Deep learning of aftershock patterns following large earthquakes
Aftershocks are a response to changes in stress generated by large earthquakes and represent the most common observations of the triggering of earthquakes. The maximum magnitude of aftershocks and their temporal decay are well described by empirical laws (such as Bath’s law 1 and Omori’s law 2 ), bu...
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Published in | Nature (London) Vol. 560; no. 7720; pp. 632 - 634 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
01.08.2018
Nature Publishing Group |
Subjects | |
Online Access | Get full text |
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Abstract | Aftershocks are a response to changes in stress generated by large earthquakes and represent the most common observations of the triggering of earthquakes. The maximum magnitude of aftershocks and their temporal decay are well described by empirical laws (such as Bath’s law
1
and Omori’s law
2
), but explaining and forecasting the spatial distribution of aftershocks is more difficult. Coulomb failure stress change
3
is perhaps the most widely used criterion to explain the spatial distributions of aftershocks
4
–
8
, but its applicability has been disputed
9
–
11
. Here we use a deep-learning approach to identify a static-stress-based criterion that forecasts aftershock locations without prior assumptions about fault orientation. We show that a neural network trained on more than 131,000 mainshock–aftershock pairs can predict the locations of aftershocks in an independent test dataset of more than 30,000 mainshock–aftershock pairs more accurately (area under curve of 0.849) than can classic Coulomb failure stress change (area under curve of 0.583). We find that the learned aftershock pattern is physically interpretable: the maximum change in shear stress, the von Mises yield criterion (a scaled version of the second invariant of the deviatoric stress-change tensor) and the sum of the absolute values of the independent components of the stress-change tensor each explain more than 98 per cent of the variance in the neural-network prediction. This machine-learning-driven insight provides improved forecasts of aftershock locations and identifies physical quantities that may control earthquake triggering during the most active part of the seismic cycle.
Neural networks trained on data from about 130,000 aftershocks from around 100 large earthquakes improve predictions of the spatial distribution of aftershocks and suggest physical quantities that may control earthquake triggering. |
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AbstractList | Aftershocks are a response to changes in stress generated by large earthquakes and represent the most common observations of the triggering of earthquakes. The maximum magnitude of aftershocks and their temporal decay are well described by empirical laws (such as Bath's law.sup.1 and Omori's law.sup.2), but explaining and forecasting the spatial distribution of aftershocks is more difficult. Coulomb failure stress change.sup.3 is perhaps the most widely used criterion to explain the spatial distributions of aftershocks.sup.4-8, but its applicability has been disputed.sup.9-11. Here we use a deep-learning approach to identify a static-stress-based criterion that forecasts aftershock locations without prior assumptions about fault orientation. We show that a neural network trained on more than 131,000 mainshock-aftershock pairs can predict the locations of aftershocks in an independent test dataset of more than 30,000 mainshock-aftershock pairs more accurately (area under curve of 0.849) than can classic Coulomb failure stress change (area under curve of 0.583). We find that the learned aftershock pattern is physically interpretable: the maximum change in shear stress, the von Mises yield criterion (a scaled version of the second invariant of the deviatoric stress-change tensor) and the sum of the absolute values of the independent components of the stress-change tensor each explain more than 98 per cent of the variance in the neural-network prediction. This machine-learning-driven insight provides improved forecasts of aftershock locations and identifies physical quantities that may control earthquake triggering during the most active part of the seismic cycle. Aftershocks are a response to changes in stress generated by large earthquakes and represent the most common observations of the triggering of earthquakes. The maximum magnitude of aftershocks and their temporal decay are well described by empirical laws (such as Bath’s law 1 and Omori’s law 2 ), but explaining and forecasting the spatial distribution of aftershocks is more difficult. Coulomb failure stress change 3 is perhaps the most widely used criterion to explain the spatial distributions of aftershocks 4 – 8 , but its applicability has been disputed 9 – 11 . Here we use a deep-learning approach to identify a static-stress-based criterion that forecasts aftershock locations without prior assumptions about fault orientation. We show that a neural network trained on more than 131,000 mainshock–aftershock pairs can predict the locations of aftershocks in an independent test dataset of more than 30,000 mainshock–aftershock pairs more accurately (area under curve of 0.849) than can classic Coulomb failure stress change (area under curve of 0.583). We find that the learned aftershock pattern is physically interpretable: the maximum change in shear stress, the von Mises yield criterion (a scaled version of the second invariant of the deviatoric stress-change tensor) and the sum of the absolute values of the independent components of the stress-change tensor each explain more than 98 per cent of the variance in the neural-network prediction. This machine-learning-driven insight provides improved forecasts of aftershock locations and identifies physical quantities that may control earthquake triggering during the most active part of the seismic cycle. Neural networks trained on data from about 130,000 aftershocks from around 100 large earthquakes improve predictions of the spatial distribution of aftershocks and suggest physical quantities that may control earthquake triggering. Aftershocks are a response to changes in stress generated by large earthquakes and represent the most common observations of the triggering of earthquakes. The maximum magnitude of aftershocks and their temporal decay are well described by empirical laws (such as Bath's law1 and Omori's law2), but explaining and forecasting the spatial distribution of aftershocks is more difficult. Coulomb failure stress change3 is perhaps the most widely used criterion to explain the spatial distributions of aftershocks4-8, but its applicability has been disputed9-11. Here we use a deep-learning approach to identify a static-stress-based criterion that forecasts aftershock locations without prior assumptions about fault orientation. We show that a neural network trained on more than 131,000 mainshock-aftershock pairs can predict the locations of aftershocks in an independent test dataset of more than 30,000 mainshock-aftershock pairs more accurately (area under curve of 0.849) than can classic Coulomb failure stress change (area under curve of 0.583). We find that the learned aftershock pattern is physically interpretable: the maximum change in shear stress, the von Mises yield criterion (a scaled version of the second invariant of the deviatoric stress-change tensor) and the sum of the absolute values of the independent components of the stress-change tensor each explain more than 98 per cent of the variance in the neural-network prediction. This machine-learningdriven insight provides improved forecasts of aftershock locations and identifies physical quantities that may control earthquake triggering during the most active part of the seismic cycle. Aftershocks are a response to changes in stress generated by large earthquakes and represent the most common observations of the triggering of earthquakes. The maximum magnitude of aftershocks and their temporal decay are well described by empirical laws (such as Bath's law and Omori's law ), but explaining and forecasting the spatial distribution of aftershocks is more difficult. Coulomb failure stress change is perhaps the most widely used criterion to explain the spatial distributions of aftershocks , but its applicability has been disputed . Here we use a deep-learning approach to identify a static-stress-based criterion that forecasts aftershock locations without prior assumptions about fault orientation. We show that a neural network trained on more than 131,000 mainshock-aftershock pairs can predict the locations of aftershocks in an independent test dataset of more than 30,000 mainshock-aftershock pairs more accurately (area under curve of 0.849) than can classic Coulomb failure stress change (area under curve of 0.583). We find that the learned aftershock pattern is physically interpretable: the maximum change in shear stress, the von Mises yield criterion (a scaled version of the second invariant of the deviatoric stress-change tensor) and the sum of the absolute values of the independent components of the stress-change tensor each explain more than 98 per cent of the variance in the neural-network prediction. This machine-learning-driven insight provides improved forecasts of aftershock locations and identifies physical quantities that may control earthquake triggering during the most active part of the seismic cycle. Aftershocks are a response to changes in stress generated by large earthquakes and represent the most common observations of the triggering of earthquakes. The maximum magnitude of aftershocks and their temporal decay are well described by empirical laws (such as Bath's law.sup.1 and Omori's law.sup.2), but explaining and forecasting the spatial distribution of aftershocks is more difficult. Coulomb failure stress change.sup.3 is perhaps the most widely used criterion to explain the spatial distributions of aftershocks.sup.4-8, but its applicability has been disputed.sup.9-11. Here we use a deep-learning approach to identify a static-stress-based criterion that forecasts aftershock locations without prior assumptions about fault orientation. We show that a neural network trained on more than 131,000 mainshock-aftershock pairs can predict the locations of aftershocks in an independent test dataset of more than 30,000 mainshock-aftershock pairs more accurately (area under curve of 0.849) than can classic Coulomb failure stress change (area under curve of 0.583). We find that the learned aftershock pattern is physically interpretable: the maximum change in shear stress, the von Mises yield criterion (a scaled version of the second invariant of the deviatoric stress-change tensor) and the sum of the absolute values of the independent components of the stress-change tensor each explain more than 98 per cent of the variance in the neural-network prediction. This machine-learning-driven insight provides improved forecasts of aftershock locations and identifies physical quantities that may control earthquake triggering during the most active part of the seismic cycle.Neural networks trained on data from about 130,000 aftershocks from around 100 large earthquakes improve predictions of the spatial distribution of aftershocks and suggest physical quantities that may control earthquake triggering. |
Audience | Academic |
Author | Wattenberg, Martin DeVries, Phoebe M. R. Viégas, Fernanda Meade, Brendan J. |
Author_xml | – sequence: 1 givenname: Phoebe M. R. surname: DeVries fullname: DeVries, Phoebe M. R. email: phoebe.devries@uconn.edu organization: Department of Earth and Planetary Sciences, Harvard University, Center for Integrative Geosciences and Department of Physics, University of Connecticut – sequence: 2 givenname: Fernanda surname: Viégas fullname: Viégas, Fernanda organization: Google – sequence: 3 givenname: Martin surname: Wattenberg fullname: Wattenberg, Martin organization: Google – sequence: 4 givenname: Brendan J. surname: Meade fullname: Meade, Brendan J. organization: Department of Earth and Planetary Sciences, Harvard University |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30158606$$D View this record in MEDLINE/PubMed |
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ContentType | Journal Article |
Copyright | Springer Nature Limited 2018 COPYRIGHT 2018 Nature Publishing Group Copyright Nature Publishing Group Aug 30, 2018 |
Copyright_xml | – notice: Springer Nature Limited 2018 – notice: COPYRIGHT 2018 Nature Publishing Group – notice: Copyright Nature Publishing Group Aug 30, 2018 |
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DOI | 10.1038/s41586-018-0438-y |
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Snippet | Aftershocks are a response to changes in stress generated by large earthquakes and represent the most common observations of the triggering of earthquakes. The... |
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SubjectTerms | 704/2151/508 704/2151/562 704/4111 Aftershocks Artificial neural networks Datasets Deep learning Earthquake prediction Earthquakes Humanities and Social Sciences Laws, regulations and rules Letter Machine learning multidisciplinary Neural networks Observatories Plate tectonics Science Science (multidisciplinary) Seismic activity Seismic response Seismology Shear stress Spatial distribution Yield criteria |
Title | Deep learning of aftershock patterns following large earthquakes |
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