论文标题
使用深度学习从环境噪声相关性提取分散曲线
Extracting dispersion curves from ambient noise correlations using deep learning
论文作者
论文摘要
我们提出了一种机器学习方法，以分类表面波色散曲线的相位。在接收器阵列上观察到的表面的标准FTAN分析被转换为图像，其中每个像素都被归类为基本模式，第一个夸张或噪声。我们使用具有监督学习目标的卷积神经网络（U-NET）体系结构，并结合了转移学习。最初使用合成数据进行培训以学习粗糙结构，然后使用基于人类分类的大约10％的实际数据对网络进行微调。结果表明，机器分类几乎与人类采摘阶段相同。扩展该方法一次处理多个图像并不能改善性能。开发的技术将促进大分散曲线数据集的自动处理。
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.
