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

6 Scopus citations

Abstract

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
Volume23
Issue number4
DOIs
StatePublished - 1 Apr 2021

Keywords

  • ETAS model
  • earthquake memory
  • forecasting

ASJC Scopus subject areas

  • Physics and Astronomy (all)

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