# Displacement Patterns and Predictive Modeling of Slopes in the Bayan Obo Open-Pit Iron Mine
## 1 Introduction  
From the 1990s to the early 21 st century, with the continuous exploitation and utilization of resources, deep concave open-pit mines have become a development trend for open-pit mining worldwide [1]. Because of the lack of effective landslide early warning methods, it is often impossible to issue timely warnings before landslides occur, resulting in severe casualties and economic losses, which seriously affect mine production and people's daily lives. Traditional monitoring technologies--such as GNSS [2], 3D laser scanning [3], and total stations [4]--are limited by their single-point data acquisition modes [5, 6] and delayed data processing mechanisms [7, 8, 9], making it difficult to fully capture the spatiotemporal evolution characteristics of slope deformation. In recent years, slope radar has emerged as an important technical means for identifying, determining, and predicting landslide deformation fields because of its advantages of high-precision measurement, large-scale monitoring capability, and all-weather microwave remote sensing technology [10, 11, 12]. Existing early warning models primarily emphasize the temporal variation in displacement [13] while neglecting spatial correlations between monitoring points. In practical engineering applications, this often leads to insufficientaccuracy in identifying deformation zones and an inability to accurately reflect the spatial distribution of slope deformation. Moreover, current methods lack consideration of the detailed evolution characteristics of displacement, making it easy to misinterpret isolated outliers caused by local disturbances or equipment errors as deformation signals. Such misjudgments not only increase the false alarm rate but may also obscure actual deformation trends, thereby undermining the reliability of early warning results. Therefore, to address this issue, this study employs the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to identify and select clusters of monitoring points with significant displacement and a certain degree of spatial distribution as early warning units. The analysis of displacement curves within early warning units is complicated by the significantly non-stationary nature of slope displacement sequences, which results from the coupling of topographic, structural, and hydrogeological factors. Their evolution involves two key dynamic mechanisms: (1) long-term trend deformation dominated by geological structural stress, representing the long-term evolution of time-dependent damage accumulation in the rock mass, and (2) quasi-periodic responses induced by cyclical environmental disturbances (such as rainfall infiltration), reflecting the recurring effects of seasonal hydrological cycles. This multi-scale coupling feature poses fundamental challenges to traditional single-model prediction approaches: when a single model is used to directly fit the original displacement sequence, high-frequency noise can mask the underlying trend, while low-frequency trends may interfere with the extraction of periodic features, ultimately limiting the generalization capability of the predictive model. To address this issue, some researchers have proposed a \"decomposition-prediction-reconstruction\" framework, in which landslide displacement is first decomposed into different components, separate models are constructed for each component, and the results are finally reconstructed by summation to improve both prediction accuracy and model adaptability. In the decomposition of displacement, traditional methods typically use wavelet analysis, empirical mode decomposition (EMD), and ensemble empirical mode decomposition (EEMD). For example, [PERSON] et al. [14] introduced wavelet analysis in their study of surface subsidence pattern recognition in underground metal mining areas.