부산대학교 도서관

This study introduces an enhanced octree-based framework for integrated geotechnical analysis, combining geological, microseismic, and Mobile Laser Scanning (MLS)-based change detection data. Our approach leverages efficient statistical inference-based change detection techniques and additional octree data structures, enabling voxel-level data integration and association, semantic clustering, and comprehensive geotechnical analysis. We detail the implementation of our method, demonstrating capabilities such as integrating geological fault data, seismic energy exposure modeling, and combination of Random Sample Consensus (RANSAC) classification with Density-Based Spatial Clustering of Applications with Noise (DBSCAN). We conduct a suite of statistical analyses to investigate multi-dimensional trends and correlations among MLS-measured changes, distances to geological faults, and seismic energy exposure. We found that binary change classification and change magnitude negatively correlate with three investigated fault-distance metrics. Multivariant analysis reveals that increased seismic energy exposure and decreased fault distance positively correlates with MLS-based change classification and magnitudes. Our findings imply that proximity to geological faults and seismic energy exposure can be statistically linked to increases in geotechnically relevant deformations. This study is the first to demonstrate that octree-based MLS data processing can reveal multi-dimensional trends in underground mine datasets, thereby enhancing understanding of complex geomechanical behaviors crucial for mining productivity and safety.
Highlights: Introduced octree-based framework for integrated geotechnical analysis.Combined geological, microseismic, and MLS data at voxel level.Utilized statistical inference for change detection in framework.Found correlations between MLS changes, fault proximity, and seismic energy.