# Airwave Noise Identification from Seismic Data Using YOLOv5
## 4 The Training of YOLOv5m  
Our model underwent secondary training based on the initial weights of YOLOv5m, which were obtained through pre-training on a large dataset. Specifically, the initial weights of YOLOv5m are typically pre-trained on the COCO (Common Objects in Context) dataset [49]. The COCO dataset is a standard resource that contains hundreds of thousands of annotated images across 80 categories, widely utilized for various tasks in the field of computer vision. Utilizing these pre-trained weights enables the model to recognize common object features right from the start of secondary training (Figure 4). This capability allows the model to achieve good detection results in a shorter timeframe, thereby reducing overall training time and enhancing final performance. To facilitate the secondary training and fine-tuning of YOLOv5m for detecting airwave noise, we constructed a dedicated training dataset. The samples were collected from 5000 two-dimensional seismic recordings gathered in Ningqiang County, Shaanxi Province, in August 2023. This area presents complex seismic geological conditions characterized by significant terrain fluctuations and numerous cliffs. The exposed strata range from the Jurassic to the Sinian period, predominantly consisting of layers from the Triassic, Sinian, and Proterozoic. Notably, the dip angles of these strata vary considerably, typically between 45\({}^{\circ}\) and 70\({}^{\circ}\). The lithology is primarily limestone, with lower proportions of sandstone and mudstone, leading to overall poor excitation and reception conditions, which results in substantial airway noise. In our dataset, we manually marked the locations of airwave noise in each sample, as illustrated by the green boxes in Figure 1. After screening, we identified a total of 580 samples exhibiting clear airwave noise, with 80% allocated for training and 20% for testing. The training and prediction processes were accelerated using a GPU (NVIDIA GeForce RTX 3070 with 128 GB of memory (NVIDIA Corporation, Santa Clara, CA, USA)). The secondary training comprised a total of 100 iterations, taking approximately 1 h to complete. Upon finishing the training, the prediction time for a single two-dimensional seismic recording was under 0.5 s, while the prediction time for three-dimensional seismic recordings was under 2 s.  
Due to the slower propagation speed of airwaves compared to seismic waves, the acquisition window often closes before the acoustic waves reach the distant detectors. As a result, airwaves are only observed within a radius \(R_{a}=v_{a}t\) around the source, where \(v_{a}\) is speed of airwaves propagation and t is travel time. To enhance prediction efficiency, we crop the original seismic images, selecting only a portion of the seismic recordings for analysis. For two-dimensional seismic recordings, we extract data centered around the source with a radius of \(R_{a}\). In the case of three-dimensional seismic recordings, we extract seismic data from a rectangular area defined by \(R_{a}\times 0.7R_{a}\), treating each two-dimensional profile line as an individual sample. After preparing all the data, we construct profile images from the two-dimensional seismic recordings to serve as input for the model. To ensure consistent input dimensions, the samples undergo pixel interpolation. The 2D seismic data with one channel needs to be first mapped into images with three RGB color channels during training and prediction process (Figure 4). The model then outputs the locations of airwave noise along with their corresponding confidence. Figure 5 illustrates the evolution curves of loss and precision-recall during the secondary training process of YOLOv5m. As shown in the figure, both the localization loss and confidence loss for the training set decrease rapidly, while the precision and recall steadily increase. This indicates that the model performs well in learning the mapping from two-dimensional seismic images to airwave locations.