Automatic extraction of geological discontinuities of a tunnel surface by integrating multiple features

In water conservancy, transportation, and mining projects, the timely acquisition of geological structural information from tunnels is critical in the analysis of engineering geological problems during the investigation and construction stages. The acquisition of comprehensive and accurate geological information from a tunnel surface remains challenging. This study provides an automatic extraction method for geological discontinuities on a tunnel surface by integrating 2D textural semantic features and 3D geological semantic features. A dense point cloud is generated using multiline parallel sequence images, after which the 3D geological semantic features, including the local geological attitude, are calculated. Through a virtual projection from 3D to 2D, the red, green, and blue (RGB) images and geological semantic images based on views of the interior umbrella arch and the sidewalls of the tunnel surface are obtained. The feature mapping between the 2D textural semantic features and the 3D geological semantic features is determined accordingly. The virtual RGB images and geological semantic images serve as dual inputs for ensemble learning for pixel block segmentation, and the output is a similarity probability tensor that describes the probability that each pixel will belong to its surrounding pixel blocks. The pixel blocks are clustered on the basis of pole and contour plots of their geological attitudes to extract geological discontinuities. Experiments were conducted to confirm and evaluate the feasibility and veracity of the proposed method. The developed method automatically extracts geological discontinuities of a tunnel surface and extends the scope of surveying and mapping through geological remote sensing.