globalchange  > 气候变化与战略
DOI: 10.1016/j.epsl.2020.116344
论文题名:
Predicting the proximity to macroscopic failure using local strain populations from dynamic in situ X-ray tomography triaxial compression experiments on rocks
作者: McBeck J.; Aiken J.M.; Ben-Zion Y.; Renard F.
刊名: Earth and Planetary Science Letters
ISSN: 0012821X
出版年: 2020
卷: 543
语种: 英语
中文关键词: machine learning ; strain localization ; tomography ; triaxial compression
英文关键词: Deformation ; Failure (mechanical) ; Forecasting ; Imaging systems ; Learning algorithms ; Machine components ; Machine learning ; Outages ; Population statistics ; Rock mechanics ; Rocks ; Tomography ; X rays ; Dilatational strain ; Large-scale dynamics ; Machine learning models ; Strain accumulations ; Strain components ; Strain deformation ; Training machines ; Triaxial compression ; Shear strain ; compression ; failure analysis ; machine learning ; prediction ; rock mechanics ; shear strain ; strain analysis ; tomography ; triaxial test ; X-ray analysis
英文摘要: Predicting the proximity of large-scale dynamic failure is a critical concern in the engineering and geophysical sciences. Here we use evolving contractive, dilatational, and shear strain deformation preceding failure in dynamic X-ray tomography experiments to examine which strain components best predict the proximity to failure. We develop machine learning models to predict the proximity to failure using time series of three-dimensional local incremental strain tensor fields acquired in rock deformation experiments under stress conditions of the upper crust. Three-dimensional scans acquired in situ throughout triaxial compression experiments provide a distribution of density contrasts from which we estimate the three-dimensional incremental strain that accumulates between each scan acquisition. Training machine learning models on multiple experiments of six rock types provides suites of feature importance that indicate the predictive power of each feature. Comparing the average importance of groups of features that include information about each strain component quantifies the ability of the contractive, dilatational and shear strain to predict the proximity of macroscopic failure. A total of 24 models of four machine learning algorithms with six rock types indicate that 1) the dilatational strain provides the best predictive power of the strain components, and 2) the intermediate values (25th-75th percentile) of the strain population provide the best predictive power of the statistics of the strain populations. In addition, the success of the predictions of models trained on one rock type and tested on other rock types quantifies the similarities and differences of the precursory strain accumulation process in the six rock types. These similarities suggest the potential existence of a unified theory of brittle rock deformation for a range of rock types. © 2020 Elsevier B.V.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/165152
Appears in Collections:气候变化与战略

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作者单位: Physics of Geological Processes, The Njord Centre, Department of Geosciences, University of Oslo, Oslo, Norway; Center for Computing in Science Education, Department of Physics, University of Oslo, Oslo, Norway; Department of Physics and Astronomy, Michigan State University, East Lansing, MI, United States; Department of Earth Sciences, University of Southern California, Los Angeles, CA, United States; University Grenoble Alpes, University Savoie Mont Blanc, CNRS, IRD, IFSTTAR, ISTerre, Grenoble, 38000, France

Recommended Citation:
McBeck J.,Aiken J.M.,Ben-Zion Y.,et al. Predicting the proximity to macroscopic failure using local strain populations from dynamic in situ X-ray tomography triaxial compression experiments on rocks[J]. Earth and Planetary Science Letters,2020-01-01,543
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