HVPS-DFN-DL: Intelligent capture and characterization of geological fracture outcrops based on a hybrid vision-photogrammetric system and discrete fracture network

Abstract

The main objective of this article is to provide a framework for intelligent capture-acquisition analysis of geometric information from geological outcrops. By combining deep learning methods with photogrammetric data from unmanned aerial vehicles (UAVs), FPV drones, and terrestrial cameras acquired by a hybrid vision-photogrammetric system (HVPS), intelligent fracture detection and geometric information segmentation of multiscale field geological outcrops were achieved. The extraction results were subsequently used to generate a three-dimensional discrete fracture network (DFN) of real rock masses for studying the influence of the spatial connectivity of discontinuity structural planes on the mechanical and hydrodynamic characteristics of rock masses. By testing data collected in situ from a variety of field rock masses in several regions of China, this framework was shown to be a very efficient method for geostatistical work, exhibiting very low measurement errors. Furthermore, this framework is extremely safe for geologists and applicable to a wide range of site geological environments. It is also suitable for field geological surveys, geometry acquisition of outcropping lithologies, obtaining tunnel face and surrounding fissure statistics, and geological stability assessment of unstable rock masses. This framework can also provide a method for unmanned topographic-geological exploration. Furthermore, the fracture network realism and the data acquisition efficiency have been greatly improved, and the difficulty of developing field measurements and validating the DFN model has been overcome.