Slope stability analysis based on the deformation monitoring data has been commonly used to predict and warn slope disasters. However, due to breakdown of the monitoring equipment or restrictions and interferences of internal and external factors in the area, the loss of data is unavoidable during the process of the slope monitoring. This problem can be solved by the spatio-temporal Kriging interpolation algorithm. However, the subjective factors and theoretical semi-variogram of variogram models, and many parameters estimation may lead to low interpolation precision and poor calculation efficiency. In this paper, a hybrid spatio-temporal interpolation algorithm was presented by combining the improved adaptive genetic algorithm (IAGA) with the spatio-temporal Kriging interpolation method, and it was applied to monitor the deformation of HP1 slope in Yuebao open-pit mine. The results showed that the interpolation precision of the proposed method was about 1 times higher than that of the traditional spatio-temporal Kriging method and the spatio-temporal method of Gaussian process regression method. In addition, after the interpolation analysis of the missing part of the monitoring data, the maximum cumulative displacement of all monitoring points was around 35.8 mm, and the rate of the displacement deformation didn't exceed 2 mm/d for three consecutive days. The deformation trend was basically consistent with the actual slope. It shows that the improved spatio-temporal Kriging interpolation algorithm (ISTKIA) has certain feasibility and reliability, and could provide a new idea for the research of related problems in related fields.

Due to the use of heavy industry equipment and frequent mining activities, the rock strata and the surface of Dexing Copper Mine were prone to settlement and deformation. In order to accurately analyze the time series settlement variation of the surface, this paper used Small Baseline Subset InSAR (SBAS-InSAR) technology to obtain 23 Sentinel-1A orbit SAR images covering Dexing Copper Mine for temporal settlement monitoring. The monitoring results show that there are three obvious settlement funnels within the mining area, and the total settlement area is about 7.63km2, located in the southwest of the Copper Factory, the southwest of Fujiawu Stope and between Duyang Lake and the Copper Factory, the maximum cumulative settlement in the area is -1131.9mm, and the maximum annual average settlement rate is -375.5mm/a. In order to analyze the change trend of settlement in mining area timely and accurately, make full use of monitoring data to carry out disaster prevention and warning in mining area, and solve the problem that the prediction accuracy of traditional settlement prediction model is not high, this paper proposes a time series settlement prediction model that takes into account the periodic and seasonal variation of settlement-EMD-ARIMA (Empirical Mode Decomposition- Autoregressive Integrated Moving Average Model), further, perform retrospective predictive analysis of the monitoring data. The results show that compared with the traditional GM (1,1) and ARIMA models, the average RMSE of EMD-ARIMA model is increased by 78.5% and 49.5%, and the average MAE is increased by 74.4% and 52.4%, respectively. The average MAPE increased by 81.2% and 47.9%, respectively. It shows that the EMD-ARIMA model has high prediction accuracy, strong stability and adaptability, and can be used to predict the time series data of mining settlement, which can provide scientific guidance for the safety production of mining areas.

There are many accidents of slope failure due to rainfall at home and abroad, which have caused heavy casualties and property losses. Stability analysis and evaluation of slope have become a research hotspot. Aiming at the slope stability under different rainfall, this paper takes Yuebao Open‐pit Mine slope as an example, has constructed a dynamic variable weight (VW) model of influencing factors on slope stability, and has analyzed the dynamic change and correlation of the slope indexes weights under different monthly maximum rainfall. On this basis, the dynamic evaluation model of slope stability has been established based on uncertainty measurement (UM), and the risk importance (RI) q which is proposed has quantitatively evaluated the risk degree of slope. The results show that under different monthly maximum rainfall the weight of each index is no longer a fixed value but a nonlinear and dynamic change law, and there is a strong correlation among the monthly maximum rainfall, internal friction angle, and cohesion. In addition, the q increases gradually with the increase of the monthly maximum rainfall, and the stability evaluation results are highly consistent with the HP1 slope, which have verified the reliability of the dynamic evaluation model. So, it can provide technical support for the safety production and management of open‐pit slope and can also provide reference for related research.