## 5 Discussion
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **DOFS Monitoring** & **Traditional Stress Monitoring Methods** \\ \hline \multirow{3}{*}{Accuracy} & High accuracy due to the use of advanced & Generally lower accuracy compared to DOFS, \\  & signal processing techniques and & limited by the point measurement nature and \\  & multi-parameter sensing capabilities & susceptibility to environmental factors \\ \hline \multirow{3}{*}{Data Resolution} & High spatial resolution (can be as fine as 1 \\  & meter or less) along the entire length of & Lower spatial resolution and typically limited \\  & the fiber & to point measurements at specific locations \\ \hline \multirow{3}{*}{Practicality} & Suitable for long-distance and large-scale & Limited to shorter distances and smaller \\  & monitoring, immune to electromagnetic & scales, susceptible to electromagnetic \\  & interference, and can operate in & interference, and may require \\  & harsh environments & more maintenance \\ \hline \end{tabular}
\end{table} 
Table 2: Main differences between DOFS monitoring and traditional stress monitoring methods. instability; and employing multiple monitoring techniques such as BOTDA, DIC, and BPPS for cross-validation to enhance the credibility of inversion results. The physical model experiments in this study represent a significant advancement in validating theoretical frameworks for mining-induced stress distribution. The experimental design carefully incorporates multiple monitoring technologies (BOTDA, DIC, and BPPS) to provide comprehensive validation across different advancement stages, with particular attention to the critical layer deformation characteristics. The model's ability to capture both continuous and discontinuous deformation regimes, while maintaining measurement accuracy within 8.88% deviation, demonstrates its robust reliability. Furthermore, the observed \"U-shaped\" pressure distribution pattern and the temporal evolution of strain profiles align remarkably well with theoretical predictions, especially in the transition zones between intact and fractured rock masses. The correlation between sensor data from different monitoring systems provides strong cross-validation of the measurement methodology, while the consistent strain threshold of 4000 \(\mu\varepsilon\) for fracture initiation offers a reliable benchmark for safety monitoring. However, this method still faces several challenges and limitations in practical applications. The primary issue is the difficulty in accurately quantifying the coupling efficiency between optical fibers and rock mass, which directly affects strain transfer effectiveness and inversion accuracy. Secondly, the current model assumes the rock mass to be a homogeneous and isotropic medium, without fully considering the anisotropic characteristics of geological bodies in actual engineering. Furthermore, this method primarily focuses on quasistatic processes and lacks consideration of rapid response characteristics under dynamic mining disturbances. Additionally, the inherent scale effect of physical model tests introduces certain uncertainties when  
Figure 13: Extended comparison. (**a**) Advance to 124 cm, (**b**) Advance to 164 cm, (**c**) Advance to 192 cm, (**d**) Advance to 232 cm. translating experimental results into engineering practice. These issues require in-depth investigation and resolution in future research. The proposed methodology represents several key advancements when compared to the existing state-of-the-art approaches in mining stress monitoring and analysis. Current leading methodologies typically rely on single monitoring systems such as traditional stress meters or conventional fiber optic sensors, which often provide limited spatial resolution and lack the ability to capture the dynamic evolution of stress fields comprehensively.