Application of machine learning techniques to predict rupture propagation and arrest in 2-D dynamic earthquake simulations
SUMMARY Simulating dynamic earthquake rupture propagation is challenging due to uncertainties in the underlying physics of fault slip, stress conditions and the fault frictional properties. A trial and error approach is often used to determine the unknown parameters describing rupture, though runnin...
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| Vydáno v: | Geophysical journal international Ročník 224; číslo 3; s. 1918 - 1929 |
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Oxford University Press
01.03.2021
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| ISSN: | 0956-540X, 1365-246X |
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| Abstract | SUMMARY
Simulating dynamic earthquake rupture propagation is challenging due to uncertainties in the underlying physics of fault slip, stress conditions and the fault frictional properties. A trial and error approach is often used to determine the unknown parameters describing rupture, though running many simulations usually requires human review to determine how to adjust parameter values and is thus inefficient. We leverage machine learning approaches to reduce computational cost and improve the ability to determine reasonable stress and friction parameters. Two models have been developed using neural networks and the random forest to predict if a rupture can break 2-D geometrically complex fault. We train the models using a database of 1600 dynamic rupture simulations computed numerically. Fault geometry, stress conditions and friction parameters vary in each simulation. Both models distinguish the underlying complex data patterns that reflect the physics of the rupture. For example, our models identify in a quantitative fashion, how higher normal and shear stress components and low static and dynamic friction can be tied to the probability of rupture propagation. Both models are efficient in predicting rupture propagation such that 400 unseen examples are predicted in a fraction of a second, leading to potential applications of dynamic rupture that have previously not been possible due to the computational demands of physics-based rupture simulations. |
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| AbstractList | SUMMARY
Simulating dynamic earthquake rupture propagation is challenging due to uncertainties in the underlying physics of fault slip, stress conditions and the fault frictional properties. A trial and error approach is often used to determine the unknown parameters describing rupture, though running many simulations usually requires human review to determine how to adjust parameter values and is thus inefficient. We leverage machine learning approaches to reduce computational cost and improve the ability to determine reasonable stress and friction parameters. Two models have been developed using neural networks and the random forest to predict if a rupture can break 2-D geometrically complex fault. We train the models using a database of 1600 dynamic rupture simulations computed numerically. Fault geometry, stress conditions and friction parameters vary in each simulation. Both models distinguish the underlying complex data patterns that reflect the physics of the rupture. For example, our models identify in a quantitative fashion, how higher normal and shear stress components and low static and dynamic friction can be tied to the probability of rupture propagation. Both models are efficient in predicting rupture propagation such that 400 unseen examples are predicted in a fraction of a second, leading to potential applications of dynamic rupture that have previously not been possible due to the computational demands of physics-based rupture simulations. Simulating dynamic earthquake rupture propagation is challenging due to uncertainties in the underlying physics of fault slip, stress conditions and the fault frictional properties. A trial and error approach is often used to determine the unknown parameters describing rupture, though running many simulations usually requires human review to determine how to adjust parameter values and is thus inefficient. We leverage machine learning approaches to reduce computational cost and improve the ability to determine reasonable stress and friction parameters. Two models have been developed using neural networks and the random forest to predict if a rupture can break 2-D geometrically complex fault. We train the models using a database of 1600 dynamic rupture simulations computed numerically. Fault geometry, stress conditions and friction parameters vary in each simulation. Both models distinguish the underlying complex data patterns that reflect the physics of the rupture. For example, our models identify in a quantitative fashion, how higher normal and shear stress components and low static and dynamic friction can be tied to the probability of rupture propagation. Both models are efficient in predicting rupture propagation such that 400 unseen examples are predicted in a fraction of a second, leading to potential applications of dynamic rupture that have previously not been possible due to the computational demands of physics-based rupture simulations. |
| Author | Ahamed, Sabber Daub, Eric G |
| Author_xml | – sequence: 1 givenname: Sabber surname: Ahamed fullname: Ahamed, Sabber email: sabbers@gmail.com – sequence: 2 givenname: Eric G surname: Daub fullname: Daub, Eric G |
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| Cites_doi | 10.1029/2018JB016214 10.1029/2006JB004443 10.1007/s10462-009-9124-7 10.1111/j.1365-246X.2005.02769.x 10.1029/2001GL012869 10.1038/323533a0 10.1146/annurev.ea.06.050178.002201 10.1029/1999GL900377 10.1038/35016072 10.1016/S0012-821X(03)00424-2 10.1029/2008JB006174 10.1146/annurev.earth.26.1.643 10.1002/2017GL074677 10.1190/tle36030208.1 10.1016/j.tecto.2010.06.015 10.1002/2015GL063802 10.1002/2014JB011595 10.2307/2291432 10.1029/2008GL036832 10.1029/2000JB900241 10.1785/BSSA0870010061 10.1109/MCAS.2006.1688199 10.1029/98JB01576 10.1111/j.1365-246X.2005.02579.x 10.1023/A:1010933404324 10.1613/jair.614 10.1111/j.1365-246X.1977.tb01339.x 10.1785/0120070076 10.1371/journal.pone.0146101 10.1002/2015JB012512 10.1029/2002JB002189 10.1029/2007JB005027 10.1029/JB081i020p03575 10.1007/BF00058655 10.1126/sciadv.1700578 10.1023/A:1007607513941 10.1029/2001JB000205 10.1029/2008JB006271 10.1002/2016GL071700 10.1007/BF00116251 10.1037/h0042519 10.1029/95JB01460 10.1785/0120170293 10.1007/s00024-010-0161-6 |
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| Keywords | Earthquake ground motions Numerical modelling Earthquake dynamics Machine learning |
| Language | English |
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Simulating dynamic earthquake rupture propagation is challenging due to uncertainties in the underlying physics of fault slip, stress conditions and... Simulating dynamic earthquake rupture propagation is challenging due to uncertainties in the underlying physics of fault slip, stress conditions and the fault... |
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