Discrete fracture networks (DFNs) model interconnected fractures in a solid medium, which can be used for simulating flow through it. When only partial fracture lineament data are available from the earth’s surface, geostatistical clues can inform the process of creating an extended surface DFN model. Fracture properties such as orientation, intensity (size and frequency), and hierarchy can be attributed to dominant identifiable fracture sets and represented as probability distribution functions (PDFs) and simple rules. These geostatistical rules, and the dataset lineaments can be used to condition an efficient modelling process for creating extended surface DFN models. This study outlines such a procedure and applies it to a lineament dataset sourced from aerial photography to create larger, more detailed stochastic fracture models which are conditioned by the dataset and covering an area nine times the size of the original. These models are evaluated on how well they preserve the geostatistical characteristics derived from the source data.

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