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Deformation Precursors to Catastrophic Failure in Rocks

Journal Article · · Geophysical Research Letters
DOI:https://doi.org/10.1029/2020gl090255· OSTI ID:1852268
 [1];  [2];  [3];  [4];  [5]
  1. Univ. of Oslo (Norway). The Njord Centre. Dept. of Geosciences. Physics of Geological Processes; Univ. of Oslo (Norway)
  2. Univ. of Oslo (Norway). Center for Computing in Science Education. Dept. of Physics; Michigan State Univ., East Lansing, MI (United States). Dept. of Physics and Astronomy
  3. Univ. of Copenhagen (Denmark). The Niels Bohr Inst.
  4. Univ. of Southern California, Los Angeles, CA (United States). Dept. of Earth Sciences
  5. Univ. of Oslo (Norway). The Njord Centre. Dept. of Geosciences. Physics of Geological Processes; Univ. of Grenoble Alpes, Grenoble (France). Univ. Savoie Mont Blanc. CNRS. IRD. IFSTTAR. ISTerre
+ Show Author Affiliations
Forecasting the timing of catastrophic failure, such as crustal earthquakes, has been a central concern for centuries. Such forecasting requires identifying signals that evolve or accelerate in the precursory phase leading to failure, and the subset of signals that may be detected in the crust. We develop machine learning models to predict the proximity of catastrophic failure in synchrotron X-ray tomography triaxial compression experiments on rocks using characteristics of evolving fracture networks. We then examine the characteristics that most strongly influence the model results, and thus may be considered the best predictors of the proximity of macroscopic failure. Finally, the resulting suite of predictive parameters underscores the importance of dilation in the precursory phase leading to catastrophic failure. The results indicate that the evolution of the strain energy density field may provide more robust predictions of the proximity of failure than other existing metrics of rock deformation.
Research Organization:
Univ. of Southern California, Los Angeles, CA (United States)
Sponsoring Organization:
Norwegian Research Council; USDOE; USDOE Office of Science (SC)
Grant/Contract Number:
SC0016520
OSTI ID:
1852268
Alternate ID(s):
OSTI ID: 1786752
Journal Information:
Geophysical Research Letters, Journal Name: Geophysical Research Letters Journal Issue: 24 Vol. 47; ISSN 0094-8276
Publisher:
American Geophysical Union (AGU)Copyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

58 GEOSCIENCES
Geology
dilation
earthquakes
fracture
machine learning
precursors
triaxial compression