pyCSEP: A Python Toolkit for Earthquake Forecast Developers

William Savran*, Jose A Bayona, Pablo Iturrieta, Khawaja Asim, Han Bao, Kirsty Bayliss, Marcus Herrmann, Danijel Schorlemmer, Philip Maechling, Max Werner

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

18 Citations (Scopus)
89 Downloads (Pure)

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 recreate the figures published in this article. We recommend that interested readers work through the reproducibility package alongside this manuscript. By providing useful tools to earthquake forecast modelers and facilitating an open-source software community, we hope to broaden the impact of the Collaboratory for the Study of Earthquake Predictability (CSEP) and further promote earthquake forecasting research.
Original languageEnglish
Pages (from-to)2858-2870
Number of pages13
JournalSeismological Research Letters
Volume93
Issue number5
DOIs
Publication statusPublished - 27 Jul 2022

Bibliographical note

Publisher Copyright:
© Seismological Society of America.

Keywords

  • Software development
  • Reproducibility of results
  • Earthquake interaction, forecasting, and prediction
  • Statistical seismology
  • Probabilistic forecasting

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