## 5 Experiments and Results
### Results
#### 5.3.2 Field Experiments  
The experimental site was located in Huayin City, Shaanxi Province, where the integrated exploration system was systematically assessed for performance. A 7 m \(\times\) 10 m test area was designated, characterized by a lack of vegetative cover and a series of terrain undulations with vertical variations of approximately 30 cm. A joint experiment was conducted employing the laboratory-developed UAV-borne magnetic detection system (UAV-MAG) in combination with an integrated UAV-based time-domain electromagnetic detection system (UAV-TDEM). Within the test area, two steel targets were precisely positioned at coordinates (4.49, 7.19, \(-\)0.80) and (3.32, 3.76, \(-\)0.40), with respective dimensions of \(\Phi\)8 \(\times\) 28 cm and \(\Phi\)6 \(\times\) 18.5 cm.  
Figure 11 illustrates the electromagnetic and magnetic responses within the survey area, clearly highlighting the responses of the two targets. The vertical axis represents the northward position, meaning the top of the figure corresponds to the northernmost area in the survey. The horizontal axis represents the eastward position, ensuring a conventional north-up orientation as commonly used in maps. In the electromagnetic response, only the \(Z\)-component was collected, resulting in exclusively positive values. Due to the rapid attenuation of the electromagnetic response, the anomaly regions are relatively compact. In contrast, the magnetic response exhibits both positive and negative anomalies, attributed to the relative orientation between the targets and the geomagnetic field.  
Given that our simulation region spans the range \([3.5,6.5]\), we initially rescaled the area to ensure alignment with the defined simulation boundaries. Subsequently, two sets of data were input into MagEMNet for inversion, with the results presented in Table 4. Figure 12 shows the predicted values for two targets with corresponding error bars. The visualization highlights the differences between the predicted parameters for both targets, including spatial and angular metrics.  
Figure 11: Electromagnetic and magnetic response of survey area: (**a**) electromagnetic survey response; (**b**) magnetic survey response.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Coefficient of** & \multirow{2}{*}{**Explained Variance (EV)**} & \multirow{2}{*}{**MSE**} & \multirow{2}{*}{**MAE**} \\  & **Determination (\(R^{2}\))** & & & \\ \hline DNN & 0.853 & 0.824 & 1.152 & 0.552 \\ LM & 0.658 & 0.634 & 1.562 & 0.854 \\ BAD & 0.781 & 0.755 & 1.359 & 0.635 \\ Ours & **0.929** & **0.927** & **0.956** & **0.483** \\ \hline \hline \end{tabular}  
* The best results are highlighted in bold in the table.  
\end{table}
Table 3: Quality assessment metrics comparison on our dataset.  
The inferred coordinates of the two target locations closely approximate the actual values, with depth errors within 10 cm. Target 1, having relatively larger dimensions, resulted in a higher \(L_{3}\) value compared to Target 2. Additionally, both targets exhibit symmetry, as indicated by the similarity between the inverted \(L_{1}\) and \(L_{2}\) values. These findings confirm the proposed method's high accuracy and robust generalization capability.  
Figure 12: Comparison of predicted values with errors for different parameters: (**a**) \(x,y,|z|\), (**b**) \(L_{1},L_{2},L_{3}\), and (**c**) \(\alpha,\beta\).