# Airwave Noise Identification from Seismic Data Using YOLOv5
## 1 Introduction  
During field seismic data acquisition, strong airway noise can sometimes occur due to factors such as poor source excitation conditions. For example, in well-shooting seismic records, improper burial of explosive sources may result in seismic wave energy escaping through the wellhead at the moment of detonation, propagating as acoustic waves through the air and being picked up by the detectors [1, 2]. On 2D seismic profiles, these airway waves typically appear as straight lines with slopes corresponding to the speed of sound (approximately 343 m/s) in seismic profiles [3]. In contrast, on 3D seismic profiles, they may take the form of arcs or straight lines with steeper slopes, indicating a higher apparent velocity (Figure 1). This airway noise tends to exhibit broadband characteristics, with particularly strong energy concentrated in the higher effective frequency range [4]. If this high-energy noise is not promptly identified and addressed, it can impair the effectiveness of multi-channel processing techniques, such as pre-stack consistency, and severely degrade the accuracy of pre-stack seismic migration results. Currently, airway noise is primarily identified through visual inspection, a simple and intuitive method that is still effective for 2D seismic exploration or small-scale 3D surveys [5]. However, as 3D surveys become more complex--with data volumes per shot reaching hundreds of megabytes and time intervals between successive shots shrinking to as little as half a minute (for well-shooting) or a few seconds (for controlled sources)--manual identification becomes increasingly impractical [6]. The large volume of data and short timeframes lead to omissions and significant subjective bias in the identification process. Traditional quality control methods focus on monitoring various operational parameters, such as seismic instrument status, TB time differences, equipment performance, source-receiver relationships, and observation systems [7; 8; 9; 10]. However, these methods do not fully account for the effects of surface conditions, geological variations, environmental factors, and random events on seismic data. Moreover, they lack the ability to quickly identify airway noise based on waveform characteristics. Although airway noise often presents clear features on seismic profiles, weaker instances (as shown in Figure 1b) may go undetected. Additionally, airway noise may appear as unilateral slanting lines (as shown in Figure 1e), which, in some cases, are caused by noise sources unrelated to well-shooting. These complexities increase the difficulty and uncertainty of accurately identifying airway noise. Therefore, enhancing the efficiency and accuracy of computer-based automatic detection is essential for ensuring high-quality seismic data. In recent years, deep learning (DL) methods have emerged as innovative solutions for tackling the enormous challenges of geophysical data processing and interpretation [11; 12; 13; 14; 15]. These methods have been successfully applied to tasks such as seismic data denoising [16; 17; 18] and inversion [19; 20; 21; 22]. Unlike traditional approaches, which often rely on physical models, deep learning is entirely data-driven. Once the model is trained, it can rapidly establish a relationship between observed data and the predicted parameters. As a result, DL-based techniques offer high efficiency, making them well-suited for real-time data processing and inversion imaging [11; 23]. The identification of airway noise is fundamentally an image recognition task, where the objective is to automatically detect the location and extent of interference in individual  
Figure 1: Typical characteristics of airway noise (indicated in green). (**a**) No airway noise; (**b**) Weak airway noise; (**c**,**d**) represent airway noise in three-dimensional exploration, where (**c**) is closer to the seismic source than (**d**); (**e**,**f**) depict typical airway noise in two-dimensional seismic exploration. seismic shot records.