Training, Validation and Test Sets for paper 'A Little Data goes a Long Way: Automating Seismic Phase Arrival Picking at Nabro Volcano with Transfer Learning', Version 3

  • Sacha Lapins (Creator)
  • Berhe Gezahegn (Creator)
  • J-Michael Kendall (University of Oxford) (Creator)
  • Max Werner (Creator)
  • Katharine V Cashman (Creator)
  • James O S Hammond (Creator)

Dataset

Description

Training, Validation and Test Data for model presented in paper 'A Little Data Goes A Long Way: Automating Seismic Phase Arrival Picking at Nabro Volcano with Transfer Learning', submitted to Journal of Geophysical Research: Solid Earth. Files: - train_events_2498.h5 = training set of seismic waveforms (events with P-/S-wave labelled arrivals only, i.e., no noise waveforms) - train_events_2498.pkl = event training set metadata (UTC P-/S-wave phase arrival times) - train_noise_2498.h5 = training set of seismic waveforms (noise sections only, i.e., no event waveforms) - train_noise_2498.pkl = noise training set metadata (UTC time for training noise waveforms) - val_events.h5 = validation set of seismic waveforms (events with P-/S-wave labelled arrivals only, i.e., no noise waveforms) - val_events.pkl = event validation set metadata (UTC P-/S-wave phase arrival times) - val_noise.h5 = validation set of seismic waveforms (noise sections only, i.e., no event waveforms) - val_noise.pkl = noise validation set metadata (UTC time for validation noise waveforms) - test.h5 = test set of seismic waveforms (events and noise) - test_events.pkl = event test set metadata (UTC P-/S-wave phase arrival times for test event waveforms) - test_noise.pkl = noise test set metadata (UTC time for test noise waveforms) - nabro_2011-247.mseed = 24 hours seismic data from Nabro Urgency Array (2011-09-04), saved in mseed format (e.g., can be read with obspy) - nabro_2011-269.mseed = 24 hours seismic data from Nabro Urgency Array (2011-09-26), saved in mseed format (e.g., can be read with obspy) Further details and code for reading and using these files can be found at the GitHub repo for this paper: https://github.com/sachalapins/U-GPD
Date made available3 Feb 2021
PublisherZenodo

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