## 4 Discussion  
1. The maximum subsidence amount estimated using D-InSAR technology is significantly lower than the actual subsidence amount, and it fails to capture the substantial gradient deformations within the subsidence basin. On the other hand, the DSM elevation derived from UAV oblique photogrammetry technology exhibits an accuracy of approximately 5 cm, enabling effective monitoring of the central region with large-gradient deformations in the mine subsidence basin. However, its monitoring accuracy decreases significantly toward the edges of the subsidence basin, rendering it incapable of precisely identifying the boundary of the subsidence basin.
2. The reliability of the BFGS algorithm for parameter inversion in the probability integration method under various error conditions was assessed through simulation experiments. Subsidence values were simulated using the probability integration method, and noise of 3 cm, 5 cm, and 10 cm was added. The BFGS optimization algorithm was then employed to invert the parameters. The results demonstrate that even with a data source error of 10 cm, the demand for probability integration parameter inversion was still met.
3. The fusion of two types of monitoring data, InSAR and UAV, was performed, and the accuracy of the fused subsidence measurements was validated to be within 10 cm when compared with the level data. This level of accuracy satisfies the requirements of the BFGS algorithm for parameter inversion in the probability integration model. The static prediction of subsidence at each monitoring point was performed using parameters inverted from the fused data and UAV data. This prediction was then  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Monitoring Method** & **q** & **tan\(\beta\)** & \(\theta_{0}\) & \(\mathbf{s_{1}}\) & \(\mathbf{s_{2}}\) & \(\mathbf{s_{3}}\) & \(\mathbf{s_{4}}\) & **RMSE (mm)** \\ \hline UAV & 1.16 & 1.97 & 86.21 & 31.65 & 77.34 & 22.6 & \(-\)82.74 & 168.10 \\ InSAR/UAV & 1.18 & 2.05 & 87.67 & 40.34 & 79.26 & 20.17 & \(-\)70.42 & 138.74 \\ Leveling & 1.18 & 2.09 & 88.52 & 39.52 & 84.63 & 10.79 & \(-\)80.48 & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameter inversion result.  
Figure 9: Comparison of fusion data and level data on strike line.  
compared with the leveling data. The comparison confirmed that the parameter inversion, which relied on the results of the synergistic monitoring of InSAR and UAV, yielded superior outcomes compared to employing single monitoring methods.  
This study leverages the advantages of InSAR for monitoring small deformations and UAV for tracking significant gradient deformations. This approach overcomes the limitations observed in prior research, which exclusively relied on a singular monitoring technique, thus hindering the attainment of high-precision and comprehensive subsidence basin data. Compared with conventional monitoring methods, this approach enhances operational efficiency while concurrently mitigating expenditures. The entire process of subsidence basin formation was meticulously monitored through the combined use of InSAR and UAV technology. The results of inverting the parameters of the probabilistic integral model based on fused data were found to be highly accurate and reliable., surpassing the precision achieved through the use of the UAV technique alone. These findings hold profound significance for the unmanned monitoring and prediction of surface subsidence disasters in mining regions.