气候变化领域集成服务门户(CCPortal): Predicting the proximity to macroscopic failure using local strain populations from dynamic in situ X-ray tomography triaxial compression experiments on rocks

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DOI	10.1016/j.epsl.2020.116344
	Predicting the proximity to macroscopic failure using local strain populations from dynamic in situ X-ray tomography triaxial compression experiments on rocks
	McBeck J.; Aiken J.M.; Ben-Zion Y.; Renard F.
发表日期	2020
ISSN	0012821X
卷号	543
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.
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
语种	英语
来源期刊	Earth and Planetary Science Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/202774
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
推荐引用方式 GB/T 7714	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],2020,543.
APA	McBeck J.,Aiken J.M.,Ben-Zion Y.,&Renard F..(2020).Predicting the proximity to macroscopic failure using local strain populations from dynamic in situ X-ray tomography triaxial compression experiments on rocks.Earth and Planetary Science Letters,543.
MLA	McBeck J.,et al."Predicting the proximity to macroscopic failure using local strain populations from dynamic in situ X-ray tomography triaxial compression experiments on rocks".Earth and Planetary Science Letters 543(2020).