AzimPy

Estimate horizontal orientation of ocean-bottom seismograph

Copyright (c) 2022–2024 Yasunori Sawaki All rights reserved.

AzimPy is an open-source Python package for estimating the horizontal orientation of ocean-bottom seismographs. This package performs the Rayleigh-wave polarization method (e.g., Stachnik+ 2012; Doran & Laske 2017). One of the main classes OrientOBS, inherited from obspy.clients.fdsn.Client, searches an earthquake catalog for teleseismic events as a web client, and computes Rayleigh-wave polarizations for each event–station pair. This package also provides other classes (e.g., OrientSingle, OrientAnalysis) and functions for statistical analysis of circular data and plotting the estimated azimuths with uncertainties.

Terms of use

• Cite an article below (Sawaki et al., 2023, GJI) and a Zenodo DOI for the specific version of AzimPy when you publish your reseach or make a presentation. The DOI representing the specific version is probably found through the Zenodo page for the latest version.
• This package is under development, so any bug reports and suggestions are welcome!

Use cases

• Sawaki, Y., Yamashita, Y., Ohyanagi, S., Garcia, E.S.M., Ito, A., Sugioka, H., Takahashi, T., Shinohara, M., & Ito, Y. (2023), Seafloor depth controls seismograph orientation uncertainty, Geophys. J. Int., 232(2), 1376–1392, https://doi.org/10.1093/gji/ggac397

How to install

[Recommended] Install AzimPy from PyPI in a new conda environment

You may replace mamba with conda.

$ mamba create -n azimpy-test python=3.10 jupyter astropy "matplotlib>=3.5" "scipy>=1.4" pandas numpy tqdm
$ mamba activate azimpy-test
(azimpy-test) $ mamba install -c conda-forge "obspy>=1.3" (azimpy-test) $ python -m pip install AzimPy

• [Alternative] pip install locally in the environment

$ mamba create -n azimpy-test python=3.10 jupyter
$ mamba activate azimpy-test
(azimpy-test) $ git clone -b v0.3.0 https://github.com/yasuit21/AzimPy.git
(azimpy-test) $ cd AzimPy
(azimpy-test) $ python -m pip install .

Optional installation : rpy2

Installation of R

(azimpy-test) $ mamba install r-essentials r-base r-circular

Note that this installation will take time.

Then, set environent variables.

export R_HOME=/path/to/envs/azimpy-test/lib/R
export R_USER=/path/to/envs/azimpy-test/lib/python3.9/site-packages/rpy2

Installation of rpy2 in PyPI

(azimpy-test) $ python -m pip install rpy2
(azimpy-test) $ python -c "import azimpy"

If no warning or error is returned, the installation has been completed.

Usage

Compute Rayleigh-wave polarization

importobspyasobfromazimpyimportOrientOBS## Initialize web client## Specity the timezone of recording dataobs=OrientOBS(base_url='USGS', timezone=9)
## Query earthquake event catalogobs.query_events(
starttime=ob.UTCDateTime('20200401000000'),
endtime=ob.UTCDateTime('20201001000000'),
minmagnitude=6.0,
maxdepth=100,
orderby='time-asc',
)
## Compute Rayleigh-wave polarization for each event## Raw SAC data should be located in '/path/to/datadir'obs.find_stream(
'/path/to/datadir',
output_path='/path/to/output/stationA1',
polezero_fpath='/path/to/polezero/hoge.paz',
fileformat="sac",
filenameformat=f'*.*.%y%m%d%H%M.sac',
freqmin=1./40, freqmax=1./20,
max_workers=4,
vel_surface=4.0,
time_before_arrival=-20.0,
time_after_arrival=600.0,
distmin=5., distmax=120.,
read_func=ob.read
)

Note that fileformat was renamed as filenameformat in v0.3.0. fileformat denotes the data format of the records. Also, a user-defined read function can be incorpolated in v0.3.0. Specify the function in read_func argument. This would allow us to read data recorded by local formats such as WIN/WIN32, which are not supported by the ObsPy's read function.

Then, the output dataframe will be pickled as stationA1_020_040.pickle under /path/to/output/stationA1 directory. The pickled dataframe can be loaded by pd.read_pickle().

Perform circular statistics and make a plot

Single station

The example uses a single station stationA1.

1. Perform analysis and save as pickled data

   importpandasaspdfromazimpyimportOrientSingle, read_chtbl## Init paramsmin_CC=0.5alpha_CI=0.05## 100(1-a)% CIbootstrap_iteration=5000## The output dataframe of orientationsdf_orient=pd.read_pickle(
   '/path/to/output/stationA1/stationA1_020_040.pickle'
   )
   ## Init OrientSingle for circular statisticsorientsingle_raw=OrientSingle(
   df_orient, 'stationA1', if_selection=False, # w/o bootstrap analysismin_CC=min_CC, weight_CC=True,
   )
   orientsingle_boot=OrientSingle(
   df_orient, 'stationA1', if_selection=True, # perform bootstrap analysismin_CC=min_CC, weight_CC=True, K=5.0,
   bootstrap_iteration=bootstrap_iteration, alpha_CI=alpha_CI
   )
   ## Save orientsingle objects as pickled dataorientsingle_raw.write_obj(
   '/path/to/output/orientsingle/raw/stationA1_020_040.pickle'
   )
   orientsingle_boot.write_obj(
   '/path/to/output/orientsingle/boot/stationA1_020_040.pickle'
   )

2. Plot the result

   ## Load orientsingle objects## You may skip this partorientsingle_raw=OrientSingle.load_obj(
   '/path/to/output/orientsingle/raw/stationA1_020_040.pickle'
   )
   orientsingle_boot=OrientSingle.load_obj(
   '/path/to/output/orientsingle/boot/stationA1_020_040.pickle'
   )
   ## Init a figure with subfiguresfig=plt.figure(figsize=[8,4])
   subfigs=fig.subfigures(nrows=1, ncols=2).flatten()
   ## Plot for `orientsingle_raw`orientsingle_raw.plot(
   polar=True, fig=subfigs[0], in_parentheses='BB',
   add_cbar=True
   )
   subfigs[0].legend(loc=1, bbox_to_anchor=(1,1.15), fontsize='small')
   ## Plot for `orientsingle_boot`orientsingle_boot.plot(
   fig=subfigs[1], in_parentheses='BB',
   )
   subfigs[1].legend(loc=1, bbox_to_anchor=(1,1.15), fontsize='small')
   ## Show or save the figurefig.savefig('/path/to/fig/stationA1_020_040.png')
   plt.show()

Multiple stations

The example uses multiple stations whose names are stationAX.

1. Initialize OrientAnalysis

   fromazimpyimportOrientAnalysisstationList= ['stationA1','stationA2','stationA3','stationA4']
   ## Channeltable including above stations' infodf_chtbl=read_chtbl('/path/to/channeltable.txt')
   df_chtbl=df_chtbl.query('comp.str.endswith("U")')
   ## Init OrientAnalysis for circular statisticsoa_raw=OrientAnalysis(
   if_selection=False, # w/o bootstrap analysisdf_chtbl=df_chtbl, min_CC=min_CC, )
   oa_boot=OrientAnalysis(
   if_selection=True, # perform bootstrap analysisdf_chtbl=df_chtbl, min_CC=min_CC, alpha_CI=alpha_CI, bootstrap_iteration=bootstrap_iteration, )

2. Store the analyzed data or perform analysis
   • If storing the orientation data by OrientSingle

     forstationNameinstationList:
     period=df_chtbl.at[stationName,'period']
     ## Add the dataframe in `oa_raw`oa_raw.add_station(
     orientsingle_path=f'/path/to/output/orientsingle/raw/{stationName}_020_040.pickle',
     period=period,
     )
     oa_boot.add_station(
     orientsingle_path=f'/path/to/output/orientsingle/boot/{stationName}_020_040.pickle',
     period=period,
     )

   • If performing analysis

     forstationNameinstationList:
     period=df_chtbl.at[stationName,'period']
     df_orient=pd.read_pickle(
     f'/path/to/output/{stationName}/{stationName}_020_040.pickle'
     )
     ## Add the dataframe in `oa_raw`## This is actually passed to `OrientSingle`oa_raw.add_station(
     df_orient, stationName, period=period
     )
     ## Add the dataframe in `oa_boot`oa_boot.add_station(
     df_orient, stationName, period=period
     )

3. Plot the results using matplotlib.pyplot
   • Original results w/o bootstrap resampling

   fig=oa_raw.plot()

   • Results of bootstrap analysis

   fig=oa_boot.plot()

4. Save the results

   ## Write dataframe as csv, json, or pickledf_analysis=oa_boot.write(
   "/path/to/output/orientation/StationAX_020_040.csv",
   networkname='StationAX',
   format='csv'
   )

How to read the result CSV file

• Saved dataframe can be loaded as

  fromazimpyimportread_resultdf_analysis=read_result(
  "/path/to/output/orientation/StationAX_020_040.csv"
  )

• The column station is indexed
• The estimated orientation is in the column circular mean. circular_mean and h1azimuth are aliases for circular mean.

  df_analysis.h1azimuth

• The uncertainty is in the column Half 95%CI. uncertainty is the alias for Half 95%CI.

  df_analysis.uncertainty

Note

• SAC format is only supported, but you may use some other formats. Specify the function in read_func argument for OrientOBS..find_stream().
• The observed data files must be located in one directory, where OrientOBS.find_stream() will try to search for necessary input files. No waveform data in websites and repository are available in this package at this moment.
• The author has tested this package in Linux environments (CentOS 7 and WSL Ubuntu 20.04), so it might be incompatible when installed in Windows.
• rpy2 is an optional wrapper to run circular in R language, which performs the Kuiper test.

References

• Sawaki, Y., Yamashita, Y., Ohyanagi, S., Garcia, E.S.M., Ito, A., Sugioka, H., Takahashi, T., Shinohara, M., & Ito, Y., 2023, Seafloor Depth Controls Seismograph Orientation Uncertainty, Geophys. J. Int., 232(2), 1376–1392, https://doi.org/10.1093/gji/ggac397
• Stachnik, J.C., Sheehan, A.F., Zietlow, D.W., et al., 2012, Determination of New Zealand ocean bottom seismometer orientation via Rayleigh-wave polarization. Seismol. Res. Lett., 83, 704–713. https://doi.org/10.1785/0220110128
• Doran, A.K. & Laske, G., 2017, Ocean‐bottom deismometer instrument orientations via automated Rayleigh‐wave arrival‐angle measurements. Bull. Seismol. Soc. Am., 107, 691–708. https://doi.org/10.1785/0120160165
• Takagi, R., Uchida, N., Nakayama, T., et al., 2019, Estimation of the orientations of the S‐net cabled ocean‐bottom sensors. Seismol. Res. Lett., 90, 2175–2187. https://doi.org/10.1785/0220190093
• Concept DOI for the latest AzimPy: 10.5281/zenodo.6972713

Acknowledgments

This package makes use of ObsPy>=1.3.0 for FDSN web client services and processing seismograms.

License

This project is licensed under the MIT License, see the LICENSE for details.