Abstract
Abstract. The 100 000 m2 wave-cut pavement in the Bristol Channel near Lilstock, UK, is a world-class outcrop, perfectly exposing a very large fracture network in several thin limestone layers. We present an analysis based on manual interpretation of fracture generations in selected domains and compare it with automated fracture tracing. Our dataset of high-resolution aerial photographs of the complete outcrop was acquired by an unmanned aerial vehicle, using a survey altitude optimized to resolve all fractures. We map fractures and identify fracture generations based on abutting and overprinting criteria, and we present the fracture networks of five selected representative domains. Each domain is also mapped automatically using ridge detection based on the complex shearlet transform method. The automatic fracture detection technique provides results close to the manually traced fracture networks in shorter time but with a bias towards closely spaced Y over X nodes. The assignment of fractures into generations cannot yet be done automatically, because the fracture traces extracted by the automatic method are segmented at the nodes, unlike the manual interpretation in which fractures are traced as a path from fracture tip to fracture tip and consist of several connected segments. This segmentation makes an interpretation of relative age impossible, because the identification of correct abutting relationships requires the investigation of the complete fracture trace by following a clearly defined set of rules. Generations 1 and 2 are long fractures that traverse all domains. Generation 3 is only present in the southwestern domains. Generation 4 follows an ENE–WSW striking trend, is suborthogonal to generations 1 and 2, and abuts on them and generation 3, if present. Generations 5 is the youngest fracture set with a range of orientations, creating polygonal patterns by abutting at all other fracture generations. Our mapping results show that the northeastern domains only contain four fracture generations; thus, the five generations of the outcrop identified in the southwestern domains are either not all present in each of the five domains or vary locally in their geometry, preventing the interpreter from linking the fractures to their respective generation over several spatially separate mapping domains. Fracture intensities differ between domains where the lowest is in the NE with 7.3 m−1 and the highest is in the SW with 10 m−1, coinciding with different fracture orientations and distributions of abutting relationships. Each domain has slightly different fracture network characteristics, and greater connectivity occurs where the development of later shorter fractures is not affected by the stress shadowing of pre-existing longer fractures.
Highlights
Recent technological advances allow us to collect large amounts of remote-sensing and outcrop data, e.g., using lidar, unmanned aerial vehicles (UAVs), and structure from motion (SfM) tools (Bemis et al, 2014; Bisdom et al, 2017; Hansman and Ring, 2019; Müller et al, 2017; Niethammer et al, 2012; Vasuki et al, 2014; Weismüller et al, 2019; Westoby et al, 2012)
We present our interpretation of several fracture generations in the selected areas to review the evolution of the fracture network in time steps, using topological branch and node analysis (e.g., Dimmen et al, 2017; Morley and Nixon, 2016; Nyberg et al, 2018; Procter and Sanderson, 2018; Sanderson and Nixon, 2015)
Since we focus on the 2D geometry of the fracture networks, we were able to omit the placement of ground control points to increase the georeferencing accuracy
Summary
Recent technological advances allow us to collect large amounts of remote-sensing and outcrop data, e.g., using lidar, unmanned aerial vehicles (UAVs), and structure from motion (SfM) tools (Bemis et al, 2014; Bisdom et al, 2017; Hansman and Ring, 2019; Müller et al, 2017; Niethammer et al, 2012; Vasuki et al, 2014; Weismüller et al, 2019; Westoby et al, 2012). Automated tools can aid interpretation but do not yet match the quality and reliability of manual interpretations and must be used with care (Duelis Viana et al, 2016; Vasuki et al, 2014)
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