Identification of homogeneous region boundaries of fractured rock masses in candidate sites for Chinese HLW repository
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ORIGINAL PAPER
Identification of homogeneous region boundaries of fractured rock masses in candidate sites for Chinese HLW repository Liang Guo 1,2,3
&
Lizhou Wu 2 & Junwei Zhang 1 & Mingwei Liao 1 & Youjun Ji 1
Received: 16 August 2019 / Accepted: 30 April 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Identification of homogeneous region boundaries in a fractured rock mass is the basis of statistics and modeling of discontinuities. In engineering practice, an objective division can be obtained by adopting the major influential factors as indicators and the optimal approach as a tool. For discontinuities in Beishan, a main candidate site for Chinese high-level radioactive waste (HLW) repository, pretreatment techniques (e.g., sampling window truncation, sampling bias correction and block-net variation correction) were used to deal with field data. Programming was then applied to realize homogeneous region division via several methods (e.g., the improved Miller’s method, Mahtab and Yegulalp’s method, and correlation coefficient method). This study preliminarily examined these methods’ distinguishing capability, applicability, and limitations. Results showed that the applicability of the correlation coefficient method as well as the Mahtab and Yegulalp’s method was weak; the improved Miller’s method appeared most satisfactory, especially with a large-area strategy (34 blocks). Finally, two statistical homogeneous regions were obtained by applying the optimal approach to the Jijicao block. Findings can offer guidance for subsequent research on discrete fracture network (DFN) modeling and seepage path simulation, which is important for the prediction and evaluation of radionuclide migration in rock masses. Keywords Fractured rock masses . Statistical homogeneous region . Division approach . Distinguishing capability
Abbreviations eij fij Xi (i = 1, 2, 3, n) Yi (i = 1, 2, 3, n) Ri
is the expected frequency of poles in unit ij is the observation frequency of poles in unit ij is the observation frequency in blocks of region 1 is the observation frequency in blocks of region 2 is the total number of poles observed in row i
Cj N D TCF c k m n
* Liang Guo [email protected] 1
School of Geoscience and Technology, Southwest Petroleum University, Xindu District, Xindu Road No. 8, Chengdu 610500, Sichuan, China
2
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, Sichuan, China
3
Sichuan Mineral Resources Research Center, Chengdu 610059, Sichuan, China
p q α γ δ αn and βn
is the total number of poles observed in column j is the total number of poles observed is the random density is the correction coefficient used in the sampling line is the fractional area of a block versus the upper-hemisphere is the minimum integer is the average pole number of one block in the sample, m = qc is the number of blocks selected for use on the lower-hemisphere, and the value in the correlation coefficient method is 100 is the pro
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