使用深度学习从环境噪声相关性中提取色散曲线
Extracting dispersion curves from ambient noise correlations using deep learning
摘要：
我们提出了一种机器学习的方法来分类的面波频散曲线的相位。在接收器阵列上观察到的表面的标准FTAN分析被转换成图像，其中，每个像素被分类为基模、第一泛音或噪声。我们使用一个卷积神经网络（U网）架构与监督学习的目标，并纳入迁移学习。训练最初使用合成数据来学习粗略的结构，然后使用基于人类分类的大约10%的真实数据来微调网络。结果表明，机器分类与人类挑选的相位几乎相同。扩展该方法以同时处理多个图像并没有提高性能。所开发的技术将促进大型色散曲线数据集的自动化处理。
英文摘要：
We present a machine-learning approach to classifying the phases of surface wave dispersion curves. Standard FTAN analysis of surfaces observed on an array of receivers is converted to 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 faciliate automated processing of large dispersion curve datasets.
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