EPISODES Platform Documentation

Application User Guides > Source and Shaking Parameters Estimation

P and S Waves Detection Tool user guide ​

TIP

This document contains instructions for running application P and S Waves Detection Tool within the EPISODES Platform. The application is a tool for automatic detection of the first arrival time of the P and S waves. The input seismogram is scanned to find potential events. It uses Deep Learning technique based on a convolutional neural network to process the seismograms and detect the first arrival times. The models are based on Ross, Meier, Hauksson and Heaton (2018) and Zhu and Beroza (2019). The implementation is based on SeisBench a toolbox for machine learning in seismology (Woollam, Münchmeyer, Tilmann et al. 2022).
To obtain more general information about working with applications within the Platform, see Applications Quick Start Guide.

NOTE

CATEGORY Source and Shaking Parameters Estimation

KEYWORDS Waveform viewing, Picking on waveform

CITATION If you use the results or visualizations retrieved from this application in a publication, then you must cite the data source as follows:

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

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

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

Orlecka-Sikora, B., Lasocki, S., Kocot, J. et al. (2020) An open data infrastructure for the study of anthropogenic hazards linked to georesource exploitation., Sci Data 7, 89, doi: 10.1038/s41597-020-0429-3.

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.
Figure 1. Application input files specification.

Filling form values ​

The application form consists of five parameters - see Figure 2. The first parameter allows you to choose the model used for the the detection - GPD or PhaseNet (see [1] and [2], respectively). Each model was originally trained on a set of data described in the respective publication, the weights obtained based on that training are set as default, however, different weights may be chosen depending on the characteristic of the input data by modifying the Pretrained model weights field. The Original option should be set to use the default, original weights published by the authors of the models, while to use one of the weights obtained for public data described in [3] use one of the ETHZ, GEOFON, INSTANCE, Iquique, LenDB, NEIC, SCEDC and STEAD options. Additionally, for the PhaseNet model, there are also weights obtained by training on BOGDANKA and LGCD1 episodes available. See Figure 3 for detailed view of the available weights.

To configure the sensitivity of the phase detection, use the P phase threshold and the S phase threshold (see Figure 2), which determine the threshold of the probability for which we assume a detection.

Figure 2. Application form with values filled.
Figure 2. Application form with values filled.
Figure 3. Available values of the Pretrained model weights.
Figure 3. Available values of the Pretrained model weights.

1. The weights for the LGCD episode are not well suited for S phase detection.

Produced output ​

The main output of the application is a QuakeML file containing picks marking the phase detections. The file is named after the input MiniSEED file with -picks.xml suffix added. The picks are displayed on the original input file within the application output view (Figure 4).

Figure 4. Application output - MiniSEED waveform with resulting picks displayed.
Figure 4. Application output - MiniSEED waveform with resulting picks displayed.

As an additional information, the application provides also the detection probability values, saved to the file named after the input MiniSEED with -annotations.mseed suffix added. The probability can be examined (see Figure 5 and Figure 6) after expanding the Wave detection probabilityoption (bottom of Figure 4).

Figure 5. Application output - wave detection probability
Figure 5. Application output - wave detection probability
Figure 6. Details of detection probability plots, for phase P, phase S and noise.
Figure 6. Details of detection probability plots, for phase P, phase S and noise.

[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.