# Study on the Early Warning Methods of Dynamic Landslides of Large Abandoned Rockfill Slopes
## 1 Introduction  
In recent years, the construction volume of large underground projects in China has grown [1]. During underground engineering excavation, a large amount of rubble waste material is temporarily piled up near the project area to form a large-scale waste rockfill body. With the development and utilization of rubble waste material, the temporary waste material dump needs to be excavated and transported. Excavation disturbance inevitably causes slope failure. Therefore, the safety of waste rockfill slopes during excavation and transportation has become an important factor [2]. Slope deformation monitoring and landslide warning rank high among the most effective safety prevention and control measures [3]. Displacement monitoring forms the basis of landslide deformation monitoring. Slope displacement and its related parameters are important reference indicators for landslide early warning research [4]. In 1968, [PERSON] proposed the \"three-segment\" change rule of slope displacement-time curves [5]. Since then, scholars have conducted a vast amount of research on slope deformation monitoring and landslide warning from different perspectives and with different means. For instance, [PERSON] (1970) analyzed the displacement-time curve measured at the Chuquicanata mine and proposed an extension method to extrapolate the displacement-time curves and to predict the time of landslides according to the curve trend [6; 7; 8]. [PERSON] proposed the Verhulst inverse function model according to the characteristics of displacement-time curves [9]. [PERSON] et al. analyzed and studied many typical landslide displacement monitoring results and proposed a landslide early warning method using acceleration as the evaluation index [10]. In addition, many scholars have tried to combine various mathematical models with slope displacement-time curves. Such examples include the golden section method for early warning of the time of landslides [11; 12; 13], the markov early warning model [14; 15], the fuzzy mathematics early warning model [16; 17], the poisson cycle early warning model [18; 19; 20; 21], and the gradient-sinusoidal early warning model [20; 22; 23]. For natural landslides, the displacement vectors at various points on the landslide body can generally be kept consistent. Even under special circumstances, the deformation of each point in the initial stage of slope deformation is disordered, but, once entering deformation acceleration, the displacement of each point on the landslide body and the time curve also tend to be the same. Therefore, the displacement data of one or more monitoring points can be used as the basis for slope deformation analysis and landslide warning research [24]. However, the waste rockfill are massive and unconsolidated and feature dynamic excavation and transportation processes. In slope deformation monitoring, it is difficult to apply contact monitoring methods such as embedded sensors and GPS. Because of the granular structure characteristics of a rockfill body, the rubble particles on the slope will rotate during slope excavation, which leads to inconsistent deformation among the rubble. It is irrational to describe the displacement and warn of the deformation of the slope by a single point. Therefore, traditional slope monitoring and landslide warning methods are not applicable for large abandoned rockfill slopes. To effectively solve the problem of catastrophic events that may occur on the slopes of the rockfills, it is necessary to select appropriate monitoring methods, to describe the deformation process of slopes with scientific quantitative indicators, and to provide early warning for slope landslides. GB-InSAR (ground-based interferometric synthetic aperture radar) can provide full-field deformation information within the monitored area [25], which makes it possible to use the change of deformation region on the slope as the characteristic value to study the slope deformation.