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Journal Article

pyCSEP: A Python Toolkit for Earthquake Forecast Developers

Authors

Savran,  William H.
External Organizations;

Bayona,  José A.
External Organizations;

/persons/resource/pciturri

Iturrieta,  Pablo Cristián
2.6 Seismic Hazard and Risk Dynamics, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/khawaja

Khawaja,  Muhammad Asim
2.1 Physics of Earthquakes and Volcanoes, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Bao,  Han
External Organizations;

Bayliss,  Kirsty
External Organizations;

Herrmann,  Marcus
External Organizations;

/persons/resource/ds

Schorlemmer,  Danijel
2.6 Seismic Hazard and Risk Dynamics, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Maechling,  Philip J.
External Organizations;

Werner,  Maximilian J.
External Organizations;

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Fulltext (public)

5015095.pdf
(Postprint), 10MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Savran, W. H., Bayona, J. A., Iturrieta, P. C., Khawaja, M. A., Bao, H., Bayliss, K., Herrmann, M., Schorlemmer, D., Maechling, P. J., Werner, M. J. (2022): pyCSEP: A Python Toolkit for Earthquake Forecast Developers. - Seismological Research Letters, 93, 2858-2870.
https://doi.org/10.1785/0220220033


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5015095
Abstract
The Collaboratory for the Study of Earthquake Predictability (CSEP) is an open and global community whose mission is to accelerate earthquake predictability research through rigorous testing of probabilistic earthquake forecast models and prediction algorithms. pyCSEP supports this mission by providing open‐source implementations of useful tools for evaluating earthquake forecasts. pyCSEP is a Python package that contains the following modules: (1) earthquake catalog access and processing, (2) representations of probabilistic earthquake forecasts, (3) statistical tests for evaluating earthquake forecasts, and (4) visualization routines and various other utilities. Most significantly, pyCSEP contains several statistical tests needed to evaluate earthquake forecasts, which can be forecasts expressed as expected earthquake rates in space–magnitude bins or specified as large sets of simulated catalogs (which includes candidate models for governmental operational earthquake forecasting). To showcase how pyCSEP can be used to evaluate earthquake forecasts, we have provided a reproducibility package that contains all the components required to re‐create the figures published in this article. We recommend that interested readers work through the reproducibility package alongside this article. By providing useful tools to earthquake forecast modelers and facilitating an open‐source software community, we hope to broaden the impact of the CSEP and further promote earthquake forecasting research.