## 3 Results  
This study employed the method outlined in Section 2.2 to utilize D-InSAR technology by processing a dataset comprising 38 SAR images taken between 10 April 2021 and 28 June 2022. This process resulted in the creation of a time-sequence deformation map for the study area, which allowed for the extraction of subsidence measurements at various time points for each monitoring point. The collected data were then superimposed to derive the overall subsidence values for the monitoring points. The analysis of the monitoring results revealed that the maximum subsidence value observed in the study area was \(-\)0.7964 m, significantly smaller than the maximum subsidence value of \(-\)2.5171 m obtained from the leveling measurements. Consequently, exclusive reliance on the D-InSAR technique proved insufficient for effectively monitoring the substantial gradient deformation in the central sedimentation basin of the mine and accurately determining the predicted parameters. The study area's DSM for the two periods, 12 April 2021 and 28 June 2022, was obtained using UAV oblique photogrammetry, as described in Section 2.3 of this paper. Elevation values of the monitoring points were extracted for each time period. The difference in \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Group Number**} & **10 cm-** & **5 cm-** & **3 cm-** \\  & **RMSE/mm** & **RMSE/mm** & **RMSE/mm** \\ \hline
1 & 6.56 & 6.42 & 4.17 \\
2 & 7.26 & 2.20 & 2.66 \\
3 & 18.94 & 2.91 & 3.05 \\
4 & 7.22 & 4.94 & 3.22 \\
5 & 5.84 & 2.90 & 2.88 \\
6 & 7.40 & 2.94 & 3.10 \\
7 & 9.71 & 6.42 & 2.02 \\
8 & 6.09 & 4.15 & 2.28 \\
9 & 15.19 & 3.51 & 2.96 \\
10 & 11.76 & 4.02 & 4.15 \\ Mean & 9.60 & 4.04 & 3.05 \\ Maximum & 18.94 & 6.42 & 4.17 \\ Minimum & 5.84 & 2.20 & 2.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RMSEs of subsidence value calculated by each group of parameters. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Initial** \\ **Parameters** \\ \end{tabular} } & **q** & **b** & **tan\(\beta\)** & \(\theta_{0}\) & \(\mathbf{s_{1}}\) & \(\mathbf{s_{2}}\) & \(\mathbf{s_{3}}\) & \(\mathbf{s_{4}}\) \\  & **1.2** & **0.22** & **2.37** & **88** & **50** & **50** & **50** & **50** \\ \hline
10 cm & RMSE & 0.0313 & 0.0028 & 0.0234 & 0.1232 & 6.2651 & 5.9499 & 6.4140 & 4.3044 \\  & Pre/\% & 2.6059 & 1.2774 & 0.9893 & 0.1400 & 12.5302 & 11.8999 & 12.8280 & 8.6088 \\  & RMSE & 0.0121 & 0.0010 & 0.0140 & 0.0553 & 4.3701 & 5.1554 & 5.6315 & 5.9693 \\
5 cm & Pre/\% & 1.0061 & 0.4379 & 0.5896 & 0.0629 & 8.7401 & 10.3107 & 11.2631 & 11.9386 \\  & RMSE & 0.0101 & 0.0005 & 0.0065 & 0.0601 & 4.1432 & 4.6900 & 5.7843 & 6.5734 \\
3 cm & Pre/\% & 0.8424 & 0.2315 & 0.2725 & 0.0683 & 8.2863 & 9.3800 & 11.5685 & 13.1468 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RMSEs of each parameter and percentage. elevation between the two periods represents the total settlement of the monitoring points. The monitoring results reveal a maximum settlement value of \(-2.5416\) m in the study area, with a negligible difference of only 0.02 m between the results of the level measurements. This observation underscores the advantages of employing UAV oblique photogrammetry technology for monitoring large-gradient deformations in the mining area. The literature analysis and verification [29] indicate that the elevation error of monitoring points in the study area, obtained through UAV oblique photogrammetry, is approximately 5 cm. This level of accuracy is sufficient for the inversion of mining subsidence parameters. The settlement of the fused monitoring points was determined by integrating the two types of monitoring data, as per the procedure outlined in Section 2.4 of this article. Subsequently, the settlement was compared with the leveling data, as depicted in Figures 8 and 9. Following the analysis, it was found that the absolute error between the settlement of the fused monitoring points and the measured leveling settlement fell within a range of 10 cm.