Improved earthquake aftershocks forecasting model based on long-term memory

Yongwen Zhang, Dong Zhou, Jingfang Fan, Warner Marzocchi, Yosef Ashkenazy, Shlomo Havlin

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


A prominent feature of earthquakes is their empirical laws, including memory (clustering) in time and space. Several earthquake forecasting models, such as the epidemic-type aftershock sequence (ETAS) model, were developed based on these empirical laws. Yet, a recent study [1] showed that the ETAS model fails to reproduce the significant long-term memory characteristics found in real earthquake catalogs. Here we modify and generalize the ETAS model to include short- and long-term triggering mechanisms, to account for the short- and long-time memory (exponents) discovered in the data. Our generalized ETAS model accurately reproduces the short- and long-term/distance memory observed in the Italian and Southern Californian earthquake catalogs. The revised ETAS model is also found to improve earthquake forecasting after large shocks.

Original languageEnglish
Article number042001
JournalNew Journal of Physics
Issue number4
StatePublished - Apr 2021

Bibliographical note

Funding Information:
We thank the Israel Science Foundation (Grants No. 189/19), the joint China-Israel Science Foundation (Grants No. 3132/19), ONR, BSF-NSF (Grant No. 2019740), the EU H2020 project RISE, the BIU Center for Research in Applied Cryptography and Cyber Security, and DTRA (Grants No. HDTRA-1-19-1-0016) for financial support. The Italian earthquake catalog is provided by reference [26] and available on request from the authors. The Southern California catalog can be downloaded from the SCEDC (https://scedc. [27].

Publisher Copyright:
© 2021 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft.


  • ETAS model
  • earthquake memory
  • forecasting


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