# Airwave Noise Identification from Seismic Data Using YOLOv5
## 6 Field Data Validation
To assess the model's transferability, we applied the model, initially trained on 2D data, to 55 shots of 3D seismic exploration data collected from the Xiushui Town area in Sichuan, China. The predictions are partially illustrated in Figure 9. For the points close to the seismic source, the airway noise appears as symmetrical diagonal lines, closely resembling predictions from the 2D records. In contrast, for survey lines farther from the source, the interference takes on an arc-shaped pattern, which differs from the 2D seismic \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Category** & **Precision** & **Recall** & **F1-Score** & **Support** & **TPR** & **FPR** & **Dice Index** & **Jaccard Index** \\ \hline
0 (No airwave) & 1.0 & 0.98 & 0.99 & 410 & & 0.9565 & 0.0220 & 0.8889 & 0.8 \\ \hline
1 (airwave) & 0.83 & 0.96 & 0.89 & 46 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Predictions of airwave noise in the 456-shot two-dimensional seismic records from Ningqiang County (accuracy and recall statistics). Figure 8: Instances labeled as no interference but predicted as interference; the boxes represent the prediction results, and the numbers indicate the confidence levels. recordings. Nonetheless, the model perform well, accurately predicting the airwave on both sides of the arc. As shown in Table 3, the overall prediction accuracy in this region reached 94%, surpassing the results obtained in the Qiangning area. The values of the Dice Index and Jaccard Index are higher than that in 2D case, suggesting a good performance in detecting 3D airwave cases. In addition, the missed detections were primarily attributed to the airwave being too weak to exceed the confidence threshold of 0.35. These findings confirm that the proposed model possesses strong predictive accuracy and robust generalization capabilities. Furthermore, the YOLOv5m model demonstrated its ability to identify unilateral acoustic waves (Figure 10). When the shot point is positioned near a mountain, the terrain can obstruct and reflect the propagation path of the acoustic waves. This results in the waves being reflected and absorbed on the mountain side, leading to a predominance of wave propagation toward the flat side, which creates the unilateral airwave wave phenomenon. Since this type of interference is mainly caused by the terrain, it can generally be disregarded in practical evaluations unless the interference energy is exceptionally high. Figure 10: The 3D seismic records in the Xiushui area exhibit unilateral airwave noise, primarily caused by the terrain. Figure 9: Randomly selected airwave noise prediction results from the Xiushui three-dimensional seismic data. The green boxes indicate YOLOv5m predictions of airwave noise, with the first and second rows representing seismic records corresponding to different seismic sources. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Category** & **Precision** & **Recall** & **F1-Score** & **Support** & **TPR** & **FFR** & **Dice Index** & **Jaccard Index** \\ \hline
0 (No airwave) & 0.94 & 1.0 & 0.97 & 31 & 1.0 & 0.06 & 0.954 & 0.913 \\ \hline
1 (airwave) & 1.0 & 0.91 & 0.95 & 23 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Predictions of airwave noise in the 54-shot 3D seismic records from Xiushui County.