In rocks that are polydeformed an approach which separates faults prior to stress inversion is more appropriate. The traditional stress inversion approach involving the concept of the best-Jit stress tensor, e.g. a tensor which minimises the misfit between calculated and measured fault-striae data, often risks computing artificial stress tensors that are some form of average of mixed sets of real stress tensors. A new approach is proposed in which fault data are pre-processed to group the faults on the basis of their response to all possible orientations and magnitudes of applied stress. A computer method is described which utilises cluster analysis based on the right-dihedra method to divide dynamically-mixed fault populations to monophase subsets. This division is based on the ranked similarity coefficients of each fault pair from the raw data set. The data clusters form dynamically-homogeneous subsets, which are used for the composite right-dihedra solution. This solution is re-computed for the reduced stress tensor defined by the orientation of principal stress axes and the ratio of their magnitudes. (C) 1999 Elsevier Science Ltd. All rights reserved.
机构:
Kyoto Univ, Div Earth & Planetary Sci, Grad Sch Sci, Sakyo Ku, Kyoto 6068502, JapanKyoto Univ, Div Earth & Planetary Sci, Grad Sch Sci, Sakyo Ku, Kyoto 6068502, Japan
机构:
School of Energy Engineering, Xi'an University of Science and Technology, Xi'anSchool of Energy Engineering, Xi'an University of Science and Technology, Xi'an
Zhu G.
Jiang Q.
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School of Energy Engineering, Xi'an University of Science and Technology, Xi'anSchool of Energy Engineering, Xi'an University of Science and Technology, Xi'an
Jiang Q.
Wu Y.
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School of Energy Engineering, Xi'an University of Science and Technology, Xi'anSchool of Energy Engineering, Xi'an University of Science and Technology, Xi'an
Wu Y.
Dou L.
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机构:
Key Laboratory of Deep Coal Resource Mining, Ministry of Education of China, China University of Mining and Technology, XuzhouSchool of Energy Engineering, Xi'an University of Science and Technology, Xi'an
Dou L.
Lin Z.
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School of Energy Engineering, Xi'an University of Science and Technology, Xi'anSchool of Energy Engineering, Xi'an University of Science and Technology, Xi'an
Lin Z.
Liu H.
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School of Energy Engineering, Xi'an University of Science and Technology, Xi'anSchool of Energy Engineering, Xi'an University of Science and Technology, Xi'an
Liu H.
Caikuang yu Anquan Gongcheng Xuebao/Journal of Mining and Safety Engineering,
2021,
38
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: 370
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