# Airwave Noise Identification from Seismic Data Using YOLOv5
## 6 Field Data Validation  
In practical applications, we usually focus on whether airway noise is present, rather than pinpointing the airway region. Moreover, we classify interference as significant only when it is detected on both sides of the seismic source. Interference detected on just one side is not considered significant, as it may result from other sources and may not be strong enough to qualify as substantial interference. To determine the presence of airway noise, we follow these steps: (1) Extract seismic images from within the acoustic range, based on 2D or 3D seismic records, and use these as input for YOLOv5m; (2) Check the YOLOv5m predictions to see if two predicted targets are detected. If no targets are detected in any input image, airway noise is considered absent. If targets are detected, proceed to the next step; (3) Assess whether both predicted bounding boxes have confidence scores greater than 0.35 and are spatially separated. For 3D seismic profiles, if at least one image (one line data) contains a pair of boxes meeting these criteria, airway noise is confirmed; otherwise, it is deemed absent. To further assess the model's effectiveness, we tested it using 456 shots of 2D seismic data from the same region, which were excluded from both the training and test datasets. As shown in Table 2, the model achieved an overall accuracy of 96.4% on this dataset, indicating that it can reliably detect the presence or absence of airway noise in most cases (see Table 2). Notably, in the instances where airway was present, the model's recall was significantly higher than its precision, suggesting a tendency towards false positives rather than missed detections. The high values of the Dice Index and Jaccard Index suggest that the model has performed very well in detecting airway cases. The formulas for different evaluation metrics used are shown as follows:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{8}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{9}\]  
Figure 7: Randomly selected predictions from the test dataset, where the red boxes indicate airway noise detected by YOLOv5m, and the numbers in the top-left corner represent the confidence levels. \[\text{TPR}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{10}\] \[\text{FPR}=\frac{\text{FP}}{\text{FP}+\text{TN}}\] (11) \[\text{Dice}=\frac{2\times\text{TP}}{2\times\text{TP}+\text{FP}+ \text{FN}}\] (12) \[\text{Jaccard}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}} \tag{13}\]  
where TP = True Positives (correctly predicted positive cases), and FN = False Negatives (actual positives that were incorrectly predicted as negatives); FP = False Positives (actual negatives that were incorrectly predicted as positives); TN = True Negatives (correctly predicted negative cases). TPR measures how well the model can identify positive instances. A higher TPR means fewer false negatives, indicating better performance in detecting positive cases. FPR measures the proportion of actual negative instances that are incorrectly identified as positive by the model. The Dice Index is a measure of overlap between two sets (predicted and actual), commonly used in binary classification tasks, especially in medical imaging and object detection. The Jaccard Index is another measure of similarity between two sets, commonly used to compare the overlap of predicted and actual positive instances. The model's recall rate was higher than its performance on the YOLO test dataset (Figure 5), because we defined interference as present if at least one 2D seismic record from a shot satisfied the interference condition, thereby minimizing missed detections. The model's precision on airway was slightly lower compared to the YOLO test dataset, because the airway noises are weak, which was not represented in the training data. In practical applications, such weak interference is usually negligible, but during prediction, it can result in low-confidence scores. When these scores exceed the threshold, they are classified as airway noise (Figure 8).