###### Abstract  
Environmental noise is inevitable during seismic data acquisition, with major sources including heavy machinery, rivers, wind, and other environmental factors. During field data acquisition, it is important to assess the impact of environmental noise and evaluate data quality. In subsequent seismic data processing, these noise components also need to be eliminated. Accurate identification of noise traces facilitates rapid quality control (QC) during fieldwork and provides a reliable basis for targeted noise attenuation. Conventional environmental noise identification primarily relies on amplitude differences. However, in seismic data, high-amplitude signals are not necessarily caused by environmental noise. For example, surface waves or traces near the shot point may also exhibit high amplitudes. Therefore, relying solely on amplitude-based criteria has certain limitations. To improve noise identification accuracy, we use the Mel-spectrogram to extract features from seismic data and construct the dataset. Compared to raw time-series signals, the Mel-spectrogram more clearly reveals energy variations and frequency differences, helping to identify noise traces more accurately. We then employ a Vision Transformer (ViT) network to train a model for identifying noise in seismic data. Tests on synthetic and field data show that the proposed method performs well in identifying noise. Moreover, a denoising case based on synthetic data further confirms its general applicability, making it a promising tool in seismic data QC and processing workflows. automated noise identification; noise attenuation; Mel-spectrogram; seismic data quality control; deep learning Article  
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Evinmental noise is unavoidable during seismic data acquisition. This type of noise typically originates from external sources such as heavy machinery, natural wind, flowing rivers and other anthropogenic or environmental disturbances [1]. When noise sources are located near geophones, their vibrations are often captured and typically exhibit stronger amplitudes than subsurface reflections. Some sources, such as passing vehicles or wind, can cause continuous disturbances over extended periods and broad areas. Others, like workers' footsteps, are brief and sporadic, leading to localized interference. Consequently, environmental noise often shows high amplitude and irregular temporal patterns, which can significantly degrade signal quality [2]. During field acquisition, real-time noise monitoring is important for ensuring the collection of high-quality seismic data [3, 4]. If data quality is severely compromised, re-acquisition may be necessary. Meanwhile, quality control (QC) should be performed on the collected data to ensure their reliability and usability for downstream processing [5]. In addition to field-level monitoring, accurate noise identification plays a critical role in seismic data processing. Based on the results of noise detection, targeted and precise noise attenuation techniques can be applied to suppress contaminating energy without damaging useful signals [6; 7]. Therefore, accurate identification of noise-contaminated traces is essential not only for effective noise monitoring and QC during the acquisition process, but also for improving the quality and interpretability of seismic images in subsequent data processing workflows. The identification of noise traces is essentially a pattern recognition problem involving seismic waveforms [8]. In oil and gas seismic exploration, waveform-based pattern recognition is primarily used for seismic phase classification, with a focus on analyzing subsurface geological structures such as faults, horizons, and stratigraphic features [9; 10; 11]. In natural earthquake research, it is mainly applied to the detection of seismic events [12; 13; 14]. These approaches demonstrate the power of pattern recognition in identifying meaningful structures or events within complex seismic signals. However, research on noise trace identification in raw field seismic data remains limited.