Tectonic interpretation of the connectivity of a multiscale fracture system in limestone

Abstract

This paper studies the statistics and tectonism of a multiscale natural fracture system in limestone. The fracture network exhibits a self‐similar characteristic with a correlation between its power law length exponent a and fractal dimension D, i.e., a ≈ D + 1. Contradicting the scale‐invariant connectivity of idealized self‐similar systems, the percolation state of trace patterns mapped at different scales and localities of the study area varies significantly, from well to poorly connected. A tectonic interpretation based on a polyphase fracture network evolution history is proposed to explain this discrepancy. We present data to suggest that the driving force for fracture formation may be dissipated at the end of a tectonic event when the system becomes connected. However, the “effective” connectivity can successively be reduced by cementation of early fractures and reestablished by subsequent cracking, rendering a variable “apparent” connectivity that can be significantly above the percolation threshold.

Influence of Landscape Coverage on Measuring Spatial and Length Properties of Rock Fracture Networks: Insights from Numerical Simulation

“…Actually we can also see from Fig. 5b that the landscape coverage can lead to a curvature at the small scale in the density distribution of fracture lengths, which may be related to the socalled ''truncation effect'' (Pickering et al 1995;Bonnet et al 2001) that is always present in the length distribution plot of outcrop data (Davy 1993;Odling 1997;Odling et al 1999;Bour et al 2002;Davy et al 2010;Le Garzic et al 2011;Bertrand et al 2015;Lei et al 2015;Lei and Wang 2016).…”

“…However, in some geological sites, it can be very difficult to avoid the groundcover effects, because embryophytes can form vegetation on the Earth's surface over different length scales, making the mapped outcrop containing unknown gap zones (Ghosh and Daemen 1993;Rawnsley et al 1998;Gillespie et al 2001;Bisdom et al 2017). Especially for aerial photograph-based mapping, small-scale fractures can be easily hidden by forests (Lei and Wang 2016). We thus suggest that when conducting field mapping, the distribution of vegetation should also be measured and characterised, especially when the landscape occupies more than 10% of the sampling region.…”

“…However, the quality of photogrammetric measurements is often affected by groundcover effects due to the presence of forests and other plant species over the Earth's surface (Belayneh et al 2009;Le Garzic et al 2011). This may result in some biases in the measurement of fracture network geometries, such as an exaggeration of clustering behaviour, an underestimation of fracture density, and superficial truncation of large fractures (Lei and Wang 2016). Figure 1 shows a few examples of fracture patterns reported in the literature, in which the landscape coverage effects seem not trivial (Ghosh and Daemen 1993;Rawnsley et al 1998;Gillespie et al 2001;Bisdom et al 2017).…”

“…By analysing the samples of outcrop mapping, many important geometrical properties of natural fracture systems can be studied including their density, spacing, spatial organisation, and length distribution (Odling 1992(Odling , 1997Bonnet et al 2001;Bour et al 2002;Lei and Wang 2016). However, the quality of photogrammetric measurements is often affected by groundcover effects due to the presence of forests and other plant species over the Earth's surface (Belayneh et al 2009;Le Garzic et al 2011).…”

Shape Effect of Fractures on Intensity and Density of Discreet Fracture Networks

“…Randomized modeling of the discrete fracture network stem from percolation studies the application domain of which was extended in rock engineering by some researchers, including Long et al [3], Beacher [4], Andersson et al [5], and Dershowitz and Einstein [6]. The discrete fracture network models have also been developed and employed by Robinson [7], Dershowitz [8], Long [9], Rouleau [10], Long and Witherspoon [11], Long and Billaux [12], Schwartz et al [13], Schwartz and Smith [14], Long et al [15], Lie et al [16], Sanderson and Nixon [17], Cacas et al [18], Dreuzy et al [19], Elmo and Stead [20], Mauldon and Dershowitz [21], Wang [22], Dershowitz [23], Decker et al [24], Zhang [25], Jin et al [26], Jin et al [27], Mayer and Stead [28] Gao and Kang [29], Vallejos [30], Zhang and Zhao [31], Lie [32], Lie et al [33], Zou et al [34], Wang [35], Lie and Wang [36], Tsang et al [37], Lie et al [38], Brzovic et al [39].…”