# Displacement Patterns and Predictive Modeling of Slopes in the Bayan Obo Open-Pit Iron Mine
## 2 Data and Methodology
### Data  
Radar monitoring data are stored in the cloud database in the form of point clouds. By selecting a date and querying via the web interface, the corresponding radar point cloud data are retrieved from the database, rendered into images, and projected onto a 3D model.  
Using the 2020 landslide event on the southern slope of the Bayan Obo Iron Mine's main pit as an example, a total of 114,283 radar displacement monitoring points were extracted. Each data point includes spatial coordinates \((X_{i},Y_{i},Z_{i})\) and a time series of pre-landslide displacement values. The displacement time series is recorded hourly from 00:00 on 27 August to 00:00 on 29 August 2020, resulting in an initial dataset with 3 spatial dimensions and 48 temporal dimensions.  
To meet DBSCAN's density uniformity assumption and prevent regions with large displacement values from dominating the clustering result, the displacement data were standardized using Equation (1). The StandardScaler was applied to ensure all displacement variables have a mean of 0 and a variance of 1, eliminating the effect of differing data scales:  
\[x_{\text{std}}=\frac{x-\mu}{\sigma} \tag{1}\]  
where \(x_{\text{std}}\) is the standardized displacement, \(\mu\) is the mean, and \(\sigma\) is the standard deviation.  
To eliminate unit differences among coordinate axes and avoid unequal weightings in distance calculations, the spatial coordinates were normalized using Equation (2):  
\[x_{\text{norm}}=\frac{z*(x-x_{\text{min}})}{x_{\text{max}}-x_{\text{min}}} \tag{2}\]  
where \(x_{\text{norm}}\) is the normalized spatial data, z is the scaling factor, x is the original spatial data from the time series, and \(x_{\text{max}}\) and \(x_{\text{min}}\) are the maximum and minimum values, respectively.  
After clustering the radar data, early warning units were identified. Subsequently, the selected time series were decomposed using VMD. Each decomposed component was then used as input for the GRU and LSTM models. Prior to model training, the data used for each model were preprocessed accordingly. For the GRU model, the dataset was divided into training and test sets in an 8:2 ratio. After partitioning, the input data were normalized using Equation (3):  
\[x_{\text{norm}}=\frac{x-\min(x)}{\max(x)-\min(x)} \tag{3}\]  
where \(\min(x)\) and \(\max(x)\) are the minimum and maximum values of the data, respectively.  
For the LSTM model, 70% of the data was used as the training set and 30% as the test set, conforming to conventional practices in time series forecasting. After partitioning, the current rainfall (from 27 August 00:00 to 29 August 00:00) and the prior two-day rainfall (from 25 August 00:00 to 26 August 23:59) were introduced as influencing factors. The VMD-decomposed periodic displacement component and the rainfall data were standardized using Equation (1) to enhance the stability of LSTM training. The data were then reshaped into the format (time steps \(\times\) features \(\times\) dimensions \(\times\) samples) to satisfy the input requirements of the LSTM model in MATLAB R2022a.