# Airwave Noise Identification from Seismic Data Using YOLOv5
## 8 Conclusions  
In recent years, significant advancements in neural network technology, driven by rapid increases in computational power, have led to its widespread application across various fields, including Earth sciences [20; 23; 51; 52; 53]. While manual identification of airway noise can be somewhat intuitive, processing vast amounts of three-dimensional seismic data through manual operations is not only time-consuming but also introduces subjective judgment. This paper effectively addresses these challenges through the application of YOLOv5. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Train Time (h)** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** & **mAP50.95** & **Inference (ms)** & **Parameter (MB)** & **FLOPs@640 (B)** \\ \hline yolov5l & 0.88 & 0.997 & 1 & 0.995 & 0.98 & 10.1 & 46.5 & 109.1 \\ \hline yolov5m & 0.554 & 0.97 & 1 & 0.995 & 0.945 & 8.2 & 21.2 & 49 \\ \hline yolov5s & 0.318 & 0.987 & 0.951 & 0.992 & 0.847 & 6.4 & 7.2 & 16.5 \\ \hline yolov5n & 0.24 & 0.975 & 0.94 & 0.992 & 0.772 & 6.3 & 1.9 & 4.5 \\ \hline \hline \end{tabular} Note: [EMAIL_ADDRESS] (Mean Average Precision at IoU = 0.5 thresholds); [EMAIL_ADDRESS] (Mean Average Precision at IoU = 0.50.95 thresholds); FLOPs@640 refers to the number of floating-point operations.  
\end{table} 
Table 4: Comparison of YOLOv5 family training. Figure 11: Airway predictions of different colormap in test dataset, and the numbers in the top-left corner represent the confidence levels. Up: TWILIGHT. Down: CIVIDIS. We present a comprehensive workflow for identifying airwave noise in seismic data based on YOLOv5m, encompassing model construction, loss function establishment, dataset creation, model training, and prediction. Training results indicate that the model achieves an accuracy and recall of approximately 85% on the test dataset, successfully predicting airwave noise locations and confidence levels. To further validate the model's effectiveness, we applied the YOLOv5m model, trained on two-dimensional data, to seismic records from the Ningqiang area of Shaanxi and three-dimensional seismic records from the Xiushui area of Sichuan. The overall prediction accuracy in both regions exceeded 90%, with accuracy and recall rates for airwave noise reaching over 83% and 90%, respectively. Notably, the evaluation speed for single-shot three-dimensional seismic data (over 8000 traces) was less than 2 s, demonstrating the model's excellent transferability, generalization ability, and efficiency, making it suitable for detecting airwave noise in raw seismic data across diverse regions. Additionally, our algorithm can differentiate between unilateral and bilateral airwave noise, facilitating a comprehensive assessment of seismic data quality. This study represents the first automated, real-time detection of airwave noise, significantly enhancing operational efficiency. It also enables timely noise reduction measures, improving data quality and, consequently, increasing the reliability and accuracy of seismic data processing. This method can be extended to the recognition of various seismic signals or noise signals, which will be part of our future work. Conceptualization, Z.L., Z.Z. and L.G. ; methodology, Z.L., L.G., F.S. and R.T.; validation, Z.Z., F.S. and X.H. ; writing--original draft preparation, Z.L. and L.G. ; writing--review and editing, L.G., X.H. and R.T.; visualization, X.H. and G.C. ; supervision, R.T.; funding acquisition, Z.Z. and X.H. All authors have read and agreed to the published version of the manuscript. This work is supported by the Sinopec Data Intelligent Collection Research Project and Huzhou Public Welfare Research Project (2023 GZ17). Not applicable for studies not involving humans or animals. Not applicable. Data Availability Statement: Data and codes cannot be disclosed due to commercial confidentiality. Authors [PERSON], [PERSON] and [PERSON] were employed by the Sinopec Petroleum Engineering Geophysics Co., Ltd.