Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault

Ren, Christopher Xiang; Dorostkar, Omid; Rouet‐Leduc, Bertrand Philippe; Hulbert, Claudia L.; Strebel, Dominik; Guyer, Robert A.; Johnson, Paul Allan; Carmeliet, Jan

doi:10.1029/2019GL082706

Title: Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault

Journal Article · 24 June 2019 · Geophysical Research Letters

DOI: https://doi.org/10.1029/2019GL082706 · OSTI ID:1544741

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Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Univ. of Oxford (United Kingdom); Federal Inst. of Technology, Zurich (Switzerland)
Swiss Federal Lab. for Materials Science and Technology (Empa), Dübendorf (Switzerland)
Federal Inst. of Technology, Zurich (Switzerland)

Seismogenic plate boundaries are posited to behave in a similar manner to a densely packed granular medium, where fault and block systems rapidly rearrange the distribution of forces within themselves, as particles do in slowly sheared granular systems. Machine learning is used to show that statistical features of velocity signals from individual particles in a simulated sheared granular fault contain information regarding the instantaneous global state of intermittent frictional stick-slip dynamics. We demonstrate that combining features built from the signals of more particles can improve the accuracy of the global model and discuss the physical basis behind the decrease in error. We show that the statistical features such as median and higher moments of the signals that represent the particle displacement in the direction of shearing are among the best predictive features. We demonstrate here novel insights into the applications of machine learning in studying frictional processes occurring in geophysical systems.

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Research Organization:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Chemical Sciences, Geosciences & Biosciences Division

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1544741

Report Number(s):: LA-UR--19-22300

Journal Information:: Geophysical Research Letters, Journal Name: Geophysical Research Letters Journal Issue: 13 Vol. 46; ISSN 0094-8276

Publisher:: American Geophysical UnionCopyright Statement

Country of Publication:: United States

Language:: English

References (44)

On the micromechanics of slip events in sheared, fluid-saturated fault gouge: Stick-Slip Dynamics in Saturated Gouge Dorostkar, Omid; Guyer, Robert A.; Johnson, Paul A. Geophysical Research Letters, Vol. 44, Issue 12 https://doi.org/10.1002/2017GL073768	journal	June 2017
Machine Learning Predicts Laboratory Earthquakes: MACHINE LEARNING PREDICTS LAB QUAKES Rouet-Leduc, Bertrand; Hulbert, Claudia; Lubbers, Nicholas Geophysical Research Letters, Vol. 44, Issue 18 https://doi.org/10.1002/2017GL074677	journal	September 2017
Estimating Fault Friction From Seismic Signals in the Laboratory Rouet‐Leduc, Bertrand; Hulbert, Claudia; Bolton, David C. Geophysical Research Letters, Vol. 45, Issue 3 https://doi.org/10.1002/2017GL076708	journal	February 2018
Cohesion-Induced Stabilization in Stick-Slip Dynamics of Weakly Wet, Sheared Granular Fault Gouge Dorostkar, Omid; Guyer, Robert A.; Johnson, Paul A. Journal of Geophysical Research: Solid Earth, Vol. 123, Issue 3 https://doi.org/10.1002/2017JB015171	journal	March 2018
Potential Energy as Metric for Understanding Stick–Slip Dynamics in Sheared Granular Fault Gouge: A Coupled CFD–DEM Study Dorostkar, Omid; Carmeliet, Jan Rock Mechanics and Rock Engineering, Vol. 51, Issue 10 https://doi.org/10.1007/s00603-018-1457-6	journal	April 2018
On measuring skewness and kurtosis Đorić, Dragan; Nikolić-Đorić, Emilija; Jevremović, Vesna Quality and Quantity, Vol. 43, Issue 3 https://doi.org/10.1007/s11135-007-9128-9	journal	September 2007
Empirical comparison of tree ensemble variable importance measures Auret, Lidia; Aldrich, Chris Chemometrics and Intelligent Laboratory Systems, Vol. 105, Issue 2 https://doi.org/10.1016/j.chemolab.2010.12.004	journal	February 2011
Evolution of b-value during the seismic cycle: Insights from laboratory experiments on simulated faults Rivière, J.; Lv, Z.; Johnson, P. A. Earth and Planetary Science Letters, Vol. 482 https://doi.org/10.1016/j.epsl.2017.11.036	journal	January 2018
Influence of rolling friction on single spout fluidized bed simulation Goniva, Christoph; Kloss, Christoph; Deen, Niels G. Particuology, Vol. 10, Issue 5 https://doi.org/10.1016/j.partic.2012.05.002	journal	October 2012
New Theory of the Earth Anderson, Don L. Cambridge University Press https://doi.org/10.1017/CBO9781139167291	book	January 2007
Earthquake Catalog‐Based Machine Learning Identification of Laboratory Fault States and the Effects of Magnitude of Completeness Lubbers, Nicholas; Bolton, David C.; Mohd‐Yusof, Jamaludin Geophysical Research Letters, Vol. 45, Issue 24 https://doi.org/10.1029/2018GL079712	journal	December 2018
Machine Learning Can Predict the Timing and Size of Analog Earthquakes Corbi, F.; Sandri, L.; Bedford, J. Geophysical Research Letters, Vol. 46, Issue 3 https://doi.org/10.1029/2018GL081251	journal	February 2019
PhaseLink: A Deep Learning Approach to Seismic Phase Association Ross, Zachary E.; Yue, Yisong; Meier, Men‐Andrin Journal of Geophysical Research: Solid Earth, Vol. 124, Issue 1 https://doi.org/10.1029/2018JB016674	journal	January 2019
Grain Friction Controls Characteristics of Seismic Cycle in Faults With Granular Gouge Dorostkar, Omid; Carmeliet, Jan Journal of Geophysical Research: Solid Earth, Vol. 124, Issue 7 https://doi.org/10.1029/2019JB017374	journal	July 2019
Earthquakes and friction laws Scholz, Christopher H. Nature, Vol. 391, Issue 6662 https://doi.org/10.1038/34097	journal	January 1998
A simple analytic theory for the statistics of avalanches in sheared granular materials Dahmen, Karin A.; Ben-Zion, Yehuda; Uhl, Jonathan T. Nature Physics, Vol. 7, Issue 7 https://doi.org/10.1038/nphys1957	journal	March 2011
Similarity of fast and slow earthquakes illuminated by machine learning Hulbert, Claudia; Rouet-Leduc, Bertrand; Johnson, Paul A. Nature Geoscience, Vol. 12, Issue 1 https://doi.org/10.1038/s41561-018-0272-8	journal	December 2018
Continuous chatter of the Cascadia subduction zone revealed by machine learning Rouet-Leduc, Bertrand; Hulbert, Claudia; Johnson, Paul A. Nature Geoscience, Vol. 12, Issue 1 https://doi.org/10.1038/s41561-018-0274-6	journal	December 2018
Locating induced earthquakes with a network of seismic stations in Oklahoma via a deep learning method Zhang, Xiong; Zhang, Jie; Yuan, Congcong Scientific Reports, Vol. 10, Issue 1 https://doi.org/10.1038/s41598-020-58908-5	journal	February 2020
Universal Quake Statistics: From Compressed Nanocrystals to Earthquakes Uhl, Jonathan T.; Pathak, Shivesh; Schorlemmer, Danijel Scientific Reports, Vol. 5, Issue 1 https://doi.org/10.1038/srep16493	journal	November 2015
Do Fluids Modify the Stick-Slip Behavior of Sheared Granular Media? Dorostkar, Omid; Johnson, Paul; Guyer, Robert Sixth Biot Conference on Poromechanics, Poromechanics VI https://doi.org/10.1061/9780784480779.019	conference	July 2017
Force cycles and force chains Tordesillas, Antoinette; Walker, David M.; Lin, Qun Physical Review E, Vol. 81, Issue 1 https://doi.org/10.1103/PhysRevE.81.011302	journal	January 2010
Intermittent Granular Dynamics at a Seismogenic Plate Boundary Meroz, Yasmine; Meade, Brendan J. Physical Review Letters, Vol. 119, Issue 13 https://doi.org/10.1103/PhysRevLett.119.138501	journal	September 2017
Sounds of Failure: Passive Acoustic Measurements of Excited Vibrational Modes Brzinski, Theodore A.; Daniels, Karen E. Physical Review Letters, Vol. 120, Issue 21 https://doi.org/10.1103/PhysRevLett.120.218003	journal	May 2018
Convolutional neural network for earthquake detection and location Perol, Thibaut; Gharbi, Michaël; Denolle, Marine Science Advances, Vol. 4, Issue 2 https://doi.org/10.1126/sciadv.1700578	journal	February 2018
Stick-Slip as a Mechanism for Earthquakes Brace, W. F.; Byerlee, J. D. Science, Vol. 153, Issue 3739 https://doi.org/10.1126/science.153.3739.990	journal	August 1966
California Earthquakes: Why Only Shallow Focus? Brace, W. F.; Byerlee, J. D. Science, Vol. 168, Issue 3939 https://doi.org/10.1126/science.168.3939.1573	journal	June 1970
Source Parameters for Stick-Slip and for Earthquakes Johnson, T.; Wu, F. T.; Scholz, C. H. Science, Vol. 179, Issue 4070 https://doi.org/10.1126/science.179.4070.278	journal	January 1973
Machine learning for data-driven discovery in solid Earth geoscience Bergen, Karianne J.; Johnson, Paul A.; de Hoop, Maarten V. Science, Vol. 363, Issue 6433 https://doi.org/10.1126/science.aau0323	journal	March 2019
On dense granular flows Milieux Divises, Groupement De Recherche Milieux Divis¶es The European Physical Journal E, Vol. 14, Issue 4, p. 341-365 https://doi.org/10.1140/epje/i2003-10153-0	journal	August 2004
XGBoost: A Scalable Tree Boosting System Chen, Tianqi; Guestrin, Carlos Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16 https://doi.org/10.1145/2939672.2939785	conference	January 2016
Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors) Friedman, Jerome; Tibshirani, Robert; Hastie, Trevor The Annals of Statistics, Vol. 28, Issue 2 https://doi.org/10.1214/aos/1016120463	journal	April 2000
Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors) Friedman, Jerome; Hastie, Trevor; Tibshirani, Robert The Annals of Statistics, Vol. 28, Issue 2 https://doi.org/10.1214/aos/1016218223	journal	April 2000
Models, algorithms and validation for opensource DEM and CFD-DEM Kloss, Christoph; Goniva, Christoph; Hager, Alice Progress in Computational Fluid Dynamics, An International Journal, Vol. 12, Issue 2/3 https://doi.org/10.1504/PCFD.2012.047457	journal	January 2012
Generalized Seismic Phase Detection with Deep Learning Ross, Zachary E.; Meier, Men‐Andrin; Hauksson, Egill Bulletin of the Seismological Society of America, Vol. 108, Issue 5A https://doi.org/10.1785/0120180080	journal	August 2018
Machine Learning in Seismology: Turning Data into Insights Kong, Qingkai; Trugman, Daniel T.; Ross, Zachary E. Seismological Research Letters, Vol. 90, Issue 1 https://doi.org/10.1785/0220180259	journal	November 2018
Pairwise Association of Seismic Arrivals with Convolutional Neural Networks McBrearty, Ian W.; Delorey, Andrew A.; Johnson, Paul A. Seismological Research Letters, Vol. 90, Issue 2A https://doi.org/10.1785/0220180326	journal	January 2019
Frequency of earthquakes in California* Gutenberg, B.; Richter, C. F. Bulletin of the Seismological Society of America, Vol. 34, Issue 4 https://doi.org/10.1785/BSSA0340040185	journal	October 1944
pdp: An R Package for Constructing Partial Dependence Plots Greenwell, Brandon,M. The R Journal, Vol. 9, Issue 1 https://doi.org/10.32614/RJ-2017-016	journal	January 2017
Sounds of Failure: Passive Acoustic Measurements of Excited Vibrational Modes Brzinski, Theodore A.; Daniels, Karen E. arXiv https://doi.org/10.48550/arxiv.1610.09705	text	January 2016
Estimating Fault Friction from Seismic Signals in the Laboratory Rouet-Leduc, Bertrand; Hulbert, Claudia; Bolton, David C. arXiv https://doi.org/10.48550/arxiv.1710.04172	text	January 2017
Generalized Seismic Phase Detection with Deep Learning Ross, Zachary E.; Meier, Men-Andrin; Hauksson, Egill arXiv https://doi.org/10.48550/arxiv.1805.01075	text	January 2018
PhaseLink: A Deep Learning Approach to Seismic Phase Association Ross, Zachary E.; Yue, Yisong; Meier, Men-Andrin arXiv https://doi.org/10.48550/arxiv.1809.02880	text	January 2018
On dense granular flows Midi, GdR arXiv https://doi.org/10.48550/arxiv.cond-mat/0312502	text	January 2003

Cited By (3)

Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano Ren, C. X.; Peltier, A.; Ferrazzini, V. Geophysical Research Letters, Vol. 47, Issue 3 https://doi.org/10.1029/2019gl085523	journal	February 2020
Grain Friction Controls Characteristics of Seismic Cycle in Faults With Granular Gouge Dorostkar, Omid; Carmeliet, Jan Journal of Geophysical Research: Solid Earth, Vol. 124, Issue 7 https://doi.org/10.1029/2019jb017374	journal	July 2019
Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano Ren, C. X.; Peltier, A.; Ferrazzini, V. arXiv https://doi.org/10.48550/arxiv.1909.12395	text	January 2019

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Title: Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault

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