calibrate ETAS, simulate using ETAS, estimate completeness magnitude & magnitude frequency distribution
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
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
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 catalogestimate_mc.py
estimates constant completeness magnitude for a set of magnitudesinvert_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 catalogsimulate_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/
input_data/
input data to run example inversions and simulations
california_shape.npy
shape of polygon around Californiach_catalog.csv
Swiss catalog 1972 - 2021, used by ch_forecast.py
ch_rect.npy
shape of rectangle around Switzerlandexample_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 usedmagnitudes.npy
example magnitudes for mc estimationoutput_data/
does not contain anything.
etas/