## 1 Introduction  
Landslide prediction is an indispensable part of geological disaster research, as it directly impacts public safety, ecological environments, and the stability of engineering facilities [1]. It can also cause settlements to be abandoned and lead to the 'ghost towns' phenomenon [2, 3]. In recent years, frequent landslides occurring globally have resulted in severe casualties and immense economic losses. According to a 2016 study by [PERSON] et al. [4], the annual economic losses due to landslides worldwide amount to approximately USD 19.8 billion, accounting for roughly 17% of the total annual losses from disasters globally. Drought-induced soil desiccation and structural weakening [5], coupled with slope destabilization from flood-induced saturation [6], collectively exacerbate the likelihood of landslides. This underscores the significance of research into landslide prediction. In particular, landslides occurring on the slopes of opencast mines can cause significant damage to mining operation areas and various engineering construction zones. Besides causing casualties, they also bring heavy economic losses to mining enterprises and impact their social image. For instance, on 22 February 2023, a particularly severe landslide accident occurred at an opencast coal mine in Alashan, Inner Mongolia, China, resulting in 53 fatalities, 6 injuries, and direct economic losses of USD 28 million. Landslide prediction faces numerous challenges, including the nonlinearity and uncertainty of slope deformation, the complexity of external factors, and the selection and deployment of monitoring equipment, as well as the choice of prediction models [7; 8; 9]. With the advancement of technology, more and more advanced techniques are being applied in the mining sector [10; 11; 12; 13; 14; 15]. Ground-Based Synthetic Aperture Radar (GB-SAR) has surpassed traditional GNSS (Global Navigation Satellite System) monitoring, satellite remote sensing monitoring, and total station monitoring technologies as the preferred monitoring equipment for mine managers due to its advantages of long-term real-time online monitoring, wide coverage, and immunity to weather conditions such as rain, snow, fog, and hail [16; 17]. GB-SAR can be classified into sliding-rail GB-SAR, arc GB-SAR, or MIMO GB-SAR based on its technological evolution [18]. Sliding-rail GB-SAR offers high spatial resolution but has limited monitoring range due to restrictions from the mobile platform and relatively low data acquisition efficiency. Arc GB-SAR boasts a wide observation range, short revisit cycles, and rapid response to surface changes. MIMO GB-SAR, however, has higher application costs due to its complex technology and process characteristics [19]. Landslide prediction theory has evolved from deterministic prediction models, such as the earliest empirical formula for landslide prediction proposed by Japanese scholar [PERSON] [20] based on creep theory of landslides, to statistical prediction models, like the GM (gray model), Kalman filtering, and curve regression models, and finally to nonlinear prediction models such as BP (Back Propagation) neural networks and catastrophe theory prediction models [21; 22]. To date, most research has focused on short-term prediction analysis using displacement monitoring data from the pre-critical accelerated deformation stage of landslides. By deeply mining and analyzing displacement monitoring data, corresponding physical-mechanical or mathematical models are established to understand the deformation characteristics of slopes in real-time, accurately assess the current safety status of slopes, and predict future deformation trends, thereby precisely estimating the specific time of instability and failure of rock and soil masses. Many scholars have dedicated themselves to research on landslide time prediction, leading to a series of observations and repeated experiments based on the strain or displacement variation over time.