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The understanding of fractures in hard rock is important for topics such as geomechanics, rock mechanics and groundwater flow and solute transport. One key aspect is the roughness of the fracture, often described as the joint roughness coefficient, JRC. JRC is often subjectively interpreted by one geologist comparing a fracture trace with different type traces. It has been shown that several geologists are needed to get reliable interpretations of JRC. There are numerous attempts in the literature to develop objective methods to estimate JRC from digital traces. Some methods are not applicable to fractures, which give arbitrary results while other methods are sensitive to the resolution of the digitalisation and hence need a new relationship for each resolution. Another way of describing the roughness is by the two parameters fractal dimension and magnitude distribution of the asperities. These parameters can be objectively inferred using algorithms and act as input for a model to estimate JRC. Using several evaluation methods, the uncertainty can be decreased and, hence, more robust results achieved. A multilinear model is developed, JRC = − 4.3 + 54.6σδh(1mm) + 4.3H, that estimates JRC, of the classic ten type curves by Barton and Choubey, with standard deviation ± 1 unit. Despite the simplicity of the model it explains 96.5% of the variance in JRC. The developed model is benchmarked against an ensemble of geologists, using nine synthetic fracture traces. The median difference of JRC is 0.2 units and the model shows 40% smaller spread compared to the geologists.

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Many engineering applications in fractured crystalline rocks use measured orientations of structures such as rock contact and fractures, and lineated objects such as foliation and rock stress, mapped in boreholes as their foundation. Despite that these measurements are afflicted with uncertainties, very few attempts to quantify their magnitudes and effects on the inferred orientations have been reported. Only relying on the specification of tool imprecision may considerably underestimate the actual uncertainty space. The present work identifies nine sources of uncertainties, develops inference models of their magnitudes, and points out possible implications for the inference on orientation models and thereby effects on downstream models. The uncertainty analysis in this work builds on a unique data set from site investigations, performed by the Swedish Nuclear Fuel and Waste Management Co. (SKB). During these investigations, more than 70 boreholes with a maximum depth of 1 km were drilled in crystalline rock with a cumulative length of more than 34 km including almost 200,000 single fracture intercepts. The work presented, hence, relies on orientation of fractures. However, the techniques to infer the magnitude of orientation uncertainty may be applied to all types of structures and lineated objects in boreholes. The uncertainties are not solely detrimental, but can be valuable, provided that the reason for their presence is properly understood and the magnitudes correctly inferred. The main findings of this work are as follows: (1) knowledge of the orientation uncertainty is crucial in order to be able to infer correct orientation model and parameters coupled to the fracture sets; (2) it is important to perform multiple measurements to be able to infer the actual uncertainty instead of relying on the theoretical uncertainty provided by the manufacturers; (3) it is important to use the most appropriate tool for the prevailing circumstances; and (4) the single most important parameter to decrease the uncertainty space is to avoid drilling steeper than about -80°.

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