Large-scale natural fracture network patterns: Insights from automated mapping in the Lilstock (Bristol Channel) limestone outcrops. (September 2021)

• Record Type: Journal Article
• Authors: Prabhakaran, Rahul; Urai, J.L.; Bertotti, G.; Weismüller, C.; Smeulders, D.M.J.
• Is Part Of: Journal of structural geology. Volume 150(2021)
• Publication Date: 2021-09
• Subjects: Fractured pavements -- Natural fracture networks -- Carbonates -- Spatial graphs -- Graph theory -- Discrete fracture networks; Geology, Structural -- Periodicals; Géomorphologie structurale -- Périodiques; Geology, Structural; Periodicals; 551.805
• DOI: 10.1016/j.jsg.2021.104405
• Languages: English
• ISSNs: 0191-8141

Abstract

Abstract: The Lilstock outcrop in the southern Bristol Channel provides exceptional exposures of several limestone beds displaying stratabound fracture networks, providing the opportunity to create a very large, complete, and ground-truthed fracture model. Here we present the result of automated fracture extraction of high-resolution photogrammetric images (0.9 cm/pixel) of the full outcrop, obtained using an unmanned aerial vehicle, to obtain a spatially extensive, full-resolution map of the complete fracture network with nearly 350, 000 ground-truthed fractures. We developed graph-based functions to resolve some common issues that arise in automatic fracture tracing such as incomplete traces, incorrect topology, artificial fragmentation, and linking of fracture segments to generate geologically significant trace interpretations. The fracture networks corresponding to different regions within the outcrop are compared using several network metrics and the results indicate both inter- and intra-network (layer to layer) structural variabilities. The dataset is a valuable benchmark in the study of large-scale natural fracture networks and its extension to stochastic network generation in geomodelling. The dataset also highlights the intrinsic spatial variation in natural fracture networks that can occur even in weakly-deformed rocks over relatively short length scales of tens of metres. Highlights: A large-scale fracture network dataset from Lilstock pavements, Bristol Channel, UK. Automatic trace interpretation from UAV imagery using the complex shearlet transform. Graph theory used to represent interpreted fracture networks as spatial graphs. Graph-based functions developed to convert traces to geologically-relevant fractures. A benchmark dataset covering over 17, 000 sq.m with 350, 000 interpreted fractures.