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easyQuake-1.3.2
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مشاهده بیشترتوضیحات
Simplified machine-learning driven earthquake detection, location, and analysis
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | easyQuake-1.3.2 |
| نام | easyQuake |
| نسخه کتابخانه | 1.3.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Jake Walter |
| ایمیل نویسنده | jakeiwalter@gmail.com |
| آدرس صفحه اصلی | https://github.com/jakewalter/easyQuake |
| آدرس اینترنتی | https://pypi.org/project/easyQuake/ |
| مجوز | - |
# easyQuake
Simplified machine-learning driven earthquake detection, location, and analysis in one easy-to-implement python package.
For more details, see the documentation: https://easyquake.readthedocs.io/
On most systems you should be able to simply:
```
pip install easyQuake
```
To stay on the bleeding edge of updates:
```
pip install easyQuake --upgrade
```
Or if you need to tweak something, like the number of GPUs in gpd_predict, you could:
```
git clone https://github.com/jakewalter/easyQuake.git
cd easyQuake
pip install .
```
If you find this useful, please cite:
```
Walter, J. I., P. Ogwari, A. Thiel, F. Ferrer, and I. Woelfel (2021), easyQuake: Putting machine
learning to work for your regional seismic network or local earthquake study, Seismological Research
Letters, 92(1): 555–563, https://doi.org/10.1785/0220200226
```
## Requirements
This code leverages machine-learning for earthquake detection with the choice of the GPD (https://github.com/interseismic/generalized-phase-detection), EQTransformer (https://github.com/smousavi05/EQTransformer), or PhaseNet (https://github.com/AI4EPS/PhaseNet) pickers. You should have suitable hardware to run CUDA/Tensorflow, which usually means some sort of GPU. This has been tested on servers with nvidia compute cards and modest multi-core desktop with consumer gaming nvidia card (e.g. Geforce 1050 Ti). The event-mode can be run efficiently enough on a laptop.
* Most tested configuration includes nvidia-cuda-toolkit, obspy, keras, tensorflow-gpu==2.2, basemap
* I've found that the the easiest way to install cuda, tensorflow, and keras is through installing Anaconda python and running ```conda install tensorflow-gpu==2.2```
* Because tensorflow-gpu 2.2 requires python 3.7 (not the latest version), you might find an easier road creating a new environment:
```
conda create -n easyquake python=3.7 anaconda
conda activate easyquake
conda install tensorflow-gpu==2.2
conda install keras
conda install obspy -c conda-forge
pip install easyQuake
```
## Running easyQuake
The first example is a simple one in "event mode" - try it:
```
from easyQuake import detection_association_event
detection_association_event(project_folder='/scratch', project_code='ok', maxdist = 300, maxkm=300, local=True, machine=True, latitude=36.7, longitude=-98.4, max_radius=3, approxorigintime='2021-01-27T14:03:46', downloadwaveforms=True)
```
This next example runs easyQuake for a recent M6.5 earthquake in Idaho for the 2 days around the earthquake (foreshocks and aftershocks). The catalog from running the example is in the examples folder: https://github.com/jakewalter/easyQuake/blob/master/examples/catalog_idaho_2days.xml
If you don't have a suitable computer, try it in Google Colab
[](https://colab.research.google.com/github/jakewalter/easyQuake/blob/master/examples/easyquake_demo.ipynb)
```
from easyQuake import download_mseed
from easyQuake import daterange
from datetime import date
from easyQuake import combine_associated
from easyQuake import detection_continuous
from easyQuake import association_continuous
from easyQuake import magnitude_quakeml
from easyQuake import simple_cat_df
import matplotlib.pyplot as plt
maxkm = 300
maxdist=300
lat_a = 42
lat_b = 47.5
lon_a = -118
lon_b = -111
start_date = date(2020, 3, 31)
end_date = date(2020, 4, 2)
project_code = 'idaho'
project_folder = '/data/id'
for single_date in daterange(start_date, end_date):
print(single_date.strftime("%Y-%m-%d"))
dirname = single_date.strftime("%Y%m%d")
download_mseed(dirname=dirname, project_folder=project_folder, single_date=single_date, minlat=lat_a, maxlat=lat_b, minlon=lon_a, maxlon=lon_b)
detection_continuous(dirname=dirname, project_folder=project_folder, project_code=project_code, single_date=single_date, machine=True,local=True)
#run it with EQTransformer instead of GPD picker
#detection_continuous(dirname=dirname, project_folder=project_folder, project_code=project_code, machine=True, machine_picker='EQTransformer', local=True, single_date=single_date)
#PhaseNet
#detection_continuous(dirname=dirname, project_folder=project_folder, project_code=project_code, machine=True, machine_picker='PhaseNet', local=True, single_date=single_date)
association_continuous(dirname=dirname, project_folder=project_folder, project_code=project_code, maxdist=maxdist, maxkm=maxkm, single_date=single_date, local=True)
### IMPORTANT - must call the specific picker to create association and catalogs specific to that picker within each dayfolder!!
#association_continuous(dirname=dirname, project_folder=project_folder, project_code=project_code, maxdist=maxdist, maxkm=maxkm, single_date=single_date, local=True, machine_picker='EQTransformer')
#association_continuous(dirname=dirname, project_folder=project_folder, project_code=project_code, maxdist=maxdist, maxkm=maxkm, single_date=single_date, local=True, machine_picker='PhaseNet')
cat, dfs = combine_associated(project_folder=project_folder, project_code=project_code)
#cat, dfs = combine_associated(project_folder=project_folder, project_code=project_code, machine_picker='EQTransformer')
#cat, dfs = combine_associated(project_folder=project_folder, project_code=project_code, machine_picker='PhaseNet')
cat = magnitude_quakeml(cat=cat, project_folder=project_folder,plot_event=True)
cat.write('catalog_idaho.xml',format='QUAKEML')
catdf = simple_cat_df(cat)
plt.figure()
plt.plot(catdf.index,catdf.magnitude,'.')
```
## Tips for successful outputs
Within your systems, consider running driver scripts as nohup background processes ```nohup python ~/work_dir/okla_daily.py &```. In this way, one could ```cat nohup.out | grep Traceback``` to understand python errors or ```grep nohup.out | Killed``` to understand when the system runs out of memory.
## Video intros to easyQuake
Most recent updates, recorded for the 2021 SSA Annual meeting: https://www.youtube.com/watch?v=bjBqPL9pD5w
Recorded for the fall 2020 Virtual SSA Eastern Section meeting: https://www.youtube.com/watch?v=coS2OwTWO3Y
## About EasyQuake
Stay up to date on the latest description of EasyQuake contents: https://easyquake.readthedocs.io/en/latest/About.html
## Running easyQuake with SLURM
If you have access to shared computing resources that utilize SLURM, you can drive easyQuake by making a bash script to run the example code or any code (thanks to Xiaowei Chen at OU). Save the following to a drive_easyQuake.sh and then run it
```
#!/bin/bash
#
#SBATCH --partition=gpu_cluster
#SBATCH --ntasks=1
#SBATCH --mem=1024
#SBATCH --output=easyquake_%J_stdout.txt
#SBATCH --error=easyquake_%J_stderr.txt
#SBATCH --time=24:00:00
#SBATCH --job-name=easyquake
#SBATCH --mail-user=user@school.edu
#SBATCH --mail-type=ALL
#SBATCH --chdir=/drive/group/user/folder
conda init bash
bash
conda activate easyquake
python idaho_example.py
```
## Version brief notes
Version 1.3 (11/22/2022) = PhaseNet now included, in addition to GPD and EQTransformer pickers. Numerous other bugs squashed.
Version 1.2 (8/1/2022) - Rewrote the non-ML picker to be easier to work with (recursive_sta_lta from obpsy) and include input of those parameters within detection_continuous function.
Version 0.9 (2/23/2022) - Modules to cut easyQuake event waveforms from continuous data (cut_event_waveforms) and module for converting easyQuake catalog (or any QuakeML-formatted catalog) to HDF5 (quakeML_to_hdf5) for training new ML models
Version 0.8 (7/30/2021) - Several major bug fixes and improved controls for Hypoinverse location
Verson 0.6 (2/24/2021) - Implemented choice of GPD or EQTransformer pickers for the picking stage
Version 0.5 (2/10/2021) - includes embedded hypoinverse location functionality, rather than the simple location with the associator.
## License
This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details
## Acknowledgments
* code was used or otherwise changed to suit our purposes from obspy (https://github.com/obspy/obspy/wiki), GPD (https://github.com/interseismic/generalized-phase-detection), PhasePApy (https://github.com/austinholland/PhasePApy), EQTransformer (https://github.com/smousavi05/EQTransformer), PhaseNet (https://github.com/AI4EPS/PhaseNet), and others
* would not be possible without the robust documentation in the obspy project
* this work was developed at the Oklahoma Geological Survey
نیازمندی
| مقدار | نام |
|---|---|
| - | basemap |
| ==2.3.1 | keras |
| - | obspy |
| - | pandas |
| ==2.2 | tensorflow |
| ==2.10.0 | h5py |
| - | tqdm |
| ==2.2 | tensorflow-gpu |
| ==3.20.* | protobuf |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7, <=3.8 | Python |
نحوه نصب
نصب پکیج whl easyQuake-1.3.2:
pip install easyQuake-1.3.2.whl
نصب پکیج tar.gz easyQuake-1.3.2:
pip install easyQuake-1.3.2.tar.gz