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DOI | 10.1029/2019GL082706 |
Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault | |
Ren, C. X.1,2; Dorostkar, O.3,4; Rouet-Leduc, B.2; Hulbert, C.2; Strebel, D.5; Guyer, R. A.2; Johnson, P. A.2; Carmeliet, J.4 | |
2019-07-16 | |
发表期刊 | GEOPHYSICAL RESEARCH LETTERS |
ISSN | 0094-8276 |
EISSN | 1944-8007 |
出版年 | 2019 |
卷号 | 46期号:13页码:7395-7403 |
文章类型 | Article |
语种 | 英语 |
国家 | USA; England; Switzerland |
英文摘要 | 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. We use machine learning 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. Our work provides novel insights into the applications of machine learning in studying frictional processes occurring in geophysical systems. Plain Language Summary Records of previous earthquakes do not provide adequate data for scientists to predict future earthquakes with sufficient certainty. In this study, we use computer simulations representing earthquakes as frictional slips and record hundreds of scaled earthquakes. We employ machine learning, an artificial intelligence technique, to estimate the fault friction. In machine learning, the computer is trained to establish a relation between emitted seismic signals and fault friction. Subsequently, when the trained model is applied to new seismic data, it can accurately estimate the fault friction. The similarities between our model and field-scale observations from real faults suggest that an extension of our approach may have potential of estimating the friction of geological faults leading to prediction of real earthquakes. |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000476960100039 |
WOS关键词 | STICK-SLIP DYNAMICS ; EARTHQUAKES ; STATISTICS |
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/185058 |
专题 | 气候变化 |
作者单位 | 1.Los Alamos Natl Lab, Space Data Sci & Syst Grp, MS D440, Los Alamos, NM 87545 USA; 2.Los Alamos Natl Lab, Geophys Grp, MS D446, Los Alamos, NM 87545 USA; 3.Univ Oxford, Dept Engn Sci, Oxford, England; 4.Swiss Fed Inst Technol, Swiss Fed Inst Technol Zurich, Dept Mech & Proc Engn, Zurich, Switzerland; 5.Empa, Swiss Fed Labs Mat Sci & Technol, Dubendorf, Switzerland |
推荐引用方式 GB/T 7714 | Ren, C. X.,Dorostkar, O.,Rouet-Leduc, B.,et al. Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault[J]. GEOPHYSICAL RESEARCH LETTERS,2019,46(13):7395-7403. |
APA | Ren, C. X..,Dorostkar, O..,Rouet-Leduc, B..,Hulbert, C..,Strebel, D..,...&Carmeliet, J..(2019).Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault.GEOPHYSICAL RESEARCH LETTERS,46(13),7395-7403. |
MLA | Ren, C. X.,et al."Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault".GEOPHYSICAL RESEARCH LETTERS 46.13(2019):7395-7403. |
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