be improved by increasing the free surface span on the same side. Finally, it is suggested that the blasting efficiency can be improved by preferentially detonating the charge on the side of the max static load, and then the charge on the other side can be detonated with a wider free surface span.