# Semiautomated Statistical Discontinuity Analyses from Scanline Data of Fractured Rock Masses
## 1 Introduction  
Quantitative discontinuity analysis of fractured rock masses (e.g., orientation, spacing, frequency, size and trace length, and surface conditions) is commonly needed for a wide variety of applications, comprising (i) rock mass characterization and classification for tunneling, mining, and slope design (e.g., [1, 2, 3]), (ii) discrete fracture network characterization and modeling for fluid flow in fractured rocks and geothermal energy extraction (e.g., [4, 5, 6, 7]), and (iii) natural hazard assessment and slope stability (e.g., rockfall and rock avalanches, [8, 9]). One parameter highly relevant for the fields mentioned above is in situ block size distribution (IBSD), which is also determined from discontinuity analysis methods [10]. Geometrical discontinuity data for rock mass characterization can be obtained by several methods, comprising scanline and window sampling [11, 12, 13, 14, 15, 16], borehole imaging based on optical and acoustic televiewer methods [17, 18], and logging of oriented borehole cores [19]. In the last few decades, remote surveying techniques that can acquire 3D digital data of a rock face with high resolution, in particular digital photogrammetry, light detection and ranging (LiDAR) and unmanned aerial vehicle (UAV), have become state of the art and are widely applied [20]. All of these methods have advantages and limitations that should be evaluated before application. Traditional field measurements based on scanline sampling require safe access to the rock face and rockfall-safe mapping conditions. In addition, the area of mapping is limited by the accessibility of the geologist, and by its nature the method is time-consuming. Advantageous is the close visual inspection of the rock face, which makes it possible to recognize (i) barely visible traces of discontinuities, (ii) discontinuity infillings or coatings, (iii) strength-relevant very thin intercalations of (e.g., clay) layers,and (iv) the roughness and curvature. Moreover, the origin of a discontinuity, e.g., bedding plane, extensional joint with plumose structures, and fault plane with slickenside striations, can often be determined only by close visual observation. Data analyses of scanline data are well established and underpinned by statistical methods [15; 21]. Due its line-wise sampling characteristics, outcrop-based scanline data can be nicely complemented by borehole data if televiewer data or oriented borehole data are available. Given that scanline data are often subhorizontal and boreholes subvertically drilled, a good representation of the discontinuity network with minimized sampling bias can be obtained. However, the handmade and geological compass-based sampling at the rock face and subsequent data analyses are time consuming and thus considered expensive. In comparison to the costs of drilling, scanlines are quite affordable, especially if the evaluation process can be automated. Areal-based remote sensing methods provide other advantages, such as (i) in situ measurements from a safe distance, (ii) 3D discontinuity measurements of large rock faces with high accuracy and high spatial resolution, (iii) measurement of a high number of discontinuities, (iv) determination of the areal extent of large discontinuities with a better representation of the orientation, (v) little time required for on-site measurements relative to the rock face area being sampled, and (vi) fully automatic analyses with little effort of the processor. However, these methods also have disadvantages, comprising (i) small discontinuity surfaces and closed traces are often neglected, (ii) characteristic discontinuity surface features, such as coating, slickenside striation, plumose structure, or roughness cannot be identified, (iii) the type of the discontinuity cannot be determined, and (iv) difficulties in determining representative orientation of wavy or curved discontinuities.