ETAS: Epidemic-Type Aftershock Sequence

This code was written for the following articles:

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

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

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

To cite the code, plase use its DOI, and cite the relevant article(s).<br/> For more documentation on the code, see the (electronic supplement of the) articles.<br/> For Probabilistic, Epidemic-Type Aftershock Incomplenteness, see PETAI.<br/> In case of questions or comments, contact me: leila.mizrahi@sed.ethz.ch. <br/> <br/>

To install, run <code>pip install git+https://github.com/lmizrahi/etas</code> <br/> <br/>

Contents:

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