An approach for automated lithological classification of point clouds

Abstract

Terrestrial light detection and ranging (LiDAR) data can be acquired from either static or mobile platforms. The latter presents some challenges in terms of resolution and accuracy, but the opportunity to cover a larger region and repeat surveys often prevails in practice. This paper presents a machine learning algorithm (MLA) for automated lithological classification of individual points within LiDAR point clouds based on intensity and geometry information. Two example data sets were collected by static and mobile platforms in an oil sands pit mine and the MLA was trained to distinguish sandstone and mudstone laminations. The type of approach presented here has the potential to be developed and applied for geological mapping applications such as reservoir characterization or underground excavation face mapping.