## 6 Conclusions  
In this study, a novel MPGA-SIRT hybrid intelligent reconstruction algorithm is introduced to evaluate the geo-environmental conditions within a working face. The efficacy of the proposed method was substantiated through both numerical simulations and field measurement tests. This work contributes to a deeper understanding of the effective reconstruction mechanisms involved in geo-environmental conditions within an underground closed space. In summary, the following conclusions can be drawn based on the results of this study:  
(1) Given the limitations of projection data, anticipating the anomalous areas within the working face becomes challenging. In this scenario, the coupled algorithm of MPGA and SIRT can be employed to reconstruct the geo-environmental conditions within a working face, even with insufficient projection data. (2) The MPGA outperforms the SGA in terms of accuracy and stability through the coevolution of multiple populations, providing a more rational initial model for the SIRT algorithm and mitigating its dependence on the initial model flaw. (3) The MPGA-SIRT hybrid intelligent algorithm, combining the robust global evolutionary ability of the MPGA intelligent algorithm with the local search characteristics of the SIRT linear reconstruction algorithm, proves effective in enhancing the accuracy of reconstructing geo-environmental conditions within a working face. In conclusion, the algorithm presented in this paper, with its distinctive combination of a multiple-population genetic algorithm (MPGA) and simultaneous iterative reconstruction technique (SIRT), effectively resolves issues faced by traditional linear reconstruction techniques. The MPGA-SIRT model overcomes the constraints of underground confined spaces, providing a more precise initial inversion model for geological environments within working faces based on electromagnetic wave tomography theory. Simultaneously, this algorithm ingeniously utilizes the global search capability of the MPGA and the local search properties of the SIRT, significantly enhancing the accuracy and stability of the inversion process. Validated through numerical modeling, theoretical testing, and practical applications in actual mining areas, this algorithm exhibits superior performance in terms of objective function convergence and tomography results compared to conventional methods. Therefore, it offers a novel and accurate means for assessing mine geology while providing scientific and reliable data support for engineering planning, design, construction, and other phases. This contributes to ensuring environmentally friendly and efficient mining practices, mitigating potential geological risks and enhancing the safety and stability of engineering operations. In light of the successful applications of the MPGA-SIRT model in reconstructing geo-environmental conditions within a working face, the refinement of the MPGA-SIRT algorithm could involve integrating advanced machine learning techniques to further enhance its ability to handle complex and dynamic geo-environmental data. This could lead to more precise and real-time monitoring of underground conditions, thereby improving the safety and efficiency of mining operations. Moreover, the model could be expanded to include a broader range of geophysical parameters, such as seismic wave velocities or electrical resistivity, to provide a more comprehensive assessment of the subsurface environment. By pursuing these avenues, our research can continue to contribute to the