Fine Identification of Landslide Acceleration Phase Using Time Logarithm Prediction Method Based on Arc Synthetic Aperture Radar Monitoring Data

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.