Attention Network Forecasts Time-to-Failure in Laboratory Shear Experiments
Abstract
Abstract Rocks under stress deform by creep mechanisms that include formation and slip on small‐scale internal cracks. Intragranular cracks and slip along grain contacts release energy as elastic waves termed acoustic emissions (AE). AEs are thought to contain predictive information that can be used for fault failure forecasting. Here, we present a method using unsupervised classification and an attention network to forecast labquakes using AE waveform features. Our data were generated in a laboratory setting using a biaxial shearing device with granular fault gouge intended to mimic the conditions of tectonic faults. Here, we analyzed the temporal evolution of AEs generated throughout several hundred laboratory earthquake cycles. We used a Conscience Self‐Organizing Map (CSOM) to perform topologically ordered vector quantization based on waveform properties. The resulting map was used to interactively cluster AEs. We examined the clusters over time to identify those with predictive ability. Finally, we used a variety of LSTM and attention‐based networks to test the predictive power of the AE clusters. By tracking cumulative waveform features over the seismic cycle, the network is able to forecast the time‐to‐failure (TTF) of lab earthquakes. Our results show that analyzing the data to isolate predictive signals and using a moremore »
- Authors:
-
- Rice Univ., Houston, TX (United States)
- Univ. of Texas, Austin, TX (United States). Inst. for Geophysics
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of Nevada, Reno, NV (United States). Dept. of Physics
- Pennsylvania State Univ., University Park, PA (United States). Dept. of Geosciences; Sapienza Univ. di Roma, Rome (Italy). Dipartimento Scienze della Terra
- Publication Date:
- Research Org.:
- Rice Univ., Houston, TX (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Pennsylvania State Univ., University Park, PA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE National Nuclear Security Administration (NNSA); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Office
- OSTI Identifier:
- 1837011
- Alternate Identifier(s):
- OSTI ID: 1832498
- Grant/Contract Number:
- SC0020345; 89233218CNA000001; SC0020512; EE0008763; DE‐SC0020345; DE‐SC0020512; DE‐EE0008763
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Geophysical Research. Solid Earth
- Additional Journal Information:
- Journal Volume: 126; Journal Issue: 11; Journal ID: ISSN 2169-9313
- Publisher:
- American Geophysical Union
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES
Citation Formats
Jasperson, Hope, Bolton, David C., Johnson, Paul, Guyer, Robert, Marone, Chris, and de Hoop, Maarten V. Attention Network Forecasts Time-to-Failure in Laboratory Shear Experiments. United States: N. p., 2021.
Web. doi:10.1029/2021jb022195.
Jasperson, Hope, Bolton, David C., Johnson, Paul, Guyer, Robert, Marone, Chris, & de Hoop, Maarten V. Attention Network Forecasts Time-to-Failure in Laboratory Shear Experiments. United States. https://doi.org/10.1029/2021jb022195
Jasperson, Hope, Bolton, David C., Johnson, Paul, Guyer, Robert, Marone, Chris, and de Hoop, Maarten V. Tue .
"Attention Network Forecasts Time-to-Failure in Laboratory Shear Experiments". United States. https://doi.org/10.1029/2021jb022195. https://www.osti.gov/servlets/purl/1837011.
@article{osti_1837011,
title = {Attention Network Forecasts Time-to-Failure in Laboratory Shear Experiments},
author = {Jasperson, Hope and Bolton, David C. and Johnson, Paul and Guyer, Robert and Marone, Chris and de Hoop, Maarten V.},
abstractNote = {Abstract Rocks under stress deform by creep mechanisms that include formation and slip on small‐scale internal cracks. Intragranular cracks and slip along grain contacts release energy as elastic waves termed acoustic emissions (AE). AEs are thought to contain predictive information that can be used for fault failure forecasting. Here, we present a method using unsupervised classification and an attention network to forecast labquakes using AE waveform features. Our data were generated in a laboratory setting using a biaxial shearing device with granular fault gouge intended to mimic the conditions of tectonic faults. Here, we analyzed the temporal evolution of AEs generated throughout several hundred laboratory earthquake cycles. We used a Conscience Self‐Organizing Map (CSOM) to perform topologically ordered vector quantization based on waveform properties. The resulting map was used to interactively cluster AEs. We examined the clusters over time to identify those with predictive ability. Finally, we used a variety of LSTM and attention‐based networks to test the predictive power of the AE clusters. By tracking cumulative waveform features over the seismic cycle, the network is able to forecast the time‐to‐failure (TTF) of lab earthquakes. Our results show that analyzing the data to isolate predictive signals and using a more sophisticated network architecture are key to robustly forecasting labquakes. In the future, this method could be applied on tectonic faults to monitor earthquakes and augment early warning systems.},
doi = {10.1029/2021jb022195},
journal = {Journal of Geophysical Research. Solid Earth},
number = 11,
volume = 126,
place = {United States},
year = {Tue Oct 26 00:00:00 EDT 2021},
month = {Tue Oct 26 00:00:00 EDT 2021}
}
Works referenced in this record:
Brittle and ductile friction and the physics of tectonic tremor: BRITTLE AND DUCTILE FRICTION
journal, May 2011
- Daub, Eric G.; Shelly, David R.; Guyer, Robert A.
- Geophysical Research Letters, Vol. 38, Issue 10
What to Do Next: Modeling User Behaviors by Time-LSTM
conference, August 2017
- Zhu, Yu; Li, Hao; Liao, Yikang
- Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Physicochemical processes of frictional healing: Effects of water on stick-slip stress drop and friction of granular fault gouge: AFFECT OF WATER ON STICK-SLIP FRICTION
journal, May 2014
- Scuderi, Marco M.; Carpenter, Brett M.; Marone, Chris
- Journal of Geophysical Research: Solid Earth, Vol. 119, Issue 5
A Validity Index for Prototype-Based Clustering of Data Sets With Complex Cluster Structures
journal, August 2011
- Tasdemir, K.; Merenyi, E.
- IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 41, Issue 4
Robustness of LSTM neural networks for multi-step forecasting of chaotic time series
journal, October 2020
- Sangiorgio, Matteo; Dercole, Fabio
- Chaos, Solitons & Fractals, Vol. 139
Deep Learning Can Predict Laboratory Quakes From Active Source Seismic Data
journal, June 2021
- Shokouhi, Parisa; Girkar, Vrushali; Rivière, Jacques
- Geophysical Research Letters, Vol. 48, Issue 12
Influence of confining pressure on the mechanical and structural evolution of laboratory deformation bands: PRESSURE INFLUENCE ON DEFORMATION BANDS
journal, May 2002
- Mair, Karen; Elphick, Stephen; Main, Ian
- Geophysical Research Letters, Vol. 29, Issue 10
Similarity of fast and slow earthquakes illuminated by machine learning
journal, December 2018
- Hulbert, Claudia; Rouet-Leduc, Bertrand; Johnson, Paul A.
- Nature Geoscience, Vol. 12, Issue 1
Continuous chatter of the Cascadia subduction zone revealed by machine learning
journal, December 2018
- Rouet-Leduc, Bertrand; Hulbert, Claudia; Johnson, Paul A.
- Nature Geoscience, Vol. 12, Issue 1
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
journal, February 2019
- Raissi, M.; Perdikaris, P.; Karniadakis, G. E.
- Journal of Computational Physics, Vol. 378
Self-organized formation of topologically correct feature maps
journal, January 1982
- Kohonen, Teuvo
- Biological Cybernetics, Vol. 43, Issue 1
Learned vs. Hand-Crafted Features for Deep Learning Based Aperiodic Laboratory Earthquake Time-Prediction
conference, October 2020
- Zaidi, Talha; Samy, Asmaa; Kocaturk, Mehmet
- 2020 28th Signal Processing and Communications Applications Conference (SIU)
Laboratory observations of slow earthquakes and the spectrum of tectonic fault slip modes
journal, March 2016
- Leeman, J. R.; Saffer, D. M.; Scuderi, M. M.
- Nature Communications, Vol. 7, Issue 1
Applying the self-organization feature map (SOM) algorithm to AE-based tool wear monitoring in micro-cutting
journal, January 2013
- Yen, Chia-Liang; Lu, Ming-Chyuan; Chen, Jau-Liang
- Mechanical Systems and Signal Processing, Vol. 34, Issue 1-2
Frequency of earthquakes in California*
journal, October 1944
- Gutenberg, B.; Richter, C. F.
- Bulletin of the Seismological Society of America, Vol. 34, Issue 4
Characterizing Acoustic Signals and Searching for Precursors during the Laboratory Seismic Cycle Using Unsupervised Machine Learning
journal, March 2019
- Bolton, David C.; Shokouhi, Parisa; Rouet‐Leduc, Bertrand
- Seismological Research Letters, Vol. 90, Issue 3
Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks
conference, July 2019
- Gigante, Scott; van Dijk, David; Moon, Kevin R.
- 2019 13th International conference on Sampling Theory and Applications (SampTA)
Modeling of Stick-Slip Behavior in Sheared Granular Fault Gouge Using the Combined Finite-Discrete Element Method
journal, July 2018
- Gao, Ke; Euser, Bryan J.; Rougier, Esteban
- Journal of Geophysical Research: Solid Earth, Vol. 123, Issue 7
Machine Learning Predicts Laboratory Earthquakes: MACHINE LEARNING PREDICTS LAB QUAKES
journal, September 2017
- Rouet-Leduc, Bertrand; Hulbert, Claudia; Lubbers, Nicholas
- Geophysical Research Letters, Vol. 44, Issue 18
Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission
journal, April 2020
- Louis, Steph-Yves M.; Nasiri, Alireza; Bao, Jingjing
- Applied Sciences, Vol. 10, Issue 8
A Cluster Separation Measure
journal, April 1979
- Davies, David L.; Bouldin, Donald W.
- IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-1, Issue 2
Cluster analysis of acoustic emission activity within wood material: Towards a real-time monitoring of crack tip propagation
journal, July 2017
- Diakhate, Malick; Bastidas-Arteaga, Emilio; Moutou Pitti, Rostand
- Engineering Fracture Mechanics, Vol. 180
Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning
journal, August 2020
- Seydoux, Léonard; Balestriero, Randall; Poli, Piero
- Nature Communications, Vol. 11, Issue 1
Long Short-Term Memory
journal, November 1997
- Hochreiter, Sepp; Schmidhuber, Jürgen
- Neural Computation, Vol. 9, Issue 8
Some new indexes of cluster validity
journal, June 1998
- Bezdek, J. C.; Pal, N. R.
- IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), Vol. 28, Issue 3
Machine Learning Can Predict the Timing and Size of Analog Earthquakes
journal, February 2019
- Corbi, F.; Sandri, L.; Bedford, J.
- Geophysical Research Letters, Vol. 46, Issue 3
Seismicity-based earthquake forecasting techniques: Ten years of progress
journal, February 2012
- Tiampo, Kristy F.; Shcherbakov, Robert
- Tectonophysics, Vol. 522-523
Laboratory earthquake forecasting: A machine learning competition
journal, January 2021
- Johnson, Paul A.; Rouet-Leduc, Bertrand; Pyrak-Nolte, Laura J.
- Proceedings of the National Academy of Sciences, Vol. 118, Issue 5
Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault
journal, July 2019
- Ren, C. X.; Dorostkar, O.; Rouet‐Leduc, B.
- Geophysical Research Letters, Vol. 46, Issue 13
Microfracturing and the inelastic deformation of rock in compression
journal, February 1968
- Scholz, C. H.
- Journal of Geophysical Research, Vol. 73, Issue 4
Health monitoring of FRP using acoustic emission and artificial neural networks
journal, February 2008
- de Oliveira, R.; Marques, A. T.
- Computers & Structures, Vol. 86, Issue 3-5
Estimating Fault Friction From Seismic Signals in the Laboratory
journal, February 2018
- Rouet‐Leduc, Bertrand; Hulbert, Claudia; Bolton, David C.
- Geophysical Research Letters, Vol. 45, Issue 3
Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking
journal, August 2020
- Mousavi, S. Mostafa; Ellsworth, William L.; Zhu, Weiqiang
- Nature Communications, Vol. 11, Issue 1
Evolution of b-value during the seismic cycle: Insights from laboratory experiments on simulated faults
journal, January 2018
- Rivière, J.; Lv, Z.; Johnson, P. A.
- Earth and Planetary Science Letters, Vol. 482
Estimating the number of clusters in a data set via the gap statistic
journal, May 2001
- Tibshirani, Robert; Walther, Guenther; Hastie, Trevor
- Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 63, Issue 2, p. 411-423
Nonuniformly Sampled Data Processing Using LSTM Networks
journal, May 2019
- Sahin, Safa Onur; Kozat, Suleyman Serdar
- IEEE Transactions on Neural Networks and Learning Systems, Vol. 30, Issue 5
Ensemble Methods in Machine Learning
book, January 2000
- Dietterich, Thomas G.
- Multiple Classifier Systems
Clustering of acoustic emission signals collected during tensile tests on unidirectional glass/polyester composite using supervised and unsupervised classifiers
journal, June 2004
- Godin, N.; Huguet, S.; Gaertner, R.
- NDT & E International, Vol. 37, Issue 4
The importance of studying small earthquakes
journal, May 2019
- Brodsky, Emily E.
- Science, Vol. 364, Issue 6442
Machine Learning Modelling and Feature Engineering in Seismology Experiment
journal, July 2020
- Brykov, Michail Nikolaevich; Petryshynets, Ivan; Pruncu, Catalin Iulian
- Sensors, Vol. 20, Issue 15
Identification of corrosion mechanisms by univariate and multivariate statistical analysis during long term acoustic emission monitoring on a pre-stressed concrete beam
journal, August 2013
- Calabrese, L.; Campanella, G.; Proverbio, E.
- Corrosion Science, Vol. 73
Use of acoustic emission to identify damage modes in glass fibre reinforced polyester
journal, August 2002
- Huguet, S.; Godin, N.; Gaertner, R.
- Composites Science and Technology, Vol. 62, Issue 10-11
Acoustic Energy Release During the Laboratory Seismic Cycle: Insights on Laboratory Earthquake Precursors and Prediction
journal, August 2020
- Bolton, David C.; Shreedharan, Srisharan; Rivière, Jacques
- Journal of Geophysical Research: Solid Earth, Vol. 125, Issue 8
Long Short-Term Memory Network for Remaining Useful Life estimation
conference, June 2017
- Zheng, Shuai; Ristovski, Kosta; Farahat, Ahmed
- 2017 IEEE International Conference on Prognostics and Health Management (ICPHM)
Prognosis of Bearing Acoustic Emission Signals Using Supervised Machine Learning
journal, July 2018
- Elforjani, Mohamed; Shanbr, Suliman
- IEEE Transactions on Industrial Electronics, Vol. 65, Issue 7
Modeling dynamic triggering of tectonic tremor using a brittle‐ductile friction model
journal, October 2013
- Trugman, Daniel T.; Daub, Eric G.; Guyer, Robert A.
- Geophysical Research Letters, Vol. 40, Issue 19
Correlation of acoustic emission with optically observed damage in a glass/epoxy woven laminate under tensile loading
journal, May 2015
- Li, Li; Lomov, Stepan V.; Yan, Xiong
- Composite Structures, Vol. 123
Validity index for crisp and fuzzy clusters
journal, March 2004
- Pakhira, Malay K.; Bandyopadhyay, Sanghamitra; Maulik, Ujjwal
- Pattern Recognition, Vol. 37, Issue 3
Searching for hidden earthquakes in Southern California
journal, April 2019
- Ross, Zachary E.; Trugman, Daniel T.; Hauksson, Egill
- Science, Vol. 364, Issue 6442
Influence of particle characteristics on granular friction
journal, January 2005
- Anthony, Jennifer L.
- Journal of Geophysical Research, Vol. 110, Issue B8
Preseismic Fault Creep and Elastic Wave Amplitude Precursors Scale With Lab Earthquake Magnitude for the Continuum of Tectonic Failure Modes
journal, April 2020
- Shreedharan, Srisharan; Bolton, David Chas; Rivière, Jacques
- Geophysical Research Letters, Vol. 47, Issue 8
A dendrite method for cluster analysis
journal, January 1974
- Calinski, T.; Harabasz, J.
- Communications in Statistics - Theory and Methods, Vol. 3, Issue 1
Acoustic emission and creep in rock at high confining pressure and differential stress
journal, April 1977
- Lockner, D.; Byerlee, J.
- Bulletin of the Seismological Society of America, Vol. 67, Issue 2
Earthquake Catalog‐Based Machine Learning Identification of Laboratory Fault States and the Effects of Magnitude of Completeness
journal, December 2018
- Lubbers, Nicholas; Bolton, David C.; Mohd‐Yusof, Jamaludin
- Geophysical Research Letters, Vol. 45, Issue 24
Explicit Magnification Control of Self-Organizing Maps for “Forbidden” Data
journal, May 2007
- Merenyi, E.; Jain, A.; Villmann, T.
- IEEE Transactions on Neural Networks, Vol. 18, Issue 3
Machine Learning Predicts the Timing and Shear Stress Evolution of Lab Earthquakes Using Active Seismic Monitoring of Fault Zone Processes
journal, July 2021
- Shreedharan, Srisharan; Bolton, David Chas; Rivière, Jacques
- Journal of Geophysical Research: Solid Earth, Vol. 126, Issue 7
Exploiting Data Topology in Visualization and Clustering of Self-Organizing Maps
journal, April 2009
- Tasdemir, K.; Merenyi, E.
- IEEE Transactions on Neural Networks, Vol. 20, Issue 4
Earthquakes and rock creep
journal, January 1951
- Benioff, Hugo
- Bulletin of the Seismological Society of America, Vol. 41, Issue 1
Seismicity trends and potential for large earthquakes in the Alaska-Aleutian region
journal, January 1994
- Bufe, Charles G.; Nishenko, Stuart P.; Varnes, David J.
- Pure and Applied Geophysics PAGEOPH, Vol. 142, Issue 1
Predictive modeling of the seismic cycle of the Greater San Francisco Bay Region
journal, January 1993
- Bufe, Charles G.; Varnes, David J.
- Journal of Geophysical Research, Vol. 98, Issue B6
Improved tests reveal that the accelerating moment release hypothesis is statistically insignificant: NEW TESTS CONTRADICT AMR HYPOTHESIS
journal, August 2008
- Hardebeck, Jeanne L.; Felzer, Karen R.; Michael, Andrew J.
- Journal of Geophysical Research: Solid Earth, Vol. 113, Issue B8
Predicting earthquakes by analyzing accelerating precursory seismic activity
journal, January 1989
- Varnes, David J.
- Pure and Applied Geophysics PAGEOPH, Vol. 130, Issue 4