In this study, fracture systems developed within faulted, high-porosity sandstones in the decommissioned mines of Alderley Edge, Cheshire, UK are characterized using lidar (Light Detection And Ranging)-based analysis. The geometry of the mine workings prove to be conducive to the extraction of fracture attributes, whilst providing a degree of exposure of a notable Triassicaged reservoir outcrop analogue (Helsby Sandstone Formation) not afforded at the surface.To test the fidelity of the approach, fracture statistics generated from lidar-derived digital outcrop models are compared to an equivalent dataset collected using conventional manual surveys, with digital outcrop and manually acquired fracture attributes used to populate discrete fracture network models. These are upscaled to provide equivalent porous medium properties, enabling the impact of uncertainties introduced into fracture modelling workflows by lidarbased techniques to be assessed.Whilst broadly comparable to fracture attributes obtained using manual surveys, the systematic underrepresentation of fracture properties is observed within lidar-derived dataset, resulting in the underestimation of fracture network flow capacity. The study results suggest that, whilst enhancing data acquisition rates and coverage of exposure surfaces, the use of digital discontinuity analysis may introduce additional biases into fracture datasets, increasing the level of uncertainty within resultant modelled networks.

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The routine application of digital survey technologies such as terrestrial lidar and photogrammetry to the characterization of fault and fractures in outcrop over the past decade has resulted in major advances in terms of the efficiency of discontinuity data acquisition. However, the reliance upon meshand point-cloud-based analysis approaches means that data sets obtained from these sources commonly offer heavily abstracted views of the measured fracture network due to the limited resolution of the input model. Here, we pre sent an alternative approach that combines conventional two-dimensional (2D) image analysis with ray-tracing techniques to extract three-dimensional (3D) fracture trace maps from photogrammetrically calibrated image sequences. These 3D trace objects may be interrogated to obtain fracture network properties (trace length, intensity, and connectivity), with probabilistic methods used to estimate fracture orientation for high collinearity traces.Our approach possesses a number of advantages over existing digital surface reconstruction-based methods, with the use of a 2D pixel-based approach allowing established image-processing routines (e.g., edge detection/ connected components analysis) to be applied to the characterization of fracture and fault properties. Moreover, the innately high resolution of the input images results in practically lossless 3D fracture trace representation, limiting truncation effects. As a result, the method is capable of resolving local variability in higher-order fracture properties such as fracture intensity, which are difficult to derive using existing approaches. We demonstrate the approach on pervasively faulted Permian age exposures of the Vale of Eden Basin, UK.