79seismo/etas

calibrate ETAS, simulate using ETAS, estimate completeness magnitude & magnitude frequency distribution

ETAS: Epidemic-Type Aftershock Sequence

This code was written for the following articles:

Leila Mizrahi, Shyam Nandan, Stefan Wiemer 2021;
The Effect of Declustering on the Size Distribution of Mainshocks.
Seismological Research Letters; doi: https://doi.org/10.1785/0220200231

The option for (space-time-)varying completeness magnitude in the parameter inversion is described in:

Leila Mizrahi, Shyam Nandan, Stefan Wiemer 2021;
Embracing Data Incompleteness for Better Earthquake Forecasting. (Section 3.1)
Journal of Geophysical Research: Solid Earth; doi: https://doi.org/10.1029/2021JB022379

To cite the code, plase use its DOI, and if appropriate, please cite the article(s).
For more documentation on the code, see the (electronic supplement of the) articles.
For Probabilistic, Epidemic-Type Aftershock Incomplenteness, see PETAI.
In case of questions or comments, contact me: [email protected].

To install, run pip install git+https://github.com/lmizrahi/etas

Contents:

• runnable_code/ scripts to be run for parameter inversion or catalog simulation
  • ch_forecast.py estimates ETAS parameters and creates 100 simulations using the Swiss catalog
  • estimate_mc.py estimates constant completeness magnitude for a set of magnitudes
  • invert_etas.py calibrates ETAS parameters based on an input catalog (option for varying mc, and option to fix certain parameters available)
  • simulate_catalog.py simulates a synthetic catalog
  • simulate_catalog_continuation.py simulates a continuation of a catalog, after the parameters have been inverted. if you run this many times, you get a forecast. this only works if you run invert_etas.py beforehand.
  • visualize_fit.py makes plots which visualize the model fit to the data. this only works if you run invert_etas.py beforehand, and set store_pij = True.
• config/ configuration files for running the scripts in runnable_code/
  • names should be self-explanatory.
• input_data/ input data to run example inversions and simulations
  • california_shape.npy shape of polygon around California
  • ch_catalog.csv Swiss catalog 1972 - 2021, used by ch_forecast.py
  • ch_rect.npy shape of rectangle around Switzerland
  • example_catalog.csv to be inverted by invert_etas.py
  • example_catalog_mc_var.csv to be inverted by invert_etas.py when varying mc mode is used
  • magnitudes.npy example magnitudes for mc estimation
• output_data/ does not contain anything.
  • your output goes here
• etas/
  • here is where all the important functions algorithms are defined