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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.
References
- 1.
DeVries, P. M. H., Viégas, F., Wattenberg, M. & Meade, B. J. Deep learning of aftershock patterns following large earthquakes. Nature 560, 632–634 (2018).
- 2.
Meade, B. J., DeVries, P. M. R., Faller, J., Viegas, F. & Wattenberg, M. What is better than Coulomb failure stress? A ranking of scalar static stress triggering mechanisms from 105 mainshock-aftershock pairs. Geophys. Res. Lett. 44, 11,409–11,416 (2017).
- 3.
Reasenberg, P. A. & Jones, L. M. Earthquake hazard after a mainshock in California. Science 243, 1173–1176 (1989).
- 4.
Reasenberg, P. A. & Jones, L. M. Earthquake aftershocks: update. Science 265, 1251–1252 (1994).
- 5.
Gerstenberger, M. C., Wiemer, S., Jones, L. M. & Reasenberg, P. A. Real-time forecast of tomorrow’s earthquakes in California. Nature 435, 328–331 (2005).
- 6.
Felzer, K. R. & Brodsky, E. E. Decay of aftershock density with distance indicates triggering by dynamic stress. Nature 441, 735–738 (2006).
- 7.
Richards-Dinger, K., Stein, R. S. & Toda, S. Decay of aftershock density with distance does not indicate triggering by dynamic stress. Nature 467, 583–586 (2010).
- 8.
Mignan, A. Utsu aftershock productivity law explained from geometric operations on the permanent static stress field of mainshocks. Nonlinear Process. Geophys. 25, 241–250 (2018).
- 9.
Steacy, S., Gerstenberger, M., Williams, C., Rhoades, D. & Christophersen, A. A new hybrid Coulomb/statistical model for forecasting aftershock rates. Geophys. J. Int. 196, 918–923 (2014).
- 10.
Cattania, C., Hainzl, S., Wang, L., Roth, F. & Enescu, B. Propagation of Coulomb stress uncertainties in physics-based aftershock models. J. Geophys. Res. Solid Earth 119, 7846–7864 (2014).
- 11.
Cattania, C. et al. The forecasting skill of physics-based seismicity models during the 2010–2012 Canterbury, New Zealand, earthquake sequence. Seismol. Res. Lett. 89, 1238–1250 (2018).
- 12.
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
- 13.
Jordan, M. I. & Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 349, 255–260 (2015).
- 14.
Kong, Q. et al. Machine learning in seismology: turning data into insights. Seismol. Res. Lett. 90, 3–14 (2019).
- 15.
Beroza, G. C. Aftershock forecasts turn to AI. Nature 560, 556–557 (2018).
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A.M. and M.B. contributed equally to the design and analysis of this study.
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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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