## 6 Conclusions  
This study innovatively introduces a method for dynamically identifying the \(T_{SP}\) for the slope deformation acceleration. (1) By conducting an in-depth analysis of the normal distribution characteristics exhibited by the deformation velocity random variable in the uniform deformation stage of the monitoring data, we set an appropriate confidence interval to accurately identify the initial moment of accelerated deformation of the landslide, i.e., the \(T_{SP}\) location. This improvement helps to capture the signal of the landslide entering the acceleration stage earlier, thereby achieving a timely warning in terms of time prediction. (2) The \(\Delta t-\log(T)\) logarithmic model is constructed. By transforming the \(S\)-\(\log(T)\) coordinate system, the monitoring data are unified into a coordinate system with the same time dimension, solving the problem of fitting convergence of the data in the model, improving the robustness of the model, and ensuring the reliability of the prediction results. (3) By fully leveraging the high-precision and short-cycle monitoring advantages of ArcSAR radar technology, the latest displacement data of the landslide area are obtained in real time, and the parameters of the \(\Delta\)t-\(\log(T)\) prediction model are continuously updated and corrected. This dynamic adjustment mechanism ensures that the model remains synchronized with the actual evolution state of the landslide, enabling the predicted landslide occurrence time to more accurately approximate the actual landslide time, greatly improving the accuracy of the prediction. At the same time, the all-weather, large-area, and high-precision monitoring capabilities of ArcSAR radar ensure that accurate warning information for various parts of the mine slope can be continuously provided within hours to tens of hours before a potential landslide, providing strong technical support for effectively preventing landslide disasters.  
It should be noted that the method presented in this paper is applicable to landslide types characterized by creep deformation, such as translational landslides and progressive retrogressive landslides. However, there are certain limitations in predicting sudden-collapse and flow-type landslides caused by heavy rainfall. Furthermore, we also need to further enhance the robustness of the monitoring data to reduce the impact of noise signals on it. This requires improvements in radar equipment and noise data processing techniques.  
**Author Contributions:** Conceptualization, C.L. and L.W.; methodology, C.L., L.W. and J.Z.; software, J.W. and J.Z.; validation, C.L., L.W. and J.Z.; formal analysis, C.L., J.W. and J.Z.; investigation, C.L.; resources, C.L. and J.Z.; data curation, J.Z.; writing--original draft preparation, C.L. and J.Z.; writing--review and editing, C.L., L.W., J.W. and J.Z.; visualization, C.L. and J.Z.; supervision, L.W.; project administration, C.L.; funding acquisition, C.L. and L.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National R&D Key Research Program of China under grant 2022 YFC2904105.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** [PERSON] was employed by the Kambove Mining SAS, and [PERSON] was employed by the Changsha Smart Mining Inc., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.