FINDRES

A Python code for detecting true Repeating Earthquakes from Self-similar Waveforms (FINDRES)

Abstract

The software package FINDRES is an open-source seismological software. FINDRES is designed to discriminate repeating earthquakes starting from a family of candidate repeating earthquakes, based on the cross-correlation values and S-P time difference between pairs of earthquakes (estimated using cross-spectrum).

Motivation and significance

The code FINDRES is inspired to previously published methods that combine both seismic waveform similarity, using cross-correlation function, and differential S-P travel time measured at each seismic station (Chen et al., 2008 and Shakibay Senobari and Funning, 2019). The code is versatile and works with and without P and S-wave phase pickings. At the moment the reading of phases in Hypoellipse (Lahr 1999), Hypoinverse (Klein, 2002), NonlinLoc (Lomax et al. 2000), and QuakeML (https://quake.ethz.ch/quakeml) format are implemented. The code has been tested using synthetic and real data, providing accurate results. It contributes to the implementations of open-source Python packages in seismology aiming to support the activities of researchers and the reproducibility of scientific results.

Requirements

Python installed to run the program (version 3.8 or more) is required [http://python.org].

How to use it

The package has few dependencies; the recommended way of installing them is via the Conda package manager. You can create a test environment using

foo@bar ~ % conda create -n findres-test python=3.8 numpy=1.21 pandas tqdm pyyaml obspy -c conda-forge

Remember to activate the environment with

foo@bar ~ % conda activate findres-test

The package multitaper is required, you can install it using pip

foo@bar ~ % pip install multitaper

The package FINDRES is registered on PyPi, you can install it using pip

(findres-test) foo@bar ~ % pip install findres

Otherwise, you can download or clone this repository in a specific path, then add FINDRES/bin to PATH and FINDRES to PYTHONPATH. This is useful if you want to modify the code, work with your own version of the code, get the latest one, or to extend custom_formats.py to support your own formats. For example, if the repository is located at /home/foo/dev/FINDRES you can append the new paths to the relevant environment variables with

(findres-test) foo@bar ~ % export PATH=$PATH:/home/foo/dev/FINDRES/bin
(findres-test) foo@bar ~ % export PYTHONPATH=$PYTHONPATH:/home/foo/dev/FINDRES

After that, you'll have the findres script in your path (you can check using which that you are calling the script located at the right path).

(findres-test) foo@bar ~ % findres --help
usage: ./bin/findres [-h] [--phase_file PHASE_FILE] [--phase_type {hypoinv,nll,quakeml,hypoel,rise_custom,hyposynth}] [--taup_model TAUP_MODEL] [--rebuild_model] [--graphics_dir GRAPHICS_DIR]
                     [--graphics_format GRAPHICS_FORMAT] [--hypodd] [--include INCLUDE] [--log LOG] [--progress]
                     catalogue inventory parameters output

positional arguments:
  catalogue             Modified ZMAP catalogue containing repeater candidates, their MSEED location, and names
  inventory             Inventory of the stations data
  parameters            Numerical parameters file
  output                Output YAML file

optional arguments:
  -h, --help            show this help message and exit
  --phase_file PHASE_FILE
                        Catalogue containing the phase picking and other information if available (default: None)
  --phase_type {hypoinv,nll,quakeml,hypoel,rise_custom,hyposynth}
                        Type of PHASE_FILE (default: None)
  --picker              Enable picker (default: False) 
  --taup_model TAUP_MODEL
                        Velocity model file without extension (assumed to be .tvel) if available (default: None)
  --rebuild_model       Force the rebuild of the velocity model (regenerate ObsPy local cache) (default: False)
  --graphics_dir GRAPHICS_DIR
                        Where to put the graphics relative to OUTPUT_DIR, if not specified the graphics is not generated (default: None)
  --graphics_format GRAPHICS_FORMAT
                        Graphics format, must be one of the extensions recognized by matplotlib (default: pdf)
  --hypodd              Whether to output hypodd input files (default: False)
  --include INCLUDE     Include only the listed events from catalogue (default: None)
  --log LOG             Log level (default: warning)
  --progress            Show progress bar (default: False)

You can run a test using the data provided in this repository and accessing the following folder

(findres-test) foo@bar ~ % cd /home/foo/dev/FINDRES/data/california

For example the command to analyse the data and produce graphics while showing a progress bar is

(findres-test) foo@bar california % findres cre.zmap inventory.xml parameters.yaml results_reference --phase_file=phases_hypoinv.txt --phase_type=hypoinv --taup_model=ncmodel --graphics_dir=figures --progress

The command to analyse the data without graphics (speeding up the time computation) while showing a progress bar is

(findres-test) foo@bar california % findres cre.zmap inventory.xml parameters.yaml results_reference --phase_file=phases_hypoinv.txt --phase_type=hypoinv --taup_model=ncmodel --progress

The command to analyse the data without graphics (speeding up the time computation) and producing the HypodDD output file while showing a progress bar is

(findres-test) foo@bar california % findres cre.zmap inventory.xml parameters.yaml results_reference --phase_file=phases_hypoinv.txt --phase_type=hypoinv --taup_model=ncmodel --progress --hypodd

The Hypodd output file can be used to locate the events. HypoDD software (Waldhauser, F., and W.L. Ellsworth 2000) must be installed. To run an exmaple you can accesss the following directory:

(findres-test) foo@bar california % cd Hypodd_svd_1_RES

and type:

(findres-test) foo@bar Hypodd_svd_1_RES % HypoDD hypoDD.inp

The numerical parameters are set using the parameters.yaml file. The name of the fields are self-explicative and more extensive information can be found in the provided example.

References

Sugan, M., Campanella, S., Vuan, A., and Shakibay Senobari, N., (2022). A Python Code for detecting true Repeating Earthquakes from Self-similar Waveforms (FINDRES). Seismological Research Letters, 93, 2847–2857, https://doi.org/10.1785/0220220048

Shakibay Senobari, N., and Funning, G. J., (2019). Widespread Fault Creep in the Northern San Francisco Bay Area Revealed by Multistation Cluster Detection of Repeating Earthquakes, Geophysical Research Letters, 46(12), 6425-6434, https://doi.org/10.1029/2019GL082766.

Chen, K. H., Nadeau, R. M, and Rau, R.-J. (2008). Characteristic repeating microearthquakes on an arc-continent collision boundary – the Chihshang fault of eastern Taiwan, Earth Planet. Sci. Lett., 276, 262–272, https://doi.org/10.1016/j.epsl.2008.09.021.