3. Methodology
Both the geometrical characteristics of rock mass structures and rock
mass strength could be controlled by a fault within a certain area
(Osmundsen et al., 2009). The results of geometrical characteristics of
rock mass structures and rock mass strength within the same fault zone
should be consistent approximately if the approaches are used suitably.
Hence, we firstly explored the spatial variation in the geometrical
characteristics of the rock mass structures. Rock mass structures at the
slope scale were identified and measured using a UAV at five selected
sites at varied distances from the YLTP Fault core (Fig. 1), with the
consideration that exhumation doesnβt influence fracture measurements at
the surface (Savage & Brodsky,
2011). The selection of the sites
was based on the outcrop rock mass conditions and the rock mass
structures present. The horizontal distances of the five sites from the
YLZP Fault core are 0.5 km, 3.0 km, 3.4 km, 8.5 km and 13.5 km (Fig. 1).
To get precise geometrical data of rock mass structures, we set at least
six ground control points (GCP) at each site when flying UAV. The UAV
used in our study is Phantom 4 RTK that provides real-time,
centimeter-level positioning data for improved absolute accuracy on
image metadata (https://www.dji.com/ca/ phantom-4-rtk). To satisfy the
requirement of data resolution, we ensured lateral overlap ratio of
aerial photography by UAV more than 65% and heading overlap ratio more
than 75%. We sub-sampled point clouds to a minimum point spacing of 0.1
m by Agisoft Photoscan (AgiSoft LLC, 2010).
At each site, the same window (100 β
Ή 100 β
Ή 100 m) was selected for
measuring the dip/dip direction and spacing of all visible rock mass
joints structures by PhotoScan, Coltop (Jaboyedoff et al.,2007) (Figs.
4a and b) and ESRI ArcMap 10 software. We generated the stereographic
projections by inputting the data into Rocscience DIPS 7.0 software. We
selected different appropriate viewpoints in point cloud model of
PhotoScan to generate orthographic projection images according to the
occurrence of each joint, and then the image data with scale were
imported into ArcMap. By ArcMap, we vectorized each joint and measured
discontinuity spacing in detail. The joint size measured is based on the
quantity of data obtained by UAV, with a minimum joint spacing of 0.3m
(Fig. 4b).
Fracture density is an important parameter in quantifying the
geometrical character of the rock mass (Faulkner et al., 2010). To
estimate fracture density, we used three-dimensional geomechanical data
to provide a joint volume count (Jv), which we then took as a measure of
block size and of the total number of joints encountered in a cubic
meter of the fractured rock mass (Palmstrom, 2005). After measuring the
spacing of the joints, we calculated mean value of each group of joints.
Using the mean spacing values of the joint sets, we calculated Jv as
follows (Palmstrom, 2005):
Jv=\(\frac{1}{S1}+\frac{1}{S2}+\frac{1}{S3}+\ldots+\frac{1}{\text{Sn}}\)(1)
where ππ is the mean joint spacing for each joint set,
for π = 1, 2, . . ., π.
To verify the results of joint spacing and fracture density Jv at the
five sites (1-5), we independently measured fallen block sizes using the
UAV and Photoscan imagery (Fig. 2).