We present a machine learning approach to classify the phases of surface wave dispersion curves. Standard frequency-time analysis (FTAN) analysis of seismograms observed on an array of receivers is converted into an image, of which each pixel is classified as fundamental mode, first overtone, or noise. We use a convolutional neural network (U-Net) architecture with a supervised learning objective and incorporate transfer learning. The training is initially performed with synthetic data to learn coarse structure, followed by fine-tuning of the network using approximately 10% of the real data based on human classification. The results show that the machine classification is nearly identical to the human picked phases. Expanding the method to process multiple images at once did not improve the performance. The developed technique will facilitate the automated processing of large dispersion curve data sets.

中文翻译：

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使用深度学习从环境噪声相关性中提取色散曲线

我们提出了一种机器学习方法来对表面波色散曲线的相位进行分类。在接收器阵列上观察到的地震图的标准频率时间分析 (FTAN) 分析被转换为图像，其中每个像素被分类为基模、一阶泛音或噪声。我们使用具有监督学习目标的卷积神经网络 (U-Net) 架构并结合迁移学习。训练最初使用合成数据执行以学习粗略结构，然后使用基于人类分类的大约 10% 的真实数据对网络进行微调。结果表明，机器分类与人工挑选的阶段几乎相同。将方法扩展为一次处理多个图像并没有提高性能。