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.

The recovery and comprehensive utilization of tailings resources can effectively mitigate or eliminate safety hazards in the upper zones of open-pit mines. To ensure the safe recovery of accumulated tailings and enhance resource utilization efficiency, this study establishes a two-dimensional model based on the Discrete Element Method (DEM) for the overall stability of tailings recovery, which is integrated with the existing slope and ore pillar models of the open-pit mine. Leveraging the mechanical parameters of tailings and waste rock obtained from laboratory tests, this study systematically investigates the effects of tailings recovery on the stability of existing slopes. Results show that due to differences in fracture characteristics and tailings reserves, complete tailings extraction causes no landslides in some sections, but large-scale or varying landslides occur on southern/northern flank slopes in specific sections at certain excavation depths or after full extraction. Targeted recovery recommendations are proposed: “segmented excavation with bench reservation” prevents overall landslides on southern flank slopes of landslide-prone sections; 35° slope cutting ensures stability of northern flank slopes in all sections. Further field verification considering rainfall and seismic loading factors is required for practical applications.

The recovery and utilization of tailings can reduce safety risks in open-pit mines while improving resource efficiency. This study develops a stability model for tailings recovery based on current slope and ore pillar conditions, incorporating mechanical parameters of tailings and waste rock from laboratory tests. Results indicate that complete extraction of Tailings Ore Body No. 1 in Section 1 caused no landslides, whereas excavation to 35 m in Section 2 triggered large-scale sliding on the southern slope. In Sections 3–7, northern waste rock slopes experienced varying degrees of failure after full extraction. To ensure stability, Section 2 should adopt a stepped scheme: after mining 15 m at a 20° slope angle, excavate an additional 10 m, reserve a 10 m bench, and repeat to the ore body bottom. This approach prevents southern slope failure while stabilizing the system. For northern slopes, applying a 35° slope angle effectively eliminated sliding across all profiles.

The spatial distribution of strata under nappe tectonic conditions is highly complex. Conventional approaches, such as dense borehole exploration or intensive in situ investigation, are often costly and difficult to implement for revealing detailed stratigraphic structures. To address this issue, this study focuses on the nappe tectonic setting of the main orebody in the Kambove mining area and proposes a spherical radial basis function interpolation method incorporating spatial anisotropy to construct a three-dimensional resistivity model, thereby providing a data foundation for subsequent intelligent stratigraphic identification. First, the discrete resistivity measurement points were processed through coordinate unification, elevation correction, and data quality inspection. Then, based on radial basis function interpolation theory, a spherical kernel function and anisotropic ellipsoidal constraints were introduced to achieve the three-dimensional continuous representation of discrete resistivity data. Finally, the interpolation performance of the proposed method was compared with that of linear RBF interpolation and inverse distance weighting with $P=2$ and $P=3$ using random holdout validation. The spherical RBF method yielded an ME of −12.2 Ω·m and the lowest RMSE of 299.0 Ω·m, corresponding to RMSE reductions of 13.6–22.0% relative to the comparison methods. These results indicate that the spherical RBF method provides better local interpolation performance within areas covered by existing measurements. The proposed method preserves the continuity and smoothness of the resistivity field and enhances its representation along the dominant geological structural direction, thereby providing a continuous three-dimensional resistivity basis for subsequent intelligent stratigraphic identification.