Extracting dispersion curves from ambient noise correlations using deep learning
This work addresses the need for automated processing of large dispersion curve datasets in seismology, though it is incremental as it applies an existing deep learning method to a specific domain task.
The paper tackled the problem of classifying surface wave dispersion curve phases from ambient noise correlations by converting standard FTAN analysis into images and using a convolutional neural network (U-net) with transfer learning. The results showed that machine classification was nearly identical to human-picked phases, with no improvement when processing multiple images at once.
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.