Development and Testing of Octree-Based Intra-Voxel Statistical Inference to Enable Real-Time Geotechnical Monitoring of Large-Scale Underground Spaces with Mobile Laser Scanning Data

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-04, Vol.15 (7), p.1764
• Main Authors: Fahle, Lukas, Petruska, Andrew J., Walton, Gabriel, Brune, Jurgen F., Holley, Elizabeth A.
• Format: Article
• Language: English
• Subjects: Accuracy; Automation; Change detection; Computer applications; Convergence; Datasets; Deformation; Deformation analysis; Field tests; geotechnical monitoring; Laser applications; Lasers; Metadata; Methods; Microprocessors; Mineral industry; Mines; Mining; Mining industry; Missing data; mobile laser scanning; Monitoring; octree data structures; Octrees; Parameter sensitivity; Real time; real-time computation; Remote sensing; Rockfall; Scale models; Scanning; Sensors; Statistical inference; Statistics; Underground caverns; Underground mines
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15071764

Convergence and rockmass failure are significant hazards to personnel and physical assets in underground tunnels, caverns, and mines. Mobile Laser Scanning Systems (MLS) can deliver large volumes of point cloud data at a high frequency and on a large scale. However, current change detection approaches do not deliver sufficient sensitivity and precision for real-time performance on large-scale datasets. We present a novel, octree-based computational framework for intra-voxel statistical inference change detection and deformation analysis. Our approach exploits high-density MLS data to test for statistical significance for appearing objects caused by rockfall and for low-magnitude deformations, such as convergence. In field tests, our method detects rock falls with side lengths as small as 0.03 m and convergence as low as 0.01 m, or 0.5% wall-to-wall strain. When compared against a state-of-the-art multi-scale model-to-model cloud comparison (M3C2)-based method, ours is less sensitive to noisy data and parameter selection while also requiring fewer parameters. Most notably, our method is the only one tested that can perform real-time change detection on large-scale datasets on a single processor thread. Our method achieves a computational improvement of 50 times over single-threaded M3C2 while maintaining a performance scalability that is four times greater with dataset size. Our framework shows significant potential to enable accurate real-time geotechnical monitoring of large-scale underground spaces.