## 4 Numerical Experiment and Analysis  
The MPGA-SIRT algorithm was implemented on a PC (3.3 GHz CPU, 8.0 G memory) to demonstrate its effectiveness, and all algorithms were implemented in the MATLAB 2017b programming environment (MathWorks Inc., Natick, MA, USA, 2017). A 600 m long and 200 m wide model was established according to the shape characteristics of the coal mine working face, as shown in Figure 4. Considering that the differences in the absorption capacities of electromagnetic waves between the coal seam and the geological anomaly medium provide the basis for the inversion calculation of geological anomalies at the working face, the electromagnetic wave absorption coefficient of the abnormal area was set to \(\beta_{1}\) = 1 dB/m, and that of the rest of the normal area was set to \(\beta_{2}\) = 0.5 dB/m according to the previous project detection experience, which could verify the feasibility of the prediction method proposed in this research. The observation system had a 30 m separation between transmitting stations and a 10 m separation between comparable receiving locations. A total of 420 transmission rays were captured in total. Prior to the tomographic inversion calculation, the detection area was first discretized into grids, and the size of each grid was 10 \(\times\) 10 m. The detection area was planned to be divided into \(A\) = 1200 grids, and the absorption coefficient value represented by these 1200 grids was the target requiring solution via inversion. In order to assess the efficacy of the hybrid intelligent optimization algorithm of MPGA-SIRT proposed in this paper, this research reversed the geological anomaly of the working face shown in Figure 5 using three different reconstruction models, including the SIRT conventional linear reconstruction algorithm, SGA-SIRT, and the MPGA-SIRT hybrid intelligent algorithm. After 500 generations of iterations, the final convergence for the objective function \(f\) was 8.033 dB/m based on the SIRT, 15.451 dB/m based on SGA, and 5.882 dB/m based on MPGA, as shown in Figure 6a. Therefore, the MPGA had stronger global search performance compared to SGA. Subsequently, the absorption coefficient matrix \(X_{0}\) corresponding to the convergence based on SGA and MPGA intelligent algorithms was used as the initial model of the SIRT, respectively, and iterative inversion was continued. The convergence results of the final objective function \(f\) were 3.448 dB/m and 3.071 dB/m, respectively, as shown in Figure 5b,c. The optimal solution \(X\) of the objective function obtained at the time of convergence based on the three different reconstruction algorithms of SIRT, SGA-SIRT, and MPGA-SIRT was imaged using tomography, as shown in Figure 5. As a result, the MPGA-SIRT hybrid intelligent reconstruction's distribution of geological anomalies is most compatible with the model's setting region. Therefore, the introduction of intelligent algorithms could effectively improve the search performance of the SIRT for the objective function. Stronger global and local search capabilities of the MPGA-SIRT hybrid intelligent algorithm can increase the inversion and prediction accuracy of the geological anomaly layer within a working face. Figure 4: Distribution diagram of the model construction. In addition to accuracy, stability is also an important indicator of a model's performance. Therefore, in order to verify the stability of the proposed hybrid intelligent algorithm, five repetitive reconstruction operations were performed on the anomalous area of the working face. The evolution curves of the objective function based on different reconstruction models are shown in Figure 7, and the convergence results are shown in Figure 8.  
The standard deviation \(S\), as a measure of statistical data dispersion, can well evaluate the stability of each reconstructed model, which is defined as follows.