One neuron versus deep learning in aftershock prediction

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Fig. 1: Prediction of aftershock spatial patterns based on stress features.
Fig. 2: Prediction of aftershock spatial patterns using the distance, r, and the slip, d.

Data availability

The data that support the findings of this study are available from the SRCMOD fault rupture catalogue (http://equake-rc.info/SRCMOD), the International Seismological Centre earthquake catalogue (http://www.isc.ac.uk/iscgem) and from DeVries et al.1 at https://github.com/phoebemrdevries/Learning-aftershock-location-patterns.

Code availability

Original codes by DeVries et al.1 are available at https://github.com/phoebemrdevries/Learning-aftershock-location-patterns. An R code including the distance–slip feature definition and logistic regression training/testing is available from the corresponding authors on request.

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Author information

A.M. and M.B. contributed equally to the design and analysis of this study.

Correspondence to Arnaud Mignan or Marco Broccardo.

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The authors declare no competing interests.

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Mignan, A., Broccardo, M. One neuron versus deep learning in aftershock prediction. Nature 574, E1–E3 (2019) doi:10.1038/s41586-019-1582-8

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