INTEGRATING LASER SCANNING AND 3D VISUALIZATION FOR SEMI-AUTOMATED FRACTURE DETECTION AND ANALYSIS

Geoscientists and engineers frequently rely on fracture data including orientations, connectivity and spacing in fields such as geotechnical planning, mining exploration and production, petroleum exploration and production, and waste remediation. Such analyses are often performed on surface or analog exposures using time-tested methods such as scan line surveying and cell mapping. These two-dimensional methods tend to be time consuming and generally require direct access to the outcrop by the investigator, which may or may not be feasible. To address these issues, we present a new method for field acquisition and interpretation using a three-dimensional laser scanning system and advanced interpretation software. The laser scanner acquires three-dimensional point cloud data from an outcrop at a distance in excess of 50m. The data are acquired as a series of scenes that are registered within a geographic coordinate system and then merged into a single data cloud. After optimally subsampling the point cloud data, planar regions (e.g., fracture planes) are extracted using both automated and semi-automated detection algorithms. The fracture planes are divided into related groups using a cluster detection algorithm from which population statistics are also derived. In practice, the method yields clusters statistically similar to those interpreted using traditional field methods. More importantly, the fracture data collected through laser scanning are easily extrapolated and used to produce a three-dimensional fractured volume in 3DMove. This fractured volume contains fractures statistically similar to those interpreted from laser scanning and provides important insight into the orientations, spacing and connectivity of fractures. This technique can be readily applied in a wide range of studies including resource exploration, well planning, , drilling and blasting planning, and rock face reinforcement.