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DOI | 10.1002/2017GL074677 |
Machine Learning Predicts Laboratory Earthquakes | |
Rouet-Leduc, Bertrand1,2,3; Hulbert, Claudia1,2; Lubbers, Nicholas1,2,4; Barros, Kipton1,2; Humphreys, Colin J.3; Johnson, Paul A.5 | |
2017-09-28 | |
发表期刊 | GEOPHYSICAL RESEARCH LETTERS |
ISSN | 0094-8276 |
EISSN | 1944-8007 |
出版年 | 2017 |
卷号 | 44期号:18 |
文章类型 | Article |
语种 | 英语 |
国家 | USA; England |
英文摘要 | 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. Plain Language Summary Predicting the timing and magnitude of an earthquake is a fundamental goal of geoscientists. In a laboratory setting, we show we can predict "labquakes" by applying new developments in machine learning (ML), which exploits computer programs that expand and revise themselves based on new data. We use ML to identify telltale sounds-much like a squeaky door-that predict when a quake will occur. The experiment closely mimics Earth faulting, so the same approach may work in predicting timing, but not size, of an earthquake. This approach could be applied to predict avalanches, landslides, failure of machine parts, and more. |
英文关键词 | machine learning earthquake prediction laboratory earthquakes acoustic signal identification earthquake precursors |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000413148100020 |
WOS关键词 | NON-VOLCANIC TREMOR ; ACOUSTIC EMISSIONS ; NEURAL-NETWORKS ; SLIP ; PRECURSORS ; SUBDUCTION ; FRICTION ; BEHAVIOR |
WOS类目 | Geosciences, Multidisciplinary |
WOS研究方向 | Geology |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/26997 |
专题 | 气候变化 |
作者单位 | 1.Los Alamos Natl Lab, Theoret Div, Los Alamos, NM 87545 USA; 2.Los Alamos Natl Lab, CNLS, Los Alamos, NM 87545 USA; 3.Univ Cambridge, Dept Mat Sci & Met, Cambridge, England; 4.Boston Univ, Dept Phys, 590 Commonwealth Ave, Boston, MA 02215 USA; 5.Los Alamos Natl Lab, Geophys Grp, Los Alamos, NM USA |
推荐引用方式 GB/T 7714 | Rouet-Leduc, Bertrand,Hulbert, Claudia,Lubbers, Nicholas,et al. Machine Learning Predicts Laboratory Earthquakes[J]. GEOPHYSICAL RESEARCH LETTERS,2017,44(18). |
APA | Rouet-Leduc, Bertrand,Hulbert, Claudia,Lubbers, Nicholas,Barros, Kipton,Humphreys, Colin J.,&Johnson, Paul A..(2017).Machine Learning Predicts Laboratory Earthquakes.GEOPHYSICAL RESEARCH LETTERS,44(18). |
MLA | Rouet-Leduc, Bertrand,et al."Machine Learning Predicts Laboratory Earthquakes".GEOPHYSICAL RESEARCH LETTERS 44.18(2017). |
条目包含的文件 | 条目无相关文件。 |
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