摘要：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. ? 2025 by the authors.

ISSN号：2076-3417

卷、期、页：卷15期15

发表日期：2025-08-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：四区

收录情况：SCI（科学引文索引印刷版）,CSCD（中国科技引文期刊）（扩展）,CSCD（中国科技引文期刊）（核心）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：APPLIED SCIENCES-BASEL

参与作者：Wang, Borui

通讯作者：丁乾龙

第一作者：陈双全,沈金松

论文类型：期刊论文

论文概要：丁乾龙,陈双全,沈金松,Wang, Borui，Identification of Environmental Noise Traces in Seismic Recordings Using Vision Transformer and Mel-Spectrogram，APPLIED SCIENCES-BASEL，2025，卷15期15

论文题目：Identification of Environmental Noise Traces in Seismic Recordings Using Vision Transformer and Mel-Spectrogram