1. Install miniconda and requirements
- Download PhaseNet repository
git clone https://github.com/wayneweiqiang/PhaseNet.git
cd PhaseNet- Install to default environment
conda env update -f=env.yaml -n base
- Install to "phasenet" virtual envirionment
conda env create -f env.yaml conda activate phasenet
- For Mac ARM chip, please use the env_mac.yaml file
conda env create -f env_mac.yaml conda activate phasenet
Located in directory: model/190703-214543
- Zhu, Weiqiang, and Gregory C. Beroza. "PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method." arXiv preprint arXiv:1803.03211 (2018).
- Liu, Min, et al. "Rapid characterization of the July 2019 Ridgecrest, California, earthquake sequence from raw seismic data using machine‐learning phase picker." Geophysical Research Letters 47.4 (2020): e2019GL086189.
- Park, Yongsoo, et al. "Machine‐learning‐based analysis of the Guy‐Greenbrier, Arkansas earthquakes: A tale of two sequences." Geophysical Research Letters 47.6 (2020): e2020GL087032.
- Chai, Chengping, et al. "Using a deep neural network and transfer learning to bridge scales for seismic phase picking." Geophysical Research Letters 47.16 (2020): e2020GL088651.
- Tan, Yen Joe, et al. "Machine‐Learning‐Based High‐Resolution Earthquake Catalog Reveals How Complex Fault Structures Were Activated during the 2016–2017 Central Italy Sequence." The Seismic Record 1.1 (2021): 11-19.
See examples in the notebook: example_batch_prediction.ipynb
PhaseNet currently supports four data formats: mseed, sac, hdf5, and numpy. The test data can be downloaded here:
wget https://github.com/wayneweiqiang/PhaseNet/releases/download/test_data/test_data.zip
unzip test_data.zip
- For mseed format:
python phasenet/predict.py --model=model/190703-214543 --data_list=test_data/mseed.csv --data_dir=test_data/mseed --format=mseed --amplitude --response_xml=test_data/stations.xml --batch_size=1 --sampling_rate=100
python phasenet/predict.py --model=model/190703-214543 --data_list=test_data/mseed2.csv --data_dir=test_data/mseed --format=mseed --amplitude --response_xml=test_data/stations.xml --batch_size=1 --sampling_rate=100
- For sac format:
python phasenet/predict.py --model=model/190703-214543 --data_list=test_data/sac.csv --data_dir=test_data/sac --format=sac --batch_size=1
- For numpy format:
python phasenet/predict.py --model=model/190703-214543 --data_list=test_data/npz.csv --data_dir=test_data/npz --format=numpy
- For hdf5 format:
python phasenet/predict.py --model=model/190703-214543 --hdf5_file=test_data/data.h5 --hdf5_group=data --format=hdf5
- For a seismic array (used by QuakeFlow):
python phasenet/predict.py --model=model/190703-214543 --data_list=test_data/mseed_array.csv --data_dir=test_data/mseed_array --stations=test_data/stations.json --format=mseed_array --amplitude
Notes:
-
The reason for using "--batch_size=1" is because the mseed or sac files usually are not the same length. If you want to use a larger batch size for a good prediction speed, you need to cut the data to the same length.
-
Remove the "--plot_figure" argument for large datasets, because plotting can be very slow.
Optional arguments:
usage: predict.py [-h] [--batch_size BATCH_SIZE] [--model_dir MODEL_DIR]
[--data_dir DATA_DIR] [--data_list DATA_LIST]
[--hdf5_file HDF5_FILE] [--hdf5_group HDF5_GROUP]
[--result_dir RESULT_DIR] [--result_fname RESULT_FNAME]
[--min_p_prob MIN_P_PROB] [--min_s_prob MIN_S_PROB]
[--mpd MPD] [--amplitude] [--format FORMAT]
[--s3_url S3_URL] [--stations STATIONS] [--plot_figure]
[--save_prob]
optional arguments:
-h, --help show this help message and exit
--batch_size BATCH_SIZE
batch size
--model_dir MODEL_DIR
Checkpoint directory (default: None)
--data_dir DATA_DIR Input file directory
--data_list DATA_LIST
Input csv file
--hdf5_file HDF5_FILE
Input hdf5 file
--hdf5_group HDF5_GROUP
data group name in hdf5 file
--result_dir RESULT_DIR
Output directory
--result_fname RESULT_FNAME
Output file
--min_p_prob MIN_P_PROB
Probability threshold for P pick
--min_s_prob MIN_S_PROB
Probability threshold for S pick
--mpd MPD Minimum peak distance
--amplitude if return amplitude value
--format FORMAT input format
--stations STATIONS seismic station info
--plot_figure If plot figure for test
--save_prob If save result for test
- The output picks are saved to "results/picks.csv" on default
| file_name | begin_time | station_id | phase_index | phase_time | phase_score | phase_amp | phase_type |
|---|---|---|---|---|---|---|---|
| 2020年10月01日T00:00* | 2020年10月01日T00:00:00.003 | CI.BOM..HH | 14734 | 2020年10月01日T00:02:27.343 | 0.708 | 2.4998866231208325e-14 | P |
| 2020年10月01日T00:00* | 2020年10月01日T00:00:00.003 | CI.BOM..HH | 15487 | 2020年10月01日T00:02:34.873 | 0.416 | 2.4998866231208325e-14 | S |
| 2020年10月01日T00:00* | 2020年10月01日T00:00:00.003 | CI.COA..HH | 319 | 2020年10月01日T00:00:03.193 | 0.762 | 3.708662269972206e-14 | P |
Notes:
- The phase_index means which data point is the pick in the original sequence. So phase_time = begin_time + phase_index / sampling rate. The default sampling_rate is 100Hz
A complete earthquake detection workflow can be found in the QuakeFlow project.
See details in the notebook: example_interactive.ipynb
- Download a small sample dataset:
wget https://github.com/wayneweiqiang/PhaseNet/releases/download/test_data/test_data.zip unzip test_data.zip
- Start training from the pre-trained model
python phasenet/train.py --model_dir=model/190703-214543/ --train_dir=test_data/npz --train_list=test_data/npz.csv --plot_figure --epochs=10 --batch_size=10
- Check results in the log folder