Abstract
Accurate orientation distributions are crucial to generating a reliable discrete fracture network (DFN) model for rock mass, while conventional one-dimensional (1D) and two-dimensional (2D) observation data have significant sampling bias. The study proposes a complete analytical method for estimating the orientation distributions of three-dimensional (3D) fractures in rock mass in conjunction with trace statistics in a sampling window, which is suitable for most continuous distributions by reducing the sampling bias. Traces are divided into three categories to derive the geometric probabilistic relationships between 2D trace statistics and 3D fracture orientation distributions. The moment estimation, number estimation, and normalization error functions are derived, and the distribution parameters are determined by minimizing the total error function. The proposed method is compared with the Terzaghi family methods and validated by multiple sets of stochastic fracture networks with different orientation distributions and sampling windows generated by the Monte Carlo method. The results indicate that the estimated continuous orientation distributions subjected to the error functions from a large single sampling window are well matched with the true distributions after removing the number estimation error functions of trace samples fewer than 20. Daxiagu tunnel is selected as a case study and the distributions estimated by the proposed method are more coincident with the field observation than those fitted by the orientations of the rock outcrops on the excavation face.
Similar content being viewed by others
Data Availability
Not applicable.
Code Availability
Not applicable.
Abbreviations
- \(\vec{n}\) :
-
Unit normal vector of the preferred fracture in Oxyz
- \(\vec{n}_{yz}\) :
-
Projection of \(\vec{n}\) on yOz coordinate plane
- \(\vec{n}_{xy}\) :
-
Projection of \(\vec{n}\) on xOy coordinate plane
- \(\vec{l}\) :
-
Direction vector of the trace in yOz
- \(\gamma\) :
-
Angle between \(\vec{n}\) and positive z-axis
- \(\varphi\) :
-
Angle between \(\vec{n}_{xy}\) and positive y-axis
- \(\theta\) :
-
Dip angle between the trace and positive y-axis
- \(x\) :
-
Diameter of the preferred fracture
- \(G(\theta )\) :
-
Cumulative distribution function of \(\theta\)
- \(G_{\gamma } (\gamma )\) :
-
Cumulative distribution function of \(\gamma\)
- \(G_{\varphi } (\varphi )\) :
-
Cumulative distribution function of \(\varphi\)
- \(D(x)\) :
-
Cumulative distribution function of x
- \(g_{\gamma } (\gamma )\) :
-
Probability density function of \(\gamma\)
- \(\xi\) :
-
Unknown parameters of \(g_{\gamma } (\gamma )\) and \(g_{\varphi } (\varphi )\)
- \(u_{\gamma }\) :
-
Expected value of \(\gamma\)
- \(u_{\varphi }\) :
-
Expected value of \(\varphi\)
- \(\sigma_{\gamma }\) :
-
Standard deviation of \(\gamma\)
- \(\sigma_{\varphi }\) :
-
Standard deviation of \(\varphi\)
- \(E(\theta )\) :
-
Expected value of \(\theta\)
- \(a_{k}\) :
-
kth-order expected original moment of \(\theta\)
- \(b_{k}\) :
-
kth-order expected central moment of \(\theta\)
- \(\hat{a}_{k}\) :
-
kth-order estimated original moment of \(\theta\)
- \(\hat{b}_{k}\) :
-
kth-order estimated central moment of \(\theta\)
- \(\hat{N}\) :
-
Number of trace samples intersecting the window
- \(\hat{\theta }_{i}\) :
-
ith dip angle of trace samples
- \(\overline{\theta }\) :
-
Sample mean of trace dip angles
- \(h\) :
-
Height of the rectangular infinitesimal in \(wO^{\prime}h\)
- \(dw\) :
-
Width of the rectangular infinitesimal in \(wO^{\prime}h\)
- \(W\) :
-
Projection width of the sampling window on w-axis
- w 1 :
-
Lower limit of the sampling window on w-axis
- w 2 :
-
Upper limit of the sampling window on w-axis
- \(D\) :
-
Diameter of the circular sampling window
- \(\rho_{V}\) :
-
Volume density of fracture centers
- \(\rho_{S}\) :
-
Trace density
- \(\alpha\) :
-
Angle between the fracture plane and the excavation face
- \(\beta\) :
-
Angle between the fracture unit normal vector \(\vec{n}\) and the excavation face
- \(dH\) :
-
Height on the bottom surface of the distribution region of the centers of fractures intersecting the rectangular infinitesimal
- \(dS_{T}\) :
-
Bottom area of the distribution space of the centers of fractures intersecting the rectangular infinitesimal with both censored ends
- \(dS_{D}\) :
-
Bottom area of the distribution space of the centers of fractures intersecting the rectangular infinitesimal with one censored end and one observable end
- \(dS_{C}\) :
-
Bottom area of the distribution space of the centers of fractures intersecting the rectangular infinitesimal with both observable ends
- \(dV_{T}\) :
-
Volume of the distribution space of the centers of fractures intersecting the rectangular infinitesimal with both censored ends
- \(dV_{D}\) :
-
Volume of the distribution space of the centers of fractures intersecting the rectangular infinitesimal with one censored end and one observable end
- \(dV_{C}\) :
-
Volume of the distribution space of the centers of fractures intersecting the rectangular infinitesimal with both observable ends
- \(dn_{FT}\) :
-
Number of fractures intersecting the rectangular infinitesimal with both censored ends
- \(dn_{FD}\) :
-
Number of fractures intersecting the rectangular infinitesimal with one censored end and one observable end
- \(dn_{FC}\) :
-
Number of fractures intersecting the rectangular infinitesimal with both observable ends
- \(n_{FT}\) :
-
Number of fractures intersecting the whole sampling window with both censored ends
- \(n_{FD}\) :
-
Number of fractures intersecting the whole sampling window with one censored end and one observable end
- \(n_{FC}\) :
-
Number of fractures intersecting the whole sampling window with both observable ends
- \(N_{FT}\) :
-
Number of fractures of arbitrary size and orientation intersecting the whole sampling window with both censored ends
- \(N_{FD}\) :
-
Number of fractures of arbitrary size and orientation intersecting the whole sampling window with one censored end and one observable end
- \(N_{FC}\) :
-
Number of fractures of arbitrary size and orientation, intersecting the whole sampling window with both observable ends
- \(N_{TT}\) :
-
Expected number of transecting traces
- \(N_{TD}\) :
-
Expected number of dissecting traces
- \(N_{TC}\) :
-
Expected number of contained traces
- \(\hat{N}_{TT}\) :
-
Estimated number of transecting traces
- \(\hat{N}_{TD}\) :
-
Estimated number of dissecting traces
- \(\hat{N}_{TC}\) :
-
Estimated number of contained traces
- \(r_{i}\) :
-
ith error function, \(i{ = 1,}...{,10}\)
- \(n\) :
-
Number of realizations in a group of simulation
- \(X_{i}\) :
-
ith estimator in a group of simulation
- \(\overline{X}\) :
-
Average of estimators in a group of simulation
- \(\mu\) :
-
Expected value of \(\overline{X}\)
References
Belousov TP, Mukhamediev SA, Kurtasov SF (1996) Joints orientation distributions in sedimentary rocks. Texture Stress Microstruct 25:245–250. https://doi.org/10.1155/TSM.25.245
Berkowitz B, Adler PM (1998) Stereological analysis of fracture network structure in geological formations. J Geophys Res 103(B7):15339–15360. https://doi.org/10.1029/98JB01072
Bingham C (1972) Distributions on the sphere and on the projective plane. Yale University
Chen JP, Xiao SF, Wang Q (1995) Three dimensional network modeling of stochastic fractures. Northeast Normal University Press
Chen JQ, Zhu HH, Li XJ (2016) Automatic extraction of discontinuity orientation from rock mass surface 3D point cloud. Comput Geosci 95:18–31. https://doi.org/10.1016/j.cageo.2016.06.015
Engelder T, Delteil J (2004) The orientation distribution of single joint sets. Geol Soc Lond Spec Publ 231:285–297. https://doi.org/10.1144/gsl.sp.2004.231.01.17
Fernandes AJ, Maldaner CH, Negri F, Rouleau A, Wahnfried ID (2016) Aspects of a conceptual groundwater flow model of the Serra Geral basalt aquifer (Sao Paulo, Brazil) from physical and structural geology data. Hydrogeol J 24:1199–1212. https://doi.org/10.1007/s10040-016-1370-6
Fisher RA (1953) Dispersion on a sphere. Proc R Soc Lond Ser A Math Phys Sci 217(1130):295–305
Fouché O, Diebolt J (2004) Describing the geometry of 3D fracture systems by correcting for linear sampling bias. Math Geol 36(1):33–63. https://doi.org/10.1023/B:MATG.0000016229.37309.fd
Gao M, Jin W, Zhang R, Xie J, Yu B, Duan H (2016) Fracture size estimation using data from multiple boreholes. Int J Rock Mech Min Sci 86:29–41. https://doi.org/10.1016/j.ijrmms.2016.04.005
Gaziev EG, Tiden EN (1979) Probabilistic approach to the study of jointing in rock masses. Bull Int Assoc Eng Geol 20:178–181. https://doi.org/10.1007/BF02591275
Glynn EF, Veneziano D, Einstein HH (1978) The probabilistic model for shearing resistance of jointed rock. In: Proceedings of the 19th US symposium on rock mechanics, Stateline, Nevada, pp 66–76
Gudmundsson A, De Guidi G, Scudero S (2013) Length–displacement scaling and fault growth. Tectonophys 608:1298–1309. https://doi.org/10.1016/j.tecto.2013.06.012
Han XD, Chen JP, Wang Q, Li YY, Zhang W, Yu TW (2016) A 3D fracture network model for the undisturbed rock mass at the Songta dam site based on small samples. Rock Mech Rock Eng 49:611–619. https://doi.org/10.1007/s00603-015-0747-5
Havaej M, Coggan J, Stead D, Elmo D (2016) A combined remote sensing–numerical modelling approach to the stability analysis of Delabole Slate Quarry, Cornwall. UK Rock Mech Rock Eng 49(4):1227–1245. https://doi.org/10.1007/s00603-015-0805-z
Huang L et al (2016) A novel method for correcting scanline-observational bias of discontinuity orientation. Sci Rep 6:22492. https://doi.org/10.1038/srep22942
Huang L, Tang H, Wang L, Juang CH (2019) Minimum scanline-to-fracture angle and sample size required to produce a highly accurate estimate of the 3-D fracture orientation distribution. Rock Mech Rock Eng 52:803–825. https://doi.org/10.1007/s00603-012-0244-z
Hyman JD, Karra S, Makedonska N, Gable CW, Painter SL, Viswanathan HS (2015) dfnWorks: A discrete fracture network framework for modeling subsurface flow and transport. Comput Geosci 84:10–19. https://doi.org/10.1016/j.cageo.2015.08.001
Ivanova VM, Sousa R, Murrihy B, Einstein HH (2014) Mathematical algorithm development and parametric studies with the GEOFRAC three-dimensional stochastic model of natural rock fracture systems. Comput Geosci 67:100–109. https://doi.org/10.1016/j.cageo.2013.12.004
Kulatilake PHSW (1986) Bivariate normal distribution fitting on discontinuity orientation clusters. Math Geol 18:181–195. https://doi.org/10.1007/BF00898282
Kulatilake PHSW, Wu TH (1984) Estimation of mean trace length of discontinuities. Rock Mech Rock Eng 17:215–232. https://doi.org/10.1007/BF01032335
Kulatilake PHSW, Wu TH, Wathugala DN (1990) Probabilistic modelling of joint orientation. Int J Numer Anal Method Geomech 14(5):325–350. https://doi.org/10.1002/nag.1610140503
Li XJ, Zuo YL, Zhuang XY, Zhu HH (2014a) Estimation of fracture trace length distributions using probability weighted moments and L-moments. Eng Geol 168:69–85. https://doi.org/10.1016/j.enggeo.2013.10.025
Li YY, Wang Q, Chen JP, Han LL, Song SY (2014b) Identification of structural domain boundaries at the Songta dam site based on nonparametric tests. Int J Rock Mech Min Sci 70:177–184. https://doi.org/10.1016/j.ijrmms.2014.04.018
Li XJ, Chen JQ, Zhu HH (2016) A new method for automated discontinuity trace mapping on rock mass 3D surface model. Comput Geosci 89:118–131. https://doi.org/10.1016/j.cageo.2015.12.010
Li XJ, Chen ZY, Chen JQ, Zhu HH (2019) Automatic characterization of rock mass discontinuities using 3D point clouds. Eng Geol 259:17. https://doi.org/10.1016/j.enggeo.2019.05.008
Mao YJ, Hu B, Wang L, Li Y (2018) Research on the fractal dimension of the orientation pole distribution for rock mass joint. Geotech Geol Eng 36:737–745. https://doi.org/10.1007/s10706-017-0350-5
Marcotte D, Henry E (2002) Automatic joint set clustering using a mixture of bivariate normal distributions. Int J Rock Mech Min Sci 39(3):323–334. https://doi.org/10.1016/S1365-1609(02)00033-3
Mauldon M (1998) Estimating mean fracture trace length and density from observations in convex windows. Rock Mech Rock Eng 31:201–216. https://doi.org/10.1007/s006030050021
Mauldon M, Mauldon JG (1997) Fracture sampling on a cylinder: from scanlines to boreholes and tunnels. Rock Mech Rock Eng 30:129–144. https://doi.org/10.1007/BF01047389
Mauldon M, Dunne WM, Rohrbaugh MB (2001) Circular scanlines and circular windows: new tools for characterizing the geometry of fracture traces. J Struct Geol 23(2–3):247–258. https://doi.org/10.1016/S0191-8141(00)00094-8
Middleton RS, Carey JW, Currier RP, Hyman JD, Kang QJ, Karra S, JoaquínJiménez-Martínez J, Porter ML, Viswanathan HS (2015) Shale gas and non-aqueous fracturing fluids: opportunities and challenges for supercritical CO2. Appl Energy 147:500–509. https://doi.org/10.1016/j.apenergy.2015.03.023
Mourzenko VV, Thovert JF, Adler PM (2011) Trace analysis for fracture networks with anisotropic orientations and heterogeneous distributions. Phys Rev E 83(3):031104. https://doi.org/10.1103/PhysRevE.83.031104
Munier R (2004) Statistical analysis of fracture data, adapted for modelling discrete fracture networks-version 2. Swedish Nuclear Fuel and Waste Management Comapny
Pandey SN, Chaudhuri A, Kelkar S (2017) A coupled thermo-hydro-mechanical modeling of fracture aperture alteration and reservoir deformation during heat extraction from a geothermal reservoir. Geotherm 65:17–31. https://doi.org/10.1016/j.geothermics.2016.08.006
Perino A, Barla G (2015) Resonant column apparatus tests on intact and jointed rock specimens with numerical modelling validation. Rock Mech Rock Eng 48:197–211. https://doi.org/10.1007/s00603-014-0564-2
Piggott AR (1997) Fractal relations for the diameter and trace length of disc-shaped fractures. J Geophys Res 102(B8):18121–18125. https://doi.org/10.1029/97JB01202
Singhal BBS, Gupta RP (2010) Applied hydrogeology of fractured rocks. Springer
Sisavath S, Mourzenko V, Genthon P, Thovert JF, Adler PM (2004) Geometry, percolation and transport properties of fracture networks derived from line data. Geophys J Int 157(2):917–934. https://doi.org/10.1111/j.1365-246X.2004.02185.x
Song JJ, Lee CI (2001) Estimation of joint length distribution using window sampling. Int J Rock Mech Min Sci 38(4):519–528. https://doi.org/10.1007/s00603-012-0244-z
Terzaghi RD (1965) Sources of error in joint surveys. Geotech 15(3):287–304. https://doi.org/10.1680/geot.1965.15.3.287
Wang X (2005) Stereological interpretation of rock fracture traces on borehole walls and other cylindrical surfaces. Virginia Polytechnic Institute and State University
Warburton PM (1980a) A stereological interpretation of joint trace data. Int J Rock Mech Min Sci 17(4):181–190. https://doi.org/10.1016/0148-9062(80)91084-0
Warburton PM (1980b) Stereological interpretation of joint trace data: influence of joint shape and implications for geological surveys. Int J Rock Mech Min Sci 17(6):305–316. https://doi.org/10.1016/0148-9062(80)90513-6
Xu LM, Chen JP, Wang Q, Zhou FJ (2013) Fuzzy C-means cluster analysis based on mutative scale chaos optimization algorithm for the grouping of discontinuity sets. Rock Mech Rock Eng 46:189–198. https://doi.org/10.1007/s00603-012-0244-z
Zhang W, Chen JP, Liu C, Huang R, Li M, Zhang Y (2012) Determination of geometrical and structural representative volume elements at the Baihetan dam site. Rock Mech Rock Eng 45(3):409–419. https://doi.org/10.1007/s00603-011-0191-0
Zhang PW, Hu LM, Meegoda JN, Gao SY (2015) Micro/nano-pore network analysis of gas flow in shale matrix. Sci Rep 5:13501. https://doi.org/10.1038/srep13501
Zhang KS, Wu W, Zhu HH, Zhang LY, Li XJ, Zhang H (2020) A modified method of discontinuity trace mapping using three-dimensional point clouds of rock mass surfaces. J Rock Mech Geotech Eng 12(3):571–586. https://doi.org/10.1016/j.jrmge.2019.10.006
Zhu HH, Zuo YL, Li XJ, Deng J, Zhuang XY (2014) Estimation of the fracture diameter distributions using the maximum entropy principle. Int J Rock Mech Min Sci 72:127–137. https://doi.org/10.1016/j.ijrmms.2014.09.006
Acknowledgement
The authors would like to acknowledge the financial support. This work was supported partially by the National Natural Science Foundation of China (41972277, 41602300, and 41831281), Special Fund for Basic Research on Scientific Instruments of the National Natural Science Foundation of China (4182780021), and Sichuan Railway Investment Group Co., Ltd. (SRIG2019GG0004).
Funding
This work was supported partially by the National Natural Science Foundation of China (41972277, 41602300, and 41831281), Special Fund for Basic Research on Scientific Instruments of the National Natural Science Foundation of China (4182780021), and Sichuan Railway Investment Group Co., Ltd. (SRIG2019GG0004).
Author information
Authors and Affiliations
Contributions
Not applicable.
Corresponding author
Ethics declarations
Conflicts of interest
No conflict of interest exits in this manuscript.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices
Appendix A
Number of Dissecting Traces
The bottom area of the distribution space of the centers of fractures with \(x \in (x,\;x + dx)\), \(\gamma \in (\gamma ,\;\gamma + d\gamma )\), and \(\varphi \in (\varphi ,\;\varphi + d\varphi )\), intersecting the rectangular infinitesimal with one censored end and one observable end, is the shadow area surrounded by the red line in Fig.
17. According to the relationship between the fracture diameter x and the height of the rectangular infinitesimal h(w), the bottom area is determined according to the following two situations.
(a) When \(x < h(w)\), the shadow region boundary in the positive v-axis satisfies the following function in Fig. 17a.
The bottom area \(dS_{D1}\) of the distribution space of the centers of fractures, intersecting the rectangular infinitesimal with one censored end and one observable end, is expressed as:
b) When \(x \ge h(w)\), the shadow region boundary in the positive v-axis in Fig. 17b satisfies the following equation.
The bottom area \(dS_{D2}\) of the distribution space of the centers of fractures, intersecting the rectangular infinitesimal with one censored end and one observable end, is expressed as:
Equation (44) and Eq. (46) can be expressed by the unified formula. The bottom area \(dS_{D}\) of the distribution space of the centers of fractures, intersecting the rectangular infinitesimal with one censored end and one observable end, is expressed as:
The volume \(dV_{D}\) of the distribution space of the centers of fractures, intersecting the rectangular infinitesimal with one censored end and one observable end, is determined by multiplying Eq. (18) and Eq. (47).
\(dn_{FD}\) is the number of fractures intersecting the rectangular infinitesimal with one censored end and one observable end. According to the definition, \(dn_{FD}\) is determined by multiplying the volume of the distribution space and the volume density of fracture centers.
The number \(n_{FD}\) of fractures, intersecting the whole sampling window with one censored end and one observable end, is derived by integrating Eq. (49) over \(w \in (0,\;D)\).
The number \(N_{FD}\) of fractures of arbitrary size and orientation, intersecting the whole sampling window with one censored end and one observable end, is derived by integrating Eq. (50) over \(x \in (x,\;x + dx)\), \(\gamma \in (\gamma ,\;\gamma + d\gamma )\), and \(\varphi \in (\varphi ,\;\varphi + d\varphi )\).
The number of fractures intersecting with one censored end and one observable end is equal to that of dissecting traces because of the one-to-one correspondence between fractures and traces. \(N_{TD}\) is the expected number of dissecting traces in the sampling window, and Eq. (51) can be rewritten as:
Number of contained traces
The bottom area of the distribution space of the centers of fractures with \(x \in (x,\;x + dx)\), \(\gamma \in (\gamma ,\;\gamma + d\gamma )\), and \(\varphi \in (\varphi ,\;\varphi + d\varphi )\), intersecting the rectangular infinitesimal with both observable ends, is the shadow area surrounded by the red line in Fig.
18. According to the relationship between the fracture diameter x and the height of the rectangular infinitesimal h(w), the bottom area is determined according to the following two situations.
(a) When \(x < h(w)\), the shadow region boundary in the positive v-axis in Fig. 18a satisfies the following equation.
The bottom area \(dS_{C1}\) of the distribution space of the centers of fractures is intersecting the rectangular infinitesimal with both observable ends.
b) When \(x \ge h(w)\), the shadow region boundary in the positive v-axis in Fig. 18b satisfies the following equation.
The bottom area \(dS_{C2}\) of the distribution space of the centers of fractures intersecting the rectangular infinitesimal with both observable ends, and is expressed as:
Equation (54) and Eq. (56) can be expressed by the unified formula. The bottom area \(dS_{C}\) of the distribution space of the centers of fractures intersecting the rectangular infinitesimal with both observable ends is expressed as:
The volume \(dV_{C}\) of the distribution space of the centers of fractures intersecting the rectangular infinitesimal with both observable ends is determined by multiplying Eq. (18) and Eq. (57).
\(dn_{FC}\) is the number of fractures intersecting the rectangular infinitesimal with both observable ends. According to the definition, \(dn_{FC}\) is determined by multiplying the volume of the distribution space and the volume density of fracture centers.
The number \(n_{FC}\) of fractures intersecting the whole sampling window with both observable ends is derived by integrating Eq. (59) over \(w \in (0,\;D)\).
The number \(N_{FC}\) of fractures of arbitrary size and orientation intersecting the whole sampling window with both observable ends is derived by integrating Eq. (60) over \(x \in (x,\;x + dx)\), \(\gamma \in (\gamma ,\;\gamma + d\gamma )\), and \(\varphi \in (\varphi ,\;\varphi + d\varphi )\).
The number of fractures intersecting with both observable ends is equal to that of contained traces because of the one-to-one correspondence between fractures and traces. \(N_{TC}\) is the expected number of contained traces in the sampling window, and Eq. (61) can be rewritten as:
Rights and permissions
About this article
Cite this article
Zhang, Q., Wang, X., He, L. et al. Estimation of Fracture Orientation Distributions from a Sampling Window Based on Geometric Probabilistic Method. Rock Mech Rock Eng 54, 3051–3075 (2021). https://doi.org/10.1007/s00603-021-02431-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00603-021-02431-0