High-frequency surface-wave methods have been widely used for surveying near-surface shear-wave velocities. A key step in high-frequency surface-wave methods is to acquire dispersion curves in the frequency–velocity domain. The traditional way to acquire the dispersion curves is to identify the dispersion energy and manually pick phase velocities by following energy peaks at different frequencies. A large number of dispersion curves need to be extracted for inversion, especially for surveys with long two-dimensional sections or large three-dimensional (3D) coverages. Human–machine interaction-based dispersion curves extraction, however, is still common, which is time-consuming. We developed a deep learning model, termed Dispersion Curves Network (DCNet), that can rapidly extract dispersion curves from dispersion images by treating dispersion curves extraction as an instance segmentation task. The accuracy of the dispersion curves extracted by our DCNet model is demonstrated by theoretical data. We used a 3D field application of ambient seismic noise to demonstrate the effectiveness and robustness of our method. The real-world results showed that the accuracy of the dispersion curves extracted from the field data using our method can achieve human-level performance and our method can meet the requirement of geoengineering surveys in rapidly extracting massive dispersion curves of surface waves.

中文翻译：

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用于提取色散曲线的深度学习

高频表面波方法已广泛用于测量近地表横波速度。高频表面波方法的一个关键步骤是获取频速域中的频散曲线。获取色散曲线的传统方法是通过跟踪不同频率的能量峰值来识别色散能量并手动选取相速度。反演需要提取大量频散曲线，尤其是二维剖面较长或三维（3D）覆盖范围较大的调查。然而，基于人机交互的色散曲线提取仍然很常见，这很耗时。我们开发了一个深度学习模型，称为 Dispersion Curves Network (DCNet)，通过将色散曲线提取作为实例分割任务，可以从色散图像中快速提取色散曲线。理论数据证明了我们的 DCNet 模型提取的色散曲线的准确性。我们使用环境地震噪声的 3D 现场应用来证明我们方法的有效性和稳健性。真实世界的结果表明，使用我们的方法从现场数据中提取的频散曲线的精度可以达到人类水平，并且我们的方法可以满足地球工程勘测快速提取大量面波频散曲线的要求。我们使用环境地震噪声的 3D 现场应用来证明我们方法的有效性和稳健性。真实世界的结果表明，使用我们的方法从现场数据中提取的频散曲线的精度可以达到人类水平，我们的方法可以满足地球工程勘测快速提取大量面波频散曲线的要求。我们使用环境地震噪声的 3D 现场应用来证明我们方法的有效性和稳健性。真实世界的结果表明，使用我们的方法从现场数据中提取的频散曲线的精度可以达到人类水平，我们的方法可以满足地球工程勘测快速提取大量面波频散曲线的要求。