###### Abstract
These pattern recognition techniques identify waveforms by extracting features such as amplitude, frequency, and phase from seismic data. Some studies also incorporate techniques from related fields such as acoustics and speech processing [15; 16]. Noise trace identification must detect and characterize unwanted or anomalous patterns that may vary significantly in amplitude, frequency content, and temporal attenuation. Most existing methods for anomalous noise identification still rely primarily on amplitude difference information in the original data [17], which can be insufficient for distinguishing between valid high-amplitude reflections and true noise anomalies. This limitation underscores the need for more robust, data-driven approaches that can exploit richer feature representations beyond simple amplitude thresholds. In recent years, machine learning-based methods for waveform classification have advanced rapidly, achieving notable improvements in both automation and accuracy [18; 19]. These methods can be divided into unsupervised and supervised approaches, depending on the learning strategy. Common unsupervised methods include K-means clustering [20; 21; 22] and self-organizing maps (SOMs) [23; 24; 25; 26], which have the advantage of not requiring labeled data; however, they often rely on manual feature selection. Supervised methods typically perform better, but require high-quality labeled data. Most studies using labeled data rely on verified field data for training [27; 28; 29]. Some studies also use a small amount of manually labeled data to fine-tune pre-trained models and enhance performance [30]. As for network structures, convolutional neural networks (CNNs) are widely used [28; 29; 31; 32; 33]. Other studies have used recurrent neural networks (RNNs) [34; 35] and Transformer models [36; 37; 38]. These models demonstrate strong learning ability across various tasks. Although most studies do not focus directly on environmental noise identification, their ideas and methods remain valuable references. Compared to post-stack seismic data, raw seismic data affected by environmental noise exhibit distinct characteristics. Specifically, noise in raw recordings is often more complex and shows large amplitude variations. Moreover, raw seismic waveforms display less regularity than those in post-stack seismic sections, making it more difficult to accurately identify noise traces in field recordings. Currently, environmental noise is mainly identified based on amplitude differences [17]. When interference occurs near the acquisition instrument, the recorded signals are usually stronger than normal signals. This method performs well at locations far from the source, but near the shot point, signals such as surface and shear waves may cause abnormal energy levels [1]. To make signal features clearer, we use Mel-spectrograms to extract features from field seismic recordings. Mel-spectrograms provide an intuitive and informative representation of seismic signals by converting single-trace seismic data into two-dimensional images that display frequency and amplitude variations over time. This time-frequency representation enhances the visibility of anomalous energy patterns while preserving key signal characteristics, making it well-suited for noise identification tasks. Most importantly, Mel-spectrogram features remain relatively stable across seismic signals from different areas and acquired under varying field parameters. This helps reduce the model's dependence on survey-specific characteristics, such as source-receiver distance or time sampling rate. As a result, networks trained on Mel-spectrograms exhibit better generalization and are more robust when applied to new datasets. To train the model, we constructed a high-quality dataset by combining pure noise recordings with carefully selected low-noise field data. This ensures a clear distinction between noise and valid signal traces, improving training effectiveness. A Vision Transformer (ViT) model was then trained using this dataset.