## 3 Experimental Results  
### Data Preparation  
To support model training and evaluation, we curated a diverse dataset composed of real noise, field seismic data, and synthetic simulations. Specifically, two sets of pure noise recordings were extracted from different seismic survey areas and labeled as NA (Noise Data A) and NB (Noise Data B). These recordings contain only ambient or acquisition-related noise, without any coherent subsurface reflections, and are used to simulate challenging noise scenarios. In addition, we collected two sets of field seismic data with relatively low inherent noise levels. These data were further processed using conventional denoising workflows to suppress anomalous noise and enhance signal fidelity. The resulting datasets are referred to as SA (Seismic Data A) and SB (Seismic Data B), and they represent clean seismic signals suitable for model learning. To enable quantitative analysis and assist in parameter testing during dataset construction, we also generated a synthetic seismic dataset using a finite-difference solution of the acoustic wave equation. This dataset is referred to as AA (Acoustic Data A). The training set was constructed by combining NA and SA, allowing the model to learn the distinction between pure noise and clean seismic signals. The test set was independently assembled using NB and SB, enabling the evaluation of model robustness on unseen data from different survey areas. Figure 6 presents representative examples from each dataset, illustrating the noise levels, structural features, and data quality across the different sources. In Equation (2), the parameter \(m\) determines whether a seismic trace should be classified as a noise trace. This threshold is critical for guiding downstream processes such as noise suppression and data labeling, and thus must be carefully selected. In this study, to determine an appropriate and practically meaningful value for \(m\), we established a set of evaluation criteria based on the impact of noise on imaging quality. We combined the NA and AA datasets and randomly selected one-tenth of the seismic traces to add noise, followed by anomalous noise attenuation processing. This process was designed to simulate real-world scenarios in which only a small fraction of traces are severely contaminated, allowing us to evaluate the sensitivity and robustness of the threshold parameter. The amplitude of the added noise is calculated using the following formula:  
\[NMean=m\times CMean \tag{10}\]  
Figure 6: Examples of collected data: (**a**) Noise Data A, (**b**) Noise Data B, (**c**) Seismic Data A, (**d**) Seismic Data B, (**e**) Acoustic Data A.  
where \(m\), _NMean_ and _CMean_ have the same meanings as in Equation (2). When calculating _CMean_, only the portion below the first arrival is considered, excluding both the signal-free region above the first arrival and the influence of the first arrival itself. Figure 7 shows an example of synthetic data. Based on migration imaging characteristics, some weak noise does not need to be removed, as noise attenuation may damage valid signals. This trade-off highlights the importance of distinguishing between tolerable noise and anomalous noise, particularly when dealing with weak reflection events. Therefore, we performed migration imaging on the three sets of data shown in Figure 7 and calculated the imaging signal-to-noise ratio (SNR) before and after noise attenuation. This migration imaging evaluation enables a quantitative assessment of how the denoising process affects both the preservation of useful signals and the suppression of undesired components. The formula used to calculate the SNR is as follows:  
\[\text{SNR}=10\ \log_{10}\frac{\|\hat{\mathbf{y}}\|_{2}^{2}}{\|\mathbf{y}-\hat{ \mathbf{y}}\|_{2}^{2}} \tag{11}\]  
where \(\mathbf{y}\) represents the actual results, and \(\hat{\mathbf{y}}\) represents the results with added noise or those computed by traditional methods. We tested \(m\) values ranging from 0.2 to 2 with intervals of 0.2.