Digital Muckpile Analysis Using Drone Photogrammetry

Digital Muckpile Analysis begins with mission planning: drone flight parameters (altitude, overlap—typically ≥80% forward and ≥60% sidelap—and lighting conditions) are optimized to ensure sufficient image resolution and geometric redundancy for robust SfM reconstruction. Captured images undergo automated feature detection, matching, and bundle adjustment to compute camera positions and dense 3D point clouds. From these, orthomosaic maps and DSMs are derived, enabling pixel-accurate measurements and volumetric analysis relative to a pre-blast terrain model (e.g., digital elevation model or bench surface). Key analytical outputs include muckpile volume (critical for load-haul scheduling), maximum throw distance (indicating energy efficiency), and spatially resolved fragmentation proxies (e.g., using texture analysis or supervised machine learning on orthomosaics to classify clump size distributions). The method supports iterative blast design improvement by correlating muckpile morphology with blast parameters (burden, spacing, charge weight, delay timing) and geotechnical conditions. Integration with mine management software (e.g., MinePlan, Deswik) allows automated reporting, historical trend analysis, and digital twin synchronization for continuous monitoring across blasting cycles.