## 4 Identification Results to Support Noise Attenuation  
Few studies have directly applied deep learning to anomalous amplitude attenuation, primarily due to the unstable effect that anomalous values have on model training. Most research combines deep learning for noise identification or uses it to provide better parameter settings for traditional denoising methods, thereby improving denoising performance. For example, [PERSON] et al. [6] employed deep learning to directly predict optimal parameters for a threshold-based anomalous amplitude attenuation method; [PERSON] et al. [2] used noise identification results from deep learning to adaptively attenuate anomalous noise; [PERSON] et al. [44] leveraged a U-Net to identify spatiotemporal variations of strong energy noise in seismic data, enabling more effective determination of denoising thresholds. Accurate noise identification supports both QC during field data acquisition and improves seismic data processing by enabling more accurate noise attenuation. Effective identification of noise-contaminated traces allows for targeted processing strategies, reducing the risk of overprocessing clean data and weak noise data. We present an example of noise attenuation using synthetic data to demonstrate the practical value of the proposed method. Traditional noise attenuation methods typically detect anomalies by comparing the amplitude of each sample with that of its neighboring samples. When a sample is deemed anomalous, its amplitude is reduced to the average of the surrounding values. This approach is commonly referred to as Anomalous Amplitude Attenuation (AAA). Figure 11: Identification results of noise traces on test data: (**a**) raw seismic data, (**b**) results from the model trained with raw data, (**c**) results from the model trained with Mel-spectrograms. In the figures, red marks indicate correctly identified noise traces, green marks represent weak noise traces misclassified as strong noise, and blue marks indicate strong noise traces misclassified as weak noise. While simple and computationally efficient, this method depends entirely on amplitude deviations. As a result, methods that rely solely on amplitude may misclassify and damage valid signals, especially when the signal amplitude is naturally high or rapidly changing. By incorporating the results of noise identification, attenuation is applied only to samples explicitly recognized as noise, thereby better preserving valid signals. This selective processing helps minimize signal distortion while still effectively reducing anomalous energy. The recognition results are used as weights to blend the denoised data with the original data:  
\[\mathbf{D}=(1-\mathbf{W})\mathbf{A}+\mathbf{W}\mathbf{S} \tag{12}\]  
where \(\mathbf{D}\) is the result of weighting the AAA output, \(\mathbf{A}\) represents the result of the AAA method, \(\mathbf{S}\) is the original seismic data, and \(\mathbf{W}\) is the weight, where 0 indicates a strong noise trace and 1 indicates a weak noise trace. We used a portion of the Marmousi model as the velocity model for forward modeling and added noise from the NB dataset to the synthetic data. The Marmousi model, known for its complex structural features and strong velocity contrasts, provides a challenging test case for evaluating noise attenuation performance. Figure 12 shows an example of synthetic data, which exhibits both coherent reflections and superimposed noise. Noise attenuation and migration imaging were then performed to evaluate the effectiveness of the proposed method. Migration results allow for a direct assessment of whether signal integrity has been preserved and whether noise has been sufficiently suppressed to enhance reflector continuity. This ensures that our evaluation reflects real processing practices and highlights the potential advantages of incorporating intelligent noise identification into the workflow. Here, we briefly introduce a conventional AAA method used in this article [2].Noise attenuation is applied only to data below first breaks.