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Journal Article

Improved earthquake aftershocks forecasting model based on long-term memory

Authors

Zhang,  Yongwen
External Organizations;

Zhou,  Dong
External Organizations;

/persons/resource/Jingfang.Fan

Fan,  Jingfang
Potsdam Institute for Climate Impact Research;

Marzocchi,  Warner
External Organizations;

Ashkenazy,  Yosef
External Organizations;

Havlin,  Shlomo
External Organizations;

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Fulltext (public)

Zhang_2021_New_J._Phys._23_042001.pdf
(Publisher version), 3MB

Supplementary Material (public)
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Citation

Zhang, Y., Zhou, D., Fan, J., Marzocchi, W., Ashkenazy, Y., Havlin, S. (2021): Improved earthquake aftershocks forecasting model based on long-term memory. - New Journal of Physics, 23, 042001.
https://doi.org/10.1088/1367-2630/abeb46


Cite as: https://publications.pik-potsdam.de/pubman/item/item_26716
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.