## 4 Field Test
### Results and Discussion  
After the electromagnetic field intensity data were collected from the survey experiment, the three algorithms were employed to solve the highly sparse model, which can verify the superiority of the proposed algorithm by mutual comparison. First, the detection area was discretized by meshes, and the length of each grid was \(10.3\times 10.3\) m. The total number of grids was \(B=1470\). The area where the fault is not revealed in the roadway was set as the constraint for the AMPGA, and the value of the absorption coefficient was equal to the average of the entire area.  
After 500 iterations of evolution for the three algorithms, the reconstructed results of the detection area are as shown in Figure 10. The results showed that the proposed AMPGA with constraints has the best quality of the image reconstructed with the clearest outline. From Figure 10c, we can see that there are two large areas with a high absorption coefficient, from 170 to 320 m and 750 to 900 m, which show good correspondence with the exposed structure. Therefore, the exposed structure extends into the working face and penetrates the entire working face, which has a serious influence on mining. For the area of 310-750 m, the inversion result from the AMPGA with constraints shows that most of the area's absorption coefficient is low, which has basically no impact on the mining. However, the inversion result from the SGA has many scattered abnormal areas with a high absorption coefficient, which is hard to judge.  
At present, the survey area has been mined from left to right, mining has stopped at the position of 169 m due to the structure, and the open-off cut has been rebuilt at the position of 326 m. Finally, the coal thickness is too thin to continue mining because of the structure, and the mining has stopped at the position of 760 m. Therefore, the inversion result from the AMPGA with constraints is consistent with the real results.  
On the other hand, in order to test the stability of these three algorithms, multiple repetitive tests on different algorithms were performed, as shown in Figure 11. The curves obtained from the AMPGA with constraints are basically overlapping, which has the best stability. A comprehensive  
Figure 10: Reconstructed results according to the three algorithms after 500 iterations. **(a)** Inversion result from the SGA; **(b)** inversion result from the AMPGA; **(c)** inversion result from the AMPGA with constraints.  
analysis of the repeated experiments of these three algorithms is available, as shown in Figure 12. The average _RMSE_ of the SGA was reduced from 16.30 to 6.90 dB/m, the _RMSE_ of the AMPGA was reduced from 15.51 to 2.94 dB/m, and the _RMSE_ of the AMPGA with constraints was reduced from 15.97 to 2.57 dB/m. Therefore, the AMPGA with constraints has a better stability and accuracy than the other two algorithms.  
Furthermore, the SGA begins to converge, and the error remains basically stable after 200 generations of evolution. While the error of the AMPGA with constraints continues to decrease after 200 generations of evolution, it will not converge until after 450 generations. Therefore, the AMPGA with constraints has a better convergence effect than SGA.  
Figure 11: _Cont._