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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.
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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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). https://doi.org/10.1038/s41586-019-1582-8
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