# Displacement Patterns and Predictive Modeling of Slopes in the Bayan Obo Open-Pit Iron Mine
## 1 Introduction
They analyzed the frequency variations in SBAS-InSAR-derived subsidence time series, identifying low-frequency signals to locate zones of stable subsidence and using high-frequency components to capture the frequency and amplitude of sudden deformation events. [PERSON] et al. [15] applied wavelet analysis to decompose landslide displacement series into components with distinct frequency characteristics, based on an analysis of the chaotic nature of the displacement time series. [PERSON] et al. [16] investigated the displacement sequence at monitoring point D3 of the Baijiabao landslide, decomposing the original displacement series into low-, medium-, and high-frequency components using wavelet transform; the lowest frequency term was treated as the trend component, while the sum of the remaining terms represented the periodic component. [PERSON] et al. [17] adopted empirical mode decomposition (EMD) to divide cumulative displacement into trend and periodic components, successfully extracting rainfall-related intrinsic mode functions (IMFs), which improved the performance of subsequent Long Short-Term Memory (LSTM)-based prediction. [PERSON] et al. [18] proposed a soft-sifting-criterion-optimized EMD approach, integrating K-means clustering and an FOA-LSSVM model to effectively predict \"step-like\" landslide displacements. While the model enhanced both the interpretability and accuracy of prediction, the method required complex parameter optimization. [PERSON] et al. [19] focused on the deformation characteristics of step-like landslides and used EMD to separately decompose multi-point monitoring data into trend and periodic components. To address the non-stationarity of displacement data, [PERSON] et al. [20] employed ensemble empirical mode decomposition (EEMD) and embedded the decomposed features into a Convolutional Neural Network (CNN)-LSTM hybrid model, significantly boosting prediction accuracy. [PERSON] et al. [21] utilized EEMD to reconstruct displacement series into physically interpretable trend and fluctuation components for spoil slopes with nonlinear and small-sample characteristics, thereby improving prediction robustness. [PERSON] et al. [22] applied EEMD to decompose the surface displacement time series of the Baishuile landslide into trend and fluctuation components, further illustrating the effectiveness of the method. Although these methods have achieved certain successes, they also exhibit notable shortcomings: for example, wavelet analysis relies on the empirical selection of basis functions, which limits its ability to capture the non-stationary characteristics of slope displacement and may result in energy leakage. EMD employs an adaptive decomposition approach that overcomes the dependency on predefined basis functions, but it still suffers from severe mode mixing and end effects, which can distort the decomposition results. EEMD introduces white noise and performs ensemble averaging to effectively mitigate mode mixing, yet it incurs high computational costs, and residual noise may still interfere with the accuracy of low-frequency components. Therefore, to address the limitations of the existing methods, after identifying early warning units using the DBSCAN algorithm, this study adopts Variational Mode Decomposition (VMD) [23] to decompose the landslide displacement within these units into trend and periodic components with clear physical interpretations [24]. Compared to traditional methods, such as wavelet analysis, EMD, and EEMD, VMD offers superior adaptability and frequency-domain constraints, effectively mitigating mode mixing while ensuring stable and controllable decomposition outcomes--making it well suited for handling non-stationary and nonlinear slope displacement data. In terms of prediction models, traditional machine learning methods have been widely used. [PERSON] et al.