# Airwave Noise Identification from Seismic Data Using YOLOv5
## 3 The Basic Principles of YOLOv5
### Network Architecture  
The YOLOv5m model structure employed in this paper is depicted in Figure 2 and comprises two primary modules: the Backbone and the Neck [32]. The Backbone is tasked with extracting multi-scale features from the input image and consists of three key components: the Focus layer, CSP (Cross Stage Partial Network) layer, and SSP (Spatial Pyramid Pooling) layer.  
The Focus layer functions as the initial convolutional layer, increasing the number of channels by processing and downsampling the input image in chunks. This approach effectively reduces the computational load while preserving detailed information from the image. The CSP structure facilitates feature segmentation and merging, aimed at minimizing computational costs and reducing information redundancy. It also enhances gradient flow and model expressiveness through cross-layer residual connections. The term CSP_X denotes X residual or convolutional units; the network architectures for YOLOv5m and more complex versions align with the YOLOv5m structure used in this study, with the key distinction being that more advanced networks have larger values of X. This design allows for adaptability to varying computational resources and task requirements.  
The SSP layer aggregates multi-scale information by transforming feature maps into fixed-size feature vectors through max pooling operations at different scales, thereby enhancing the model's capability to detect objects of various sizes. As illustrated in Figure 2, the Backbone's three distinct levels of features are forwarded to the Neck, enabling multi-scale feature fusion and improving the model's detection performance for a wide range of object sizes.  
The Neck network is designed to further process and integrate the features obtained from the Backbone for object detection. It primarily consists of CSP layers, but does not utilize cross-layer residual connections, as its main function is multi-scale feature fusion rather than pure feature extraction. Since the Neck network must manage a significant volume of feature maps from the Backbone, the feature fusion process is inherently complex. Consequently, eliminating residual connections simplifies the Neck network's design, reducing the number of model parameters and thereby decreasing computational overhead and memory usage.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Application Description** & **Reference** \\ \hline Seismic velocity spectrum picking & [PERSON] et al., 2024 [37] \\ \hline Real-time co-seismic landslide detection & [PERSON] et al., 2022 [35] \\ \hline Detection of co-seismic collapsed buildings & [PERSON] et al., 2024 [37] \\  & [PERSON] and [PERSON], 2024 [41] \\ \hline Microseismic event detection & [PERSON] and [PERSON], 2022 [36] \\ \hline Local velocity anomalies detection from & [PERSON] and [PERSON], 2024 [42] \\ Seismic inversion models & [PERSON] et al., 2022 [38] \\ \hline Arrival-time picking of P- and S-waves of & [PERSON] et al., 2023 [39] \\ \hline Post-earthquake fire detection & [PERSON] and [PERSON], 2023 [43] \\ \hline Seismic noise detection (this work) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Applications of the YOLO object detection algorithm in seismology.