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Extracting Dispersion Curves From Ambient Noise Correlations Using Deep Learning

2020; Institute of Electrical and Electronics Engineers; Volume: 58; Issue: 12 Linguagem: Inglês

10.1109/tgrs.2020.2992043

1558-0644

Xiaotian Zhang, Zhe Jia, Zachary E. Ross, R. W. Clayton,

Geophysics and Sensor Technology

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.