Identification of Environmental Noise Traces in Seismic Recordings Using Vision Transformer and Mel-Spectrogram
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...
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MDPI AG
2025-08-01
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| author | Qianlong Ding Shuangquan Chen Jinsong Shen Borui Wang |
| author_facet | Qianlong Ding Shuangquan Chen Jinsong Shen Borui Wang |
| author_sort | Qianlong Ding |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7b5adf674b9745e6ad8a1b3cdcfddbb1 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-7b5adf674b9745e6ad8a1b3cdcfddbb12025-08-20T04:00:49ZengMDPI AGApplied Sciences2076-34172025-08-011515858610.3390/app15158586Identification of Environmental Noise Traces in Seismic Recordings Using Vision Transformer and Mel-SpectrogramQianlong Ding0Shuangquan Chen1Jinsong Shen2Borui Wang3College of Geophysics, China University of Petroleum (Beijing), Beijing 102249, ChinaCollege of Geophysics, China University of Petroleum (Beijing), Beijing 102249, ChinaCollege of Geophysics, China University of Petroleum (Beijing), Beijing 102249, ChinaSinopec Geophysical Corporation Southern Branch, Chengdu 610213, ChinaEnvironmental 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.https://www.mdpi.com/2076-3417/15/15/8586automated noise identificationnoise attenuationMel-spectrogramseismic data quality controldeep learning |
| spellingShingle | Qianlong Ding Shuangquan Chen Jinsong Shen Borui Wang Identification of Environmental Noise Traces in Seismic Recordings Using Vision Transformer and Mel-Spectrogram Applied Sciences automated noise identification noise attenuation Mel-spectrogram seismic data quality control deep learning |
| title | Identification of Environmental Noise Traces in Seismic Recordings Using Vision Transformer and Mel-Spectrogram |
| title_full | Identification of Environmental Noise Traces in Seismic Recordings Using Vision Transformer and Mel-Spectrogram |
| title_fullStr | Identification of Environmental Noise Traces in Seismic Recordings Using Vision Transformer and Mel-Spectrogram |
| title_full_unstemmed | Identification of Environmental Noise Traces in Seismic Recordings Using Vision Transformer and Mel-Spectrogram |
| title_short | Identification of Environmental Noise Traces in Seismic Recordings Using Vision Transformer and Mel-Spectrogram |
| title_sort | identification of environmental noise traces in seismic recordings using vision transformer and mel spectrogram |
| topic | automated noise identification noise attenuation Mel-spectrogram seismic data quality control deep learning |
| url | https://www.mdpi.com/2076-3417/15/15/8586 |
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