A new tool to automatise the characterisation of fracture networks from 3D point cloud data

Fracture networks linked to the brittle deformation of rocks are often hosts of fluid flows or geomechanical discontinuities which are important to model for rock mass stability analyses, reservoir assessment or storage capacity evaluation. However, the fractures can be mapped only poorly by subsurface geophysics or borehole imagery. 1D scan lines or 2D maps on outcrops analogues are thus one of the current methods used for the assessemet of the networks, but are very limited regarding the 3D distribution of the fractures. This paper shows the first tests and results for a new automated workflow of fracture properties characterization, using high resolution LiDAR and Photogrammetry data on outcrops. From the obtained 3d point clouds on different objects such as quarries, cliffs or road sides, we reconstruct the outcrop surface in a 3D meshed surface with the help of a Simple and Scalable Surface Reconstruction (SSSR). The fracture planes are automatically detected based on a region-growing segmentation approach based on the spatial distribution of the points. It allows to quickly and effectively extract fracture properties such as orientation, geometry and position in the 3d space, with a possibility for direct 3D density computation without using proxies from 1D or 2D data. This paper present the workflow methodology and the tests on 5 basement rock outcrops of different outcropping quality. We also compare the method with a manual fracture picking tool and a classical 1D scan line methodology on the field. It is a first step to get more precise, automatic, easier and faster modelling workflows on fractured rocks from outcrop analogues data.