# Study on the Early Warning Methods of Dynamic Landslides of Large Abandoned Rockfill Slopes
## 2 Project Overview
### Monitoring Results  
The monitoring lasted 69 days, during which time 11,376 observations were obtained and 72 shallow landslides (the sliding surface depth did not exceed 1 m) were recorded, including 41 small-area landslides that were destroyed within a short time by excavator disturbance. However, when the tops of some slopes were removed, it made it very easy to lose control, resulting in large-scale landslides. In total, 31 larger landslides were recorded, as shown in Figure 6, where the top of the slopes was removed. The records of the landslide area of the 72 landslides are shown in Figure 7, and the numbers represent the landslide areas, where the landslide area is the area of each landslide which was recorded during the field monitoring. The source of the final landslide area record was divided into two parts. Firstly, all the landslide area records that occurred during the day were recorded from the perspective of radar through visual estimation. Secondly, almost all the records of the landslide area from night to morning were obtained by the visual estimation of the construction workers standing near the bottom of the slope. Then, the landslide area visible to the radar could be calculated according to the relative position of the radar position, the construction personnel, and the landslide position. However, the landslide area records shown in this paper are estimated values of the landslide area from the perspective of radar. The landslide areas shown in Figure 7 are the final data that could be measured by radar. Figure 6: Position of the sliding surface. The 72 landslides records can be divided into two types according to the position of the sliding surface as A and B. Examples of the two types of landslide surfaces are shown on the right. Figure 7: Landslide areas (72 landslides). The landslide area is the area of each landslide recorded during the field monitoring. These data are not accurate measurements but intuitive estimates. Since this paper is based on IBIS-L measurement results for analysis, the area data in the figure represent the area of the landslide area that can be seen by the corresponding radar equipment. According to the field observation, the surface morphology of the 31 landslides can be divided into two types. One is the slope surface above the excavation location of the mechanical equipment, which has no apparent large particle size and dense agglomeration zone. The other is the slope with a significant dense zone (Figure 8). Due to rainfall, the rolling of construction vehicles, and the uneven particle sizes, some layers or areas of the rockfill have a higher density than the surrounding dense zone. Therefore, the 31 landslides were classified based on the surface shape of the slopes (Table 3). This article focuses on the abovementioned 31 landslides. First, according to the monitoring results of IBIS-L, the echo signals of each pixel were extracted. Then, we dealt with the pixels that could not meet the precision requirement. The displacement values of the remaining pixels in each monitoring period were calculated by Equation (1). According to the displacement values of existing pixel points, the interpolation method could obtain the displacement values of all pixel points in the area around each cleaned point. Finally, the contour of equal displacement could be generated based on the values. Considering the limitation of displacement monitoring accuracy of IBIS-L, the displacement cross-sectional area of \(-0.5\) mm was selected as the index for the landslide early warning research. The \(-0.5\) mm equal displacement cross-sectional areas of 31 landslides at different times were extracted, as shown in Figure 9.  
Figure 8: Slope with a significant dense zone. Figure 9: The area–time curve of the equal displacement cross section. The numbers in the legend represent landslide numbers recorded in chronological order. The “0” time represents the last scan before the severe landslides.