Group A, Poster #163, Earthquake Forecasting and Predictability (EFP)

Extending pyCSEP: A Python Toolkit for Earthquake Forecast Developers

Philip J. Maechling, Fabio Silva, William H. Savran, Pablo C. Iturrieta, Kenny Graham, José A. Bayona, Khawaja M. Asim, Han Bao, Kirsty Bayliss, Marcus Herrmann, & Maximilian J. Werner
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Poster Presentation

2023 SCEC Annual Meeting, Poster #163, SCEC Contribution #12877 VIEW PDF
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. The CSEP group has recently developed the pyCSEP software toolkit to evaluate earthquake forecasts within CSEP testing centers and by the wider earthquake forecast research community. The pyCSEP modules for working with probabilistic earthquake forecasts include: (1) earthquake catalog access and processing, (2) representations of probabilistic earthquake forecast models, (3) statistical tests for evaluating earthquake forecasts, and ...(4) visualization routines and various other utilities. pyCSEP statistical test methods are designed to evaluate two types of forecasts: those expressed as expected rates in space-magnitude bins, and those specified as sets of simulated catalogs. Recent updates to pyCSEP provide support for accessing Global Centroid Moment Tensor (CMT), Italian (BSI), and New Zealand (GNS) earthquake catalogs, for defining and evaluating forecasts defined on quadtree grids, for performing non-Poissionian tests, and for plotting p-values and confidence ranges in consistency tests. The pyCSEP software development process is community-oriented, and many of these software improvements were contributed by members of the CSEP research community. The CSEP community envisions pyCSEP forecasts, data, and tests as important elements in a larger ecosystem of earthquake forecasting software codes which can be used together to quantify hazards and risks due to earthquakes.
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Citation: Maechling, P. J., Silva, F., Savran, W. H., Iturrieta, P. C., Graham, K., Bayona, J. A., Asim, K. M., Bao, H., Bayliss, K., Herrmann, M., & Werner, M. J. (2023, 09). Extending pyCSEP: A Python Toolkit for Earthquake Forecast Developers. Poster Presentation at 2023 SCEC Annual Meeting.
Keywords: CSEP, Forecasting, pyCSEP, Software
Relevant SCEC Themes:
Probabilistic Seismic Hazard Analysis
Operational Earthquake Forecasting
Post-Earthquake Rapid Response