# On the Development of a Neural Network Architecture for Magnetometer-Based UXO Classification
## 5 Results
### Resilience Analysis  
The final part of our study focused on the resilience of the proposed neural network compared to the Random Forest classifier. We evaluated model performance against three types of signal distortions--additive noise, time warping, and linear drift--as described in Section 4.4. It is important to note that the results reported in this section were obtained on a single fixed test set to ensure a consistent comparison across different distortion levels, rather than through cross-validation. Figure 4 illustrates the impact of additive noise on model performance. On the clean, unaltered test set, the Random Forest classifier slightly outperforms the neural network. However, its performance collapses precipitously with the introduction of even a small amount of noise, quickly approaching a balanced accuracy of 50% (the equivalent of random guessing). In stark contrast, the neural network demonstrates significantly greater stability. Its balanced accuracy decreases by only two percentage points at a noise level of 0.01 and remains relatively stable thereafter, dropping only by another two points as the noise level increases to 10%. This robust behavior indicates that the proposed neural network is well-suited for real-world applications where signal noise is inevitable. In practice, the impact of such noise could be further mitigated by applying a pre-processing step, such as a moving average filter. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Configuration** & **Accuracy** & \
\begin{tabular}{c} **Balanced** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **F1\({}_{macro}\)** \\ \end{tabular} & **Hiden dim.** & **MF dim.** & **FC** \\ \hline SC + BN & 87.72 \(\pm\) 1.32 & 84.78 \(\pm\) 1.45 & 84.89 \(\pm\) 1.61 & [32,128,512] & 4 & 100 \\ \hline no SC + BN & 79.74 \(\pm\) 3.36 & 71.67 \(\pm\) 8.81 & 71.87 \(\pm\) 9.84 & [32,128,512] & 4 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The influence of skip connections (SCs) and batch normalization (BN) on neural network performance. “Hidden Dim.” refers to the number of filters in the convolutional blocks. “MF Dim.” indicates the number of neurons in the metadata processing layer, where 0 signifies that no metadata was used. “FC” is the number of neurons in the final fully-connected layer. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Input** & **Accuracy** & \
\begin{tabular}{c} **Balanced** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **F1\({}_{macro}\)** \\ \end{tabular} & **Hiden dim.** & **MF dim.** & **FC** \\ \hline X,Y,Z,M & 88.60 \(\pm\) 2.29 & 87.10 \(\pm\) 2.30 & 86.51 \(\pm\) 2.36 & [32,128,512] & 4 & 100 \\ \hline M & 87.72 \(\pm\) 1.32 & 84.78 \(\pm\) 1.45 & 84.89 \(\pm\) 1.61 & [32,128,512] & 4 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The influence of the number of magnetic field components (X, Y, Z, M vs. M) on neural network performance. “Hidden Dim.” refers to the number of filters in the convolutional blocks. “MF Dim.” indicates the number of neurons in the metadata processing layer, where 0 signifies that no metadata was used. “FC” is the number of neurons in the final fully-connected layer. Next, we evaluated resilience to time warping, which simulates variations in survey speed by altering the signal's shape without changing its amplitude (Figure 5a). The results, plotted in Figure 5b, again show an immediate performance collapse for the Random Forest model. Even the slightest time warp causes its balanced accuracy to fall below 50%, indicating that its predictions become worse than random. The neural network, however, remains robust. Its performance is largely unaffected by a speed ratio of 1.1 and only degrades by approximately 6 percentage points at a more significant ratio of 1.5. This demonstrates the CNN's ability to learn scale-invariant features.