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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.
DeVries, P. M. H., Viégas, F., Wattenberg, M. & Meade, B. J. Deep learning of aftershock patterns following large earthquakes. Nature 560, 632–634 (2018).
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).
Reasenberg, P. A. & Jones, L. M. Earthquake hazard after a mainshock in California. Science 243, 1173–1176 (1989).
Reasenberg, P. A. & Jones, L. M. Earthquake aftershocks: update. Science 265, 1251–1252 (1994).
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).
Felzer, K. R. & Brodsky, E. E. Decay of aftershock density with distance indicates triggering by dynamic stress. Nature 441, 735–738 (2006).
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).
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).
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).
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).
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).
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Jordan, M. I. & Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 349, 255–260 (2015).
Kong, Q. et al. Machine learning in seismology: turning data into insights. Seismol. Res. Lett. 90, 3–14 (2019).
Beroza, G. C. Aftershock forecasts turn to AI. Nature 560, 556–557 (2018).
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
Spatial Prediction of Aftershocks Triggered by a Major Earthquake: A Binary Machine Learning Perspective
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