Input file specification

The application requires a Miniseed Waveform (or any subtype) containing seismograms on which the wave arrival time should be detected. The waveform has to contain E, N and Z channels for each station.

Figure 1. Application input files specification

Filling form values

The application form consists of four parameters - see Figure 2. The first parameter allows you to choose weights obtained by training the Machine Learning model, where the Original option are the original weights published by the authors of the models ([1] for the GPD model and [2] for the PhaseNet model), and all the others are weights obtained for public data described in [3]. The Stride parameter sets the stride in samples for point prediction models, and the P phase threshold and the S phase threshold set the threshold of probability for which the phase is detected.

Figure 2. Application form with values filled

[1] Ross, Meier, Hauksson and Heaton (2018). Generalized Seismic Phase Detection with Deep Learning. doi: 10.1785/0120180080.

[2] Zhu, W., & Beroza, G. C. (2019). PhaseNet: a deep-neural-network-based seismic arrival-time picking method. doi: 10.1093/gji/ggy423

[3] Woollam, Münchmeyer, Tilmann et al. (2022). SeisBench — A Toolbox for Machine Learning in Seismology. doi: 10.1785/0220210324.

Produced output

Figure 3. Application output - MiniSeed waveform with phases detection

Figure 4. Application output - wave detection probability

Back to top

Related Documents

• Page:
  P and S Waves Detection Tool user guide
• Page:
  Seismic Catalog Recalculation user guide
• Page:
  Waveform-based seismic event location user guide
• Page:
  Spectral Analysis user guide
• Page:
  Mechanism: Shear-Tensile crack user guide