## 2 Methods  
### Mel-Spectrogram  
The Mel-spectrogram is a time-frequency representation method originally developed for audio and speech signal processing [39]. It transforms a one-dimensional signal into a two-dimensional spectrogram, which can be used as input features for further analysis in machine learning models. The process involves dividing the signal into short, overlapping time windows, and applying a Short-Time Fourier Transform (STFT) to each window to extract its localized frequency content [40]. In the resulting spectrogram, the horizontal axis represents time, the vertical axis represents frequency, and the value at each point (\(t,f\)) shows the amplitude of frequency \(f\) at time \(t\). The frequency \(f\) conversion to the Mel scale is done using the following formula:  
\[f_{\text{mel}}=2595\times\log_{10}\biggl{(}1+\frac{f}{700}\biggr{)} \tag{1}\]  
where \(f_{\text{mel}}\) is the Mel frequency. Although various time-frequency analysis methods, such as wavelet transform decomposition, have been widely used to analyze non-stationary seismic signals [41; 42], they often require careful parameter tuning and involve trade-offs between time and frequency resolution. In contrast, the Mel-spectrogram provides a compact and stable time-frequency representation by mapping the frequency axis onto a nonlinear Mel scale. The Mel scale compresses high-frequency components while enhancing resolution at low frequencies, making it particularly suitable for seismic applications. With appropriately selected parameters, the Mel-spectrogram effectively captures the low-frequency content where most seismic energy is concentrated. This nonlinear transformation helps preserve subtle variations and improves the localization of anomalies in seismic data. Additionally, the Mel-spectrogram is computationally efficient. Compared to wavelet-based time-frequency methods, it offers higher efficiency and generates compact, image-like representations that are well suited for deep learning models. We converted seismic signals into Mel-spectrograms and applied image recognition techniques to identify seismic traces. Compared with raw seismic signals, Mel-spectrograms make it easier to visually observe the time-frequency characteristics of the data. Figure 1 presents two seismic traces. Figure 1a shows a trace with low noise,while Figure 1b shows a trace severely affected by noise. Both traces were converted into Mel-spectrograms, as shown in Figure 2. When noise is minimal, the frequency range of seismic reflections and the amplitude decay over time remain relatively stable. Figure 2a shows the Mel-spectrogram of the low-noise trace, where energy is more concentrated along both the time and frequency axes. In contrast, when a seismic trace is affected by environmental noise, its amplitude pattern over time and frequency often deviates from that of a clean signal. Figure 2b shows the spectrogram of the noise trace, where strong energy appears outside the main frequency band of the seismic signal, as highlighted by the red circles in the figure. We adopt a single-trace identification approach because it offers greater flexibility and efficiency in practical applications. Converting data into Mel-spectrograms helps avoid issues caused by varying sampling rates across different surveys or equipment, as the time-frequency representation normalizes these variations and provides a consistent feature space. Single-trace identification also mitigates problems related to missing traces, which are common in complex mountainous areas or challenging terrain where acquisition is often incomplete. In contrast, approaches based on multi-trace analysis require fully sampled data and can suffer significant degradation when traces are missing or corrupted. Additionally, converting multi-trace data into Mel-spectrograms results in three-dimensional representations, which substantially increase computational costs and memory requirements.