# A Status Evaluation of Rock Instability in Metal Mines Based on the SPA-IAHP-PCN Model
## 4 Results and Discussion
### Prospects and Limitations  
This study proposes a novel method for assessing the instability state of rock masses in metal mines using the SPA-IAHP-PCN model. By integrating multiple evaluation indicators, including cumulative number of events, cumulative microseismic event energy, cumulative apparent stress, and cumulative logarithmic seismic moment, the model effectively captures both static and dynamic risk of rock instability. The incorporation of SPA and PCN addresses the uncertainties in the assessment process, enabling dynamic predictions of instability trends and providing critical decision-making support for mine safety management. This approach facilitates proactive risk control measures, reduces the likelihood of instability incidents, and enhances the overall safety and economic efficiency of mining operations. Furthermore, the method demonstrates potential applicability in similar geological risk assessments, highlighting its value for broader adoption.  
However, several limitations and methodological constraints must be critically discussed. While the evaluation indicator system has made significant progress by incorporating critical dynamic parameters such as cumulative number of events, cumulative microseismic event energy, cumulative apparent stress, and cumulative logarithmic seismic moment, there remains potential for further enhancement. For instance, future research could expand the system by integrating additional factors, such as the physical and mechanical properties of rocks, groundwater conditions, and mining techniques, to further improve assessment accuracy and comprehensiveness. These refinements would build upon the current foundation, which already provides a robust framework for evaluating rock mass instability under dynamic conditions.  
Second, the reliance on expert judgment in the IAHP for weight determination introduces subjectivity, which may lead to methodological biases. While the IAHP-SPA approach reduces subjectivity to some extent, it does not eliminate it entirely. To address this limitation, future studies could explore alternative, more objective methods for weight determination, such as machine learning algorithms or statistical techniques that leverage historical microseismic data. These methods could provide a more robust and unbiased framework for parameter determination.  
Third, the PCN method assumes a \"worst-case scenario\" for connection coefficients during calculations. While this ensures reliability in risk predictions, it may result in overly conservative estimates, potentially leading to unnecessary safety measures or resource allocation. Future research should investigate reasonable ranges for these coefficients to enhance prediction accuracy and practicality.  
Lastly, the validation of the proposed model is limited to a single lead-zinc mine in Shaanxi, with a relatively small sample size. To ensure the generalizability and effectiveness of the model, broader application and validation across diverse mine types and geological conditions are necessary. This would also help identify potential variations in model performance under different mining environments.