Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extracting dispersion curves from ambient noise correlations using deep
  learning   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.

33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga