DeepPhasePick

DeepPhasePick (DPP) is a method for automatically detecting and picking seismic phases from local earthquakes based on highly optimized deep neural networks. The method work in a pipeline, where in a first stage phase detection is performed by a Convolutional Neural Network (CNN) on three-component seismograms. Then P- and S-picking is conducted by two Long-Short Term Memory (LSTM) Recurrent Neural Networks (RNN) on the vertical and the two-horizontal components, respectively. The CNN and LSTM networks have been trained using >30,000 seismic records extracted from manually-picked event waveforms originating from northern Chile. DPP additionally computes uncertainties of the predicted phase time onsets, based on the Monte Carlo Dropout (MCD) technique as an approximation of Bayesian inference. Predicted phase time onsets and associated uncertainties generated by DPP can be used to feed a phase associator algorithm as part of an automatic earthquake location procedure.

1. Install

An easy and straightforward way to install DPP is first to directly clone the public repository:

git clone https://github.com/hsotoparada/DeepPhasePick
cd DeepPhasePick

Then, DPP requirements can be manually installed to a dedicated conda environment or by running:

conda env create -f dpp.yml
conda activate dpp

2. DPP Worflow

1. Configuration

Before running DPP, the method needs to be configured by creating an instance of the class Config(), for example using:

import config, data, model, util
dpp_config = config.Config()

Then, parameters controlling different stages in the method can be configured as described below.

1.1 Parameters determining the selected waveform data on which DeepPhasePick is applied are defined using dpp_config.set_data().

For example, to select the waveforms from stations PB01 and PB02 (network CX), and channel HH which are stored in the archive directory archive, and save the results in directory out, run:

dpp_config.set_data(stas=['PB01', 'PB02'], net='CX', ch='HH', archive='archive', opath='out')

1.2 Parameters controlling how seismic waveforms are processed before the phase detection stage are defined using dpp_config.set_data_params().

For example, the following will apply a highpass filter (> .5 Hz) and resample the data to 100 Hz (if it is not already sampled at that sampling rate):

dpp_config.set_data_params(samp_freq=100., st_filter='highpass', filter_opts={'freq': .5})

Note that, since the models in DPP were trained using non-filtered data, this may cause numerous false positive predictions.

1.3 DPP will be applied on the selected seismic data (defined through set_data()) in the time windows defined using dpp_config.set_time().

For example, to create 30-min (1800-seconds) time windows in the period between 2015-04-03T00:00:00 and 2015-04-03T02:00:00 (2 hours), use:

dpp_config.set_time(dt_iter=1800., tstart="2015-04-03T00:00:00", tend="2015-04-03T02:00:00")

Note that the windows created will have the same duration except for the last window, which will be filled with the remainder data until tend in case dt_iter + tstart(last window) > tend.

1.4 Parameters determining how predicted discrete probability time series are computed when running phase detection on seismic waveforms are defined using dpp_config.set_trigger().

For example, the following will compute the discrete probability time series every 20 samples, using a probability threshold of 0.95 for P- and S-phases:

dpp_config.set_trigger(n_shift=20, pthres_p=[0.95, 0.001], pthres_s=[0.95, 0.001])

1.5 Parameters controlling the optional conditions applied for refining preliminary picks obtained from phase detection are defined using dpp_config.set_picking().

For example, the following will remove preliminary picks which are presumed false positive, by applying all of the four optional conditions described in the Text S1 in the Supplementary Material of Soto and Schurr (2021). This is the default and recommended option, especially when dealing with very noise waveforms or filtered seismic waveforms, which may increase the number of presumed false positives.

Then refined pick onsets and their time uncertainties will be computed by applying 20 iterations of Monte Carlo Dropout.

dpp_config.set_picking(run_mcd=True, mcd_iter=20)

More details on the arguments accepted by each of these configuration functions can be seen from the corresponding function documentation.

Note that, instead of configuring DPP by using the functions describe above, each set of parameters can be passed as a dictionary to config.Config(). See the class Config() documentation to use this approach.

2. Seismic Data

DPP method is applied on three-component MiniSEED seismic waveforms.

To read the seismic waveforms into DPP an instance of the class Data() needs to be created, for example using:

dpp_data = data.Data()

Then, the data can be read into DPP for example from a local archive directory using:

dpp_data.read_from_archive(dpp_config)

The local archive needs to have the following commonly used structure: archive/YY/NET/STA/CH

Here YY is year, NET is the network code, STA is the station code and CH is the channel code (e.g., HHZ.D) corresponding to the seismic streams. An example of archived data is included in sample_data/archive.

Alternatively, waveforms can be read from a local directory with no specific structure. For example using:

dpp_data.read_from_directory(dpp_config)

3. Phase Detection and Picking

In order to run the phase detection and picking stages, an instance of the class Model() needs to be created, for example using:

dpp_model = model.Model()

When calling Model(), particular model versions can be specified by the string parameters version_det, version_pick_P, version_pick_S.

Available model versions (more might be added in the future):

• version_det = "20201002":
  best optimized phase detection model described in Soto and Schurr (2021). This is the default value for version_det.

• version_pick_P = version_pick_S = "20201002_1":
  best optimized P- and S-phase picking models described in Soto and Schurr (2021). This is the default value for version_pick_P and version_pick_S.

• version_pick_P = version_pick_S = "20201002_2":
  best optimized picking models, which were trained using 2x (for P phase) and 3x (for S phase) the number of shifted seismic records used in version 20201002_1. Hence enhancing the performance of the phase picking.

Once the models are read into DPP, model information can be retrieved for example by using:

print(dpp_model.model_detection['best_model'].summary())
print(dpp_model.model_picking_P['best_model'].summary())
print(dpp_model.model_picking_S['best_model'].summary())

3.1 To run the phase detection on the selected seismic waveforms use:

dpp_model.run_detection(dpp_config, dpp_data)

This will compute discrete class probability time series from predictions, which are used to obtain preliminary phase picks.

The optional parameter save_dets = True (default is False) will save a dictionary containing the class probabilities and preliminary picks to opath/*/pick_stats if needed for further use. Here opath is the output directory defined in the DPP configuration (see function set_data()).

The optional parameter save_data = True (default is False) will save a dictionary containing the seismic waveform data used for phase detection to the same directory.

3.2 Next the phase picking can be run to refine the preliminary picks, using:

dpp_model.run_picking(dpp_config, dpp_data)

The optional parameter save_plots = True (default is True) will save figures of individual predicted phase onsets to opath/*/pick_plots if run_mcd=True. These figures are similar to the subplots in Figure 3 of Soto and Schurr (2021).

The optional parameter save_picks = True (default is False) will save a dictionary containing relevant information of preliminary and refined phase picks to opath/*/pick_stats.

The optional parameter save_stats = True (default is True) will save statistics of predicted phase onsets to the output file opath/*/pick_stats/pick_stats.
If run_mcd=False, the ouput file will contain the following 4 columns:

station, phase (P or S), pick number, detection probability, tons (preliminary; UTC)

If run_mcd=True, the output file will contain the previous columns plus the following additional columns with the results from the MCD iterations:

tons (refined; UTC), tons (preliminary; within picking window) [s], tons (refined; within picking window) [s], tons_err (before onset) [s], tons_err (after onset) [s], pick class, pb, pb_std

Here tons is the predicted phase time onset with uncertainty tons_err and class pick class. These fields, as well as pb and pb_std, are described in Figure 3 of Soto and Schurr (2021).

4. Plotting predicted P and S phases

Figures including continuous waveforms together with predicted P and S phases can be created using:

util.plot_predicted_phases(dpp_config, dpp_data, dpp_model)

Three additional optional parameters in this function allow to modify the figures layout (see function documentation). The parameter plot_comps defines which seismogram components are plotted. The parameter plot_probs defines which class probability time series are plotted. Finally, the parameter shift_probs controls if the plotted probability time series are shifted in time, according to the optimized hyperparameter values defining the picking window for each class (see Figura S1 in Soto and Schurr, 2021).

For example, the following will plot the predicted picks on the vertical ('Z') and north ('N') seismogram components, and the probability time series for P- and S-phase classes shifted in time as described above.

util.plot_predicted_phases(dpp_config, dpp_data, dpp_model, plot_comps=['Z','N'], plot_probs=['P','S'], shift_probs=True)

Reference:

• Soto, H., and Schurr, B. DeepPhasePick: A method for detecting and picking seismic phases from local earthquakes based on highly optimized convolutional and recurrent deep neural networks. Geophysical Journal International (2021). https://doi.org/10.1093/gji/ggab266

Thanks:

The development of DeepPhasePick method has received financial support from

• The HAzard and Risk Team (HART) initiative of the GFZ German Research Centre for Geosciences in collaboration with the Institute of GeoSciences, Energy, Water and Environment of the Polytechnic University Tirana, Albania and the KIT Karlsruhe Institute of Technology.