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One neuron versus deep learning in aftershock prediction

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Matters Arising to this article was published on 02 October 2019

The Original Article was published on 29 August 2018

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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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Authors and Affiliations

  1. Swiss Federal Institute of Technology, Institute of Geophysics, Zurich, Switzerland

    Arnaud Mignan

  2. Swiss Seismological Service, Zurich, Switzerland

    Arnaud Mignan & Marco Broccardo

  3. Institute of Risk Analysis, Prediction and Management, Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen, China

    Arnaud Mignan

  4. Swiss Federal Institute of Technology, Institute of Structural Engineering, Zurich, Switzerland

    Marco Broccardo

Authors
  1. Arnaud Mignan
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  2. Marco Broccardo
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Contributions

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

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

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