The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Machine Learning Predicts Laboratory Earthquakes
Open accessAbstract
We apply machine learning to data sets from shear laboratory experiments, with the goal of identifying hidden signals that precede earthquakes. Here we show that by listening to the acoustic signal emitted by a laboratory fault, machine learning can predict the time remaining before it fails with great accuracy. These predictions are based solely on the instantaneous physical characteristics of the acoustical signal and do not make use of its history. Surprisingly, machine learning identifies a signal emitted from the fault zone previously thought to be low‐amplitude noise that enables failure forecasting throughout the laboratory quake cycle. We infer that this signal originates from continuous grain motions of the fault gouge as the fault blocks displace. We posit that applying this approach to continuous seismic data may lead to significant advances in identifying currently unknown signals, in providing new insights into fault physics, and in placing bounds on fault failure times.
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Cites
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Non-volcanic tremor and low-frequency earthquake swarms
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The Parkfield, California, Earthquake Prediction Experiment
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OPERATIONAL EARTHQUAKE FORECASTING. State of Knowledge and Guidelines for Utilization
Jordan, T. H., Chen, Y.-T., Gasparini, P., Madariaga, R., Main, I. G., Marzocchi, W., Papadopoulos, G. A., Соболев, Г. А., Yamaoka, K., & Zschau, J. (2011) · Annals of Geophysics · doi:10.4401/ag-5350 · Cited by 431
Deep-Focus Earthquake Analogs Recorded at High Pressure and Temperature in the Laboratory
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Zhao, L. (2022) · ACM Computing Surveys · doi:10.1145/3450287 · Cited by 135
A blockchain-based federated learning mechanism for privacy preservation of healthcare IoT data
Moulahi, W., Jdey, I., Moulahi, T., Alawida, M., & Alabdulatif, A. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107630 · Cited by 103
Metadata
- Authors
- Rouet‐Leduc, B., Hulbert, C., Lubbers, N., Barros, K., Humphreys, C. J., & Johnson, P. A.
- Venue
- Geophysical Research Letters
- Publisher
- American Geophysical Union (AGU)
- Published
- 2017-09-22
- Volume
- 44
- Issue
- 18
- Pages
- 9276-9282
- ISSN
- 0094-8276 (ISSN-L)
- 1944-8007
- OpenAlex
- W2625710583
- Cited by
- 420
- References
- 13
- FWCI
- 20.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Rouet‐Leduc, B., Hulbert, C., Lubbers, N., Barros, K., Humphreys, C. J., & Johnson, P. A. (2017). Machine Learning Predicts Laboratory Earthquakes. Geophysical Research Letters. https://doi.org/10.1002/2017gl074677
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