easyQuake 0.5.1

Simplified machine-learning driven earthquake detection, location, and analysis

easyQuake

Simplified machine-learning driven earthquake detection, location, and analysis in one easy-to-implement python package.

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 (in press), easyQuake: Putting machine 
learning to work for your regional seismic network or local earthquake study, Seismological Research 
Letters

Requirements

This code leverages machine-learning for earthquake detection. 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.

• Requires nvidia-cuda-toolkit, obspy, keras==2.3.1, tensorflow-gpu==2.1 (if using multiple GPUs only tensorflow 1.15 is tested), 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==2.1
• Because tensorflow-gpu 2.1 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==2.1
conda install keras
conda install obspy -c conda-forge
pip install easyQuake

Running easyQuake

The 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

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)
    association_continuous(dirname=dirname, project_folder=project_folder, project_code=project_code, maxdist=maxdist, maxkm=maxkm, single_date=single_date, local=True)

cat, dfs = combine_associated(project_folder=project_folder, project_code=project_code)
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.

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=[e-mail]
#SBATCH --mail-type=ALL
#SBATCH --chdir=/drive/group/user/folder
conda init bash
bash
conda activate easyquake
python idaho_example.py

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

• code was reused from obspy (https://github.com/obspy/obspy/wiki), gpd (https://github.com/interseismic/generalized-phase-detection), PhasePApy (https://github.com/austinholland/PhasePApy), and others
• would not be possible without the robust documentation in the obspy project
• this work was developed at the Oklahoma Geological Survey