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.

As a critical enabler of trackless mining operations, load-haul-dump (LHD) vehicles rely heavily on efficient and safe path planning to support the industrial deployment of autonomous driving systems. However, existing planning algorithms often suffer from heavy computational demands, limited adaptability to the constrained kinematics of articulated LHDs, and insufficient robustness in complex underground environments. To address these challenges, we propose a Hybrid A* path planning method guided by high-level reference states. The method first integrates the A* algorithm with generalized Voronoi diagrams to rapidly generate a reference state set along tunnel centerlines. This reference serves as a structural guide for downstream search. Then, a region-aware dynamic node expansion strategy and a multi-objective heuristic cost function are introduced to improve planning efficiency and path quality. Comparative evaluations across three representative underground tunnel scenarios demonstrate that the proposed approach outperforms baseline methods in terms of safety, efficiency, and robustness, providing a viable solution for motion planning of LHDs in confined underground spaces.

Underground mining creates goafs, impacting resource extraction and safety. To address the issues of low efficiency and high reliance on manual operations in traditional techniques, this paper proposes a goaf scanning and modeling system (GSMS) for underground mine based on a quadruped robot. This system performs three-dimensional modeling of goafs through two main modules: frontend mapping and backend optimization, and evaluates the modeling accuracy using a multi-dimensional precision evaluation module. To validate the effectiveness of this system, both simulation and field experiments were conducted. The experimental results indicate that the goaf models established using GSMS achieved a level of accuracy comparable to traditional methods: the volume error of the constructed goaf models compared to the reference models was less than 3%, and the average profile contour similarity exceeded 98%.

In the field of slope landslide prevention and monitoring in open-pit mines, addressing the lag issues associated with the traditional GNSS inverse-velocity method, this study introduces a novel strategy that integrates high-spatiotemporal-resolution monitoring data from ArcSAR with a time log model for prediction. The key findings include the following: (1) This strategy utilizes the normal distribution characteristics of deformation velocities to set confidence intervals, accurately identifying the starting point of accelerated deformation. (2) Coupled with coordinate transformation, the time logarithm prediction method was constructed, unifying the units of measurement and resolving convergence issues in data fitting. (3) Empirical research conducted at the Kambove open-pit mine in the Democratic Republic of the Congo demonstrates that this method successfully predicts landslide times four hours in advance, with an error margin of only 0.18 h. This innovation offers robust technical support for slope landslide prevention and control in open-pit mines, enhancing safety standards and mitigating disaster losses.